TCC Gabriel dos Santos Scatena 2023
Área de Concentração: Ciências de Dados
Orientador: Prof. Dr. Júlio Cezar Estrella
%cd '/content/drive/My Drive/Colab Notebooks/'
/content/drive/My Drive/Colab Notebooks
!jupyter nbconvert --to html -theme classic TCC_MBA_GabrielScatena.ipynb
[NbConvertApp] Converting notebook TCC_MBA_GabrielScatena.ipynb to html /usr/local/lib/python3.10/dist-packages/nbconvert/filters/widgetsdatatypefilter.py:71: UserWarning: Your element with mimetype(s) dict_keys(['application/vnd.plotly.v1+json']) is not able to be represented. warn( [NbConvertApp] Writing 30812347 bytes to TCC_MBA_GabrielScatena.html
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
Criar um modelo de aprendizado de maquina para risco de crédito
SCATENA, GABRIEL DOS SANTOS. Democratizing credit granting based on Open Finance data. 2023. Monografia (MBA em Ciências de Dados) – Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos – SP, 2023.
Credit granting in Brazil presents a significant challenge, especially for self-employed workers and entrepreneurs with informal and variable incomes. Throughout history, access to credit has been historically low for this group of professionals compared to other countries. This is largely due to the fact that financial institutions traditionally use criteria such as stable income history and cash flow in credit analysis. These criteria end up excluding those with irregular or informal incomes, making it difficult for them to obtain credit for investments, business expansion, or even to cope with unforeseen financial challenges. Given this reality, it is essential to find ways to improve credit analysis and granting for these professionals in order to help them overcome financial obstacles and drive their economic development. This thesis proposes the development of a model based on Open Finance data, or Open Finance System, which can assist financial institutions in making more accurate and fair credit decisions, aiming to improve credit access and the financial situation of these individuals.
Keywords: Open Finance, Credit, Machine learning.
SCATENA, GABRIEL DOS SANTOS. Democratizando a concessão de crédito com base em dados do Open Finance. 2023. Monografia (MBA em Ciências de Dados) – Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos – SP, 2023.
A concessão de crédito no Brasil apresenta um desafio significativo, especialmente para...
Avaliação de um modelo preditivo para risco de crédito com base em dados de Open Finance.
Avaliação de vários algoritmos de aprendizado de máquina.
Comparação das métricas dos modelos obtidos com o modelo utilizado pela empresa fornecedora dos dados.
Um tutorial prático para modelagem de risco de crédito: uma Perspectiva Técnica sem Discussão de Negócios.
"Construir um modelo de aprendizado de máquina exige equilíbrio entre habilidades de negócios, científicas e técnicas... Pela minha experiência, você precisa dedicar muito tempo à parte de negócios, por exemplo, como definir um objetivo, às vezes não é fácil, como selecionar amostras para obter o melhor desempenho, isso é uma das coisas mais importantes." - Zheng
#pip install seaborn pip scikit-learn matplotlib numpy pandas plotly imbalanced-learn lightgbm xgboost catboost
# Criando um dataframe artificial para testes
import pandas as pd
import numpy as np
# Number of rows in the DataFrame
num_rows = 1900
# Number of columns in the DataFrame (excluding 'user_id' and 'label')
num_columns = 54
# Number of unique banks
num_banks = 10
nan_percentage = 0.36
zero = .95
one = .05
# Generating random data for the DataFrame
data = {
'user_id': np.arange(1, num_rows + 1),
'bank': np.random.randint(1, num_banks + 1, size=num_rows),
}
# Calculate the number of columns with specific values
num_zero_columns = int(num_columns * 0.08)
num_range_columns = int(num_columns * 0.50)
num_remaining_columns = num_columns - (num_zero_columns + num_range_columns)
# Adding the label X0000x columns with continuous data
for i in range(1, num_columns + 1):
col_name = f'X{i:04}'
if i <= num_zero_columns: # 8% columns with only 0 values
data[col_name] = 0
elif i <= num_zero_columns + num_range_columns: # First 5% columns
data[col_name] = np.random.randint(1, 1400001, size=num_rows)
else: # Remaining columns
col_values = np.random.randint(-90000, 35001, size=num_rows)
col_mean = col_values.mean()
col_diff = 1 - col_mean
# Introduce NaN values mixed among existing values
nan_indices = np.random.choice(num_rows, size=int(num_rows * nan_percentage), replace=False)
col_values = col_values.astype(float) # Convert to float
col_values[nan_indices] = np.nan
data[col_name] = col_values + col_diff
# Adding the 'label' column with values 0 or 1
data['label'] = np.random.choice([0, 1], size=num_rows, p=[zero, one])
# Creating the DataFrame
df = pd.DataFrame(data)
# Extracting 'user_id', 'bank', and 'label' columns
user_id_col = df['user_id']
bank_col = df['bank']
label_col = df['label']
# Removing 'user_id', 'bank', and 'label' columns
df = df.drop(columns=['user_id', 'bank', 'label'])
# Shuffle the remaining columns randomly
df = df.sample(frac=1, axis=1)
# Rename the shuffled columns with names X0001 to X0540
new_col_names = ['X{:04}'.format(i) for i in range(1, num_columns + 1)]
df.columns = new_col_names
# Re-insert 'user_id', 'bank', and 'label' columns at their respective positions
df.insert(0, 'user_id', user_id_col)
df.insert(1, 'bank', bank_col)
df.insert(len(df.columns), 'label', label_col)
df = df.astype(float)
# Set display options to format with no decimal places
pd.set_option('display.float_format', '{:.0f}'.format)
# Print the first few rows of the DataFrame
display(df.head())
# Print the summary statistics of the DataFrame
display(df.describe())
notebook_path = 'C:/Temp/Codes/MBA/TCC_MBA_GabrielScatena.ipynb'
# Código para medir o tempo de execução de cada célula # Code to measure the execution time of each cell
import time
def start_timer():
global start_time
start_time = time.time()
def end_timer():
end_time = time.time()
execution_time = end_time - start_time
execution_time = round(execution_time,2)
print(f"Tempo de execução: {execution_time} segundos")
# Chama start_timer() no início de cada célula
start_timer()
# Chama end_timer() no final de cada célula
end_timer()
Tempo de execução: 0.0 segundos
# Importar bibliotecas necessárias para o projeto
start_timer()
import pickle
import re
import os
import pandas as pd
import numpy as np
from numpy import nan
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from plotly.subplots import make_subplots
import plotly.express as px
import plotly.offline as pyo
import warnings
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import plotly.graph_objects as go
from sklearn.ensemble import IsolationForest
from sklearn.svm import OneClassSVM
from sklearn.impute import SimpleImputer
# Configurações de exibição. Aumentar o número máximo de linhas.
pd.set_option('display.max_rows', 600)
# Supressão de Avisos :)
warnings.filterwarnings("ignore")
# Gera o caminho para o usuário local
home_directory = os.path.expanduser('~')
home_directory += '/Klavi/Klavi Directory Cloud - Data_BRAZIL'
home_directory = home_directory.replace('\\', '/')
print(f"Home directory: {home_directory}")
# Home directory: C:\Users\GabrielScatena
# Gera o caminho para o diretório de trabalho
files_directory = 'C:/Temp/Codes/MBA'
end_timer()
Home directory: C:/Users/GabrielScatena/Klavi/Klavi Directory Cloud - Data_BRAZIL Tempo de execução: 3.65 segundos
A Klavi é uma fintech líder no setor de Open Finance no Brasil, com um lema de "revolucionar o sistema financeiro e capacitar empresas com soluções de Open Finance, possibilitando a inovação de produtos e serviços para seus clientes." Inicialmente, a Klavi forneceu um banco de dados com mais de cinquenta mil usuários registrados. Esse banco de dados foi gerado nas fases iniciais da empresa e foi derivado de dados de Open Finance, que foram preprocessados para criar as características utilizadas pela empresa. No total, existem 540 variáveis rotuladas de K00001 a K00540.
# Carrega o arquivo de dados sem rótulos
start_timer()
# Devido ao tamanho do arquivo, o mesmo foi dividido em partes para serem carregadas
file_path = rf"{home_directory}\99_Backup_Old\Temp\K360_20230404080101.csv"
chunk_size = 100000
chunks = []
for chunk in pd.read_csv(file_path, chunksize=chunk_size):
chunks.append(chunk)
base = pd.concat(chunks, ignore_index=True)
display(base.head())
display(base.tail())
# Carrega o arquivo de dados com rótulos
labels = pd.read_excel(r"C:\Temp\Codes\y_pred_proba_banc_pan_total.xlsx")
print('\nDados rotulados')
display(labels.head())
end_timer()
| l1_score | l2_score | l3_score | user_id | trace_id | query_date | bank | account | K00001 | K00002 | ... | K00531 | K00532 | K00533 | K00534 | K00535 | K00536 | K00537 | K00538 | K00539 | K00540 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 503.0 | 703.0 | 603 | W447WaIXvpw5qZB1SwSBo8G68qQHO0TApmQa | 388012889408315575-1680572652663 | 2022-10-30 00:00:00 | Caixa | 388012889408315575 | -999999 | -999999 | ... | -999999 | -999999.00 | -999999.0 | -999999.0 | -999999.00 | -999999 | -999999 | -999999 | -999999 | -999999.00 |
| 1 | 547.0 | 682.0 | 614 | W4kyVq4XuJI/oZ8x7Vb3pIxZ1T0iwgfNFzfd | 5e35ff2760cfaf39b76e9036adfed6407d3d9f126f1fb6... | 2022-12-02 00:00:00 | Santander | 5e35ff2760cfaf39b76e9036adfed6407d3d9f126f1fb6... | 18 | 18 | ... | -999999 | -999999.00 | -999999.0 | -999999.0 | -999999.00 | -999999 | -999999 | -999999 | -999999 | -999999.00 |
| 2 | 503.0 | 703.0 | 603 | Woo7WKITvp8+qJyTjX0xqOCMl2x5Ac9ugLHU | 402931997-1680572663409 | 2022-12-02 00:00:00 | MercadoPago | 402931997 | 2 | 2 | ... | -999999 | -999999.00 | -999999.0 | -999999.0 | -999999.00 | -999999 | -999999 | -999999 | -999999 | -999999.00 |
| 3 | 570.0 | 712.0 | 641 | W4oyVK8SvJ85qJ8hc8NBfY6nWrcoALZfQJTp | OF3ba40ee5145c91b167c78cb6a2f75a5a732878417369... | 2022-11-28 00:00:00 | Sicoob | OF3ba40ee5145c91b167c78cb6a2f75a5a732878417369... | 11 | 11 | ... | 3 | 2581.63 | 0.0 | 8701.8 | 10438.56 | 0 | 10 | 16 | 2 | 5219.28 |
| 4 | 571.0 | 668.0 | 619 | W4oyVK8SvJ85qJ8hc8NBfY6nWrcoALZfQJTp | 7bf543c57e1e324ab391a982233b3149b1c76e05071183... | 2022-11-28 00:00:00 | Santander | 7bf543c57e1e324ab391a982233b3149b1c76e05071183... | 11 | 11 | ... | 3 | 2581.63 | 0.0 | 8701.8 | 10438.56 | 0 | 10 | 16 | 2 | 5219.28 |
5 rows × 548 columns
| l1_score | l2_score | l3_score | user_id | trace_id | query_date | bank | account | K00001 | K00002 | ... | K00531 | K00532 | K00533 | K00534 | K00535 | K00536 | K00537 | K00538 | K00539 | K00540 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 604577 | 514.0 | 641.0 | 577 | WIgzWagQvZ0zo5nWvOhmopdtD4zFdOA+xMPb | cb16cd854c4ae1d22c1f5f9c6643ac3d33eb4394ebfa4d... | 2022-12-14 00:00:00 | Santander | cb16cd854c4ae1d22c1f5f9c6643ac3d33eb4394ebfa4d... | 12 | 5 | ... | 3 | 652.22 | 0.0 | 0.0 | 285.67 | 0 | 0 | 2 | 1 | 285.67 |
| 604578 | 525.0 | 686.0 | 605 | WIgzWagQvZ0zo5nWvOhmopdtD4zFdOA+xMPb | 068d013b117e5a139ec850341f3e4559c84f2a4af29791... | 2022-12-14 00:00:00 | Next | 068d013b117e5a139ec850341f3e4559c84f2a4af29791... | 12 | 5 | ... | 3 | 652.22 | 0.0 | 0.0 | 285.67 | 0 | 0 | 2 | 1 | 285.67 |
| 604579 | 518.0 | 576.0 | 547 | WIgzWagQvZ0zo5nWvOhmopdtD4zFdOA+xMPb | 6079a839-48c5-4d98-9a6a-9c6e34736209-3-1680620... | 2022-12-14 00:00:00 | Nubank | 6079a839-48c5-4d98-9a6a-9c6e34736209 | 12 | 5 | ... | 3 | 652.22 | 0.0 | 0.0 | 285.67 | 0 | 0 | 2 | 1 | 285.67 |
| 604580 | 499.0 | 692.0 | 595 | X4s5WaIQtpgzpJ1TOrLe9AZYVGzIOGlyPWz5 | fa915d1f82390ddb4db0da928bd5d18b3e5398a89f0b0b... | 2022-12-09 00:00:00 | Bradesco | fa915d1f82390ddb4db0da928bd5d18b3e5398a89f0b0b... | 14 | 14 | ... | -999999 | 0.00 | -999999.0 | -999999.0 | 20.00 | -999999 | -999999 | 1 | 1 | 20.00 |
| 604581 | 503.0 | 699.0 | 601 | X4s5WaIQtpgzpJ1TOrLe9AZYVGzIOGlyPWz5 | 36713d9dde89ce8e7ae48186ef03b2602512d771f01855... | 2022-12-09 00:00:00 | Bradescard | 36713d9dde89ce8e7ae48186ef03b2602512d771f01855... | 14 | 14 | ... | -999999 | 0.00 | -999999.0 | -999999.0 | 20.00 | -999999 | -999999 | 1 | 1 | 20.00 |
5 rows × 548 columns
Dados rotulados
| user_id | label | date | odds | score | |
|---|---|---|---|---|---|
| 0 | W4g5Uq0Ztpw6pZpIQS5COAxyVirSKzh5E2Z+ | 1 | 20220101 | 0.049469 | 554.468368 |
| 1 | XI84WKIYuZw6oZmG723GdSD2/BXDBbQbRHMZ | 0 | 20220101 | 0.015022 | 726.414753 |
| 2 | U4ozUKIQvZsyo5yKZkOqQr4cvXjr+zDkDg79 | 0 | 20220101 | 0.032161 | 616.588408 |
| 3 | WYM6WaoRvZ8zp5sWJ37jQiArrQ7fI6ekgvYp | 0 | 20220101 | 0.028479 | 634.130063 |
| 4 | W405Uq4XuJs+ppoTLIQzU/AMAh0Pm2CZonGM | 0 | 20220101 | 0.041598 | 579.466206 |
Tempo de execução: 25.885830402374268 segundos
# Junção dos dados
start_timer()
new_dataset = pd.merge(base, labels, on='user_id')
duplicate_count = new_dataset['user_id'].duplicated().sum()
print('The number of rows and columns is:',new_dataset.shape ,'\nThe number of duplicated values of user_id is',duplicate_count, '.')
display(new_dataset.head())
end_timer()
The number of rows and columns is: (27188, 552) The number of duplicated values of user_id is 4664 .
| l1_score | l2_score | l3_score | user_id | trace_id | query_date | bank | account | K00001 | K00002 | ... | K00535 | K00536 | K00537 | K00538 | K00539 | K00540 | label | date | odds | score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 481.0 | 613.0 | 547 | W4s4U64Tv5k6qZFvl9hopJiD/eMwRzhfzgzj | 274617943-1680572656066 | 2022-12-01 00:00:00 | MercadoPago | 274617943 | 14 | 14 | ... | 0.0 | -999999 | 0 | 0 | -999999 | 0.0 | 0 | 20220618 | 0.011838 | 760.777437 |
| 1 | 596.0 | 689.0 | 642 | X4k/UqsXuJIzp597GhwRlfGYV/Tl2LEor+yR | f6f3aa07-c979-3c4e-803e-800671bfdc9f-168057266... | 2022-12-12 00:00:00 | Iti | f6f3aa07-c979-3c4e-803e-800671bfdc9f | 19 | 19 | ... | 0.0 | 0 | 0 | 0 | -999999 | 0.0 | 0 | 20220419 | 0.020568 | 681.077800 |
| 2 | 516.0 | 717.0 | 616 | XIo4VqMZtpg6pZ8jUBLtIAT3+QxAvtJgglyi | 480800120-1680572688271 | 2022-12-03 00:00:00 | MercadoPago | 480800120 | 13 | 13 | ... | 20.0 | 0 | 0 | 1 | 1 | 20.0 | 0 | 20220328 | 0.011282 | 767.713355 |
| 3 | 598.0 | 703.0 | 650 | W4IyU64TvZkzppDR7Y0/88NNaw+ppq2KBHRe | 36859416b1a2c8641c76c31354286369ab32ddbeabfb1e... | 2022-12-03 00:00:00 | Bradesco | 36859416b1a2c8641c76c31354286369ab32ddbeabfb1e... | 21 | 21 | ... | 0.0 | 0 | 0 | 0 | -999999 | 0.0 | 0 | 20220122 | 0.077031 | 490.576321 |
| 4 | 636.0 | 703.0 | 669 | WYI8UKoXup8zoZtU7kDu0DFpIQqtDCDXw4Ob | 99c3f85f-be56-3d5b-82f0-d5e476585cd2-168057267... | 2022-11-25 00:00:00 | Itaú | 99c3f85f-be56-3d5b-82f0-d5e476585cd2 | 1 | 1 | ... | 0.0 | 0 | 0 | 0 | -999999 | 0.0 | 0 | 20220412 | 0.012872 | 748.694241 |
5 rows × 552 columns
Tempo de execução: 0.5981287956237793 segundos
# Manter somente usuarios com uma unica conta conectada para evitar ambiguidade dos rotulos
start_timer()
new_dataset_unique = new_dataset[~new_dataset['user_id'].duplicated(keep=False)]
print(' Existem',new_dataset_unique.shape[0], 'user id unicos, somente com uma conta conectada.', '\n\nContagem de valores na coluna label (rótulo)',new_dataset_unique.label.value_counts(), '\nNumero de linhas e colunas na base de dados:',new_dataset_unique.shape)
start_timer()
Existem 18983 user id unicos, somente com uma conta conectada. Contagem de valores na coluna label (rótulo) label 0 17983 1 1000 Name: count, dtype: int64 Numero de linhas e colunas na base de dados: (18983, 552)
# Salvar arquivo em formato excel antes de realizar a limpeza dos dados
new_dataset_unique.to_excel(f'{files_directory}\MBA001_rawdata.xlsx')
# Usar df. (To use df instead of new_dataset_unique, atribute it to df)
df = new_dataset_unique.copy()
# Verificar nomes das colunas (Check columns name)
print("Columns before drop:", df.columns.to_list(), '\nThe number of rows and columns was:',df.shape)
# Lista de colunas que não serão utilizadas. Drop columns that will not be used. List of columns to drop:
columns_to_drop = ['l1_score', 'l2_score', 'l3_score', 'trace_id', 'query_date', 'account', 'date', 'odds', 'score']
# Remover colunas que não serão utilizadas. Drop the columns
df.drop(columns=columns_to_drop, inplace=True)
print("\nColumns after drop:", df.columns.to_list(), '\nThe number of rows and columns is:',df.shape)
Columns before drop: ['l1_score', 'l2_score', 'l3_score', 'user_id', 'trace_id', 'query_date', 'bank', 'account', 'K00001', 'K00002', 'K00003', 'K00004', 'K00005', 'K00006', 'K00007', 'K00008', 'K00009', 'K00010', 'K00011', 'K00012', 'K00013', 'K00014', 'K00015', 'K00016', 'K00017', 'K00018', 'K00019', 'K00020', 'K00021', 'K00022', 'K00023', 'K00024', 'K00025', 'K00026', 'K00027', 'K00028', 'K00029', 'K00030', 'K00031', 'K00032', 'K00033', 'K00034', 'K00035', 'K00036', 'K00037', 'K00038', 'K00039', 'K00040', 'K00041', 'K00042', 'K00043', 'K00044', 'K00045', 'K00046', 'K00047', 'K00048', 'K00049', 'K00050', 'K00051', 'K00052', 'K00053', 'K00054', 'K00055', 'K00056', 'K00057', 'K00058', 'K00059', 'K00060', 'K00061', 'K00062', 'K00063', 'K00064', 'K00065', 'K00066', 'K00067', 'K00068', 'K00069', 'K00070', 'K00071', 'K00072', 'K00073', 'K00074', 'K00075', 'K00076', 'K00077', 'K00078', 'K00079', 'K00080', 'K00081', 'K00082', 'K00083', 'K00084', 'K00085', 'K00086', 'K00087', 'K00088', 'K00089', 'K00090', 'K00091', 'K00092', 'K00093', 'K00094', 'K00095', 'K00096', 'K00097', 'K00098', 'K00099', 'K00100', 'K00101', 'K00102', 'K00103', 'K00104', 'K00105', 'K00106', 'K00107', 'K00108', 'K00109', 'K00110', 'K00111', 'K00112', 'K00113', 'K00114', 'K00115', 'K00116', 'K00117', 'K00118', 'K00119', 'K00120', 'K00121', 'K00122', 'K00123', 'K00124', 'K00125', 'K00126', 'K00127', 'K00128', 'K00129', 'K00130', 'K00131', 'K00132', 'K00133', 'K00134', 'K00135', 'K00136', 'K00137', 'K00138', 'K00139', 'K00140', 'K00141', 'K00142', 'K00143', 'K00144', 'K00145', 'K00146', 'K00147', 'K00148', 'K00149', 'K00150', 'K00151', 'K00152', 'K00153', 'K00154', 'K00155', 'K00156', 'K00157', 'K00158', 'K00159', 'K00160', 'K00161', 'K00162', 'K00163', 'K00164', 'K00165', 'K00166', 'K00167', 'K00168', 'K00169', 'K00170', 'K00171', 'K00172', 'K00173', 'K00174', 'K00175', 'K00176', 'K00177', 'K00178', 'K00179', 'K00180', 'K00181', 'K00182', 'K00183', 'K00184', 'K00185', 'K00186', 'K00187', 'K00188', 'K00189', 'K00190', 'K00191', 'K00192', 'K00193', 'K00194', 'K00195', 'K00196', 'K00197', 'K00198', 'K00199', 'K00200', 'K00201', 'K00202', 'K00203', 'K00204', 'K00205', 'K00206', 'K00207', 'K00208', 'K00209', 'K00210', 'K00211', 'K00212', 'K00213', 'K00214', 'K00215', 'K00216', 'K00217', 'K00218', 'K00219', 'K00220', 'K00221', 'K00222', 'K00223', 'K00224', 'K00225', 'K00226', 'K00227', 'K00228', 'K00229', 'K00230', 'K00231', 'K00232', 'K00233', 'K00234', 'K00235', 'K00236', 'K00237', 'K00238', 'K00239', 'K00240', 'K00241', 'K00242', 'K00243', 'K00244', 'K00245', 'K00246', 'K00247', 'K00248', 'K00249', 'K00250', 'K00251', 'K00252', 'K00253', 'K00254', 'K00255', 'K00256', 'K00257', 'K00258', 'K00259', 'K00260', 'K00261', 'K00262', 'K00263', 'K00264', 'K00265', 'K00266', 'K00267', 'K00268', 'K00269', 'K00270', 'K00271', 'K00272', 'K00273', 'K00274', 'K00275', 'K00276', 'K00277', 'K00278', 'K00279', 'K00280', 'K00281', 'K00282', 'K00283', 'K00284', 'K00285', 'K00286', 'K00287', 'K00288', 'K00289', 'K00290', 'K00291', 'K00292', 'K00293', 'K00294', 'K00295', 'K00296', 'K00297', 'K00298', 'K00299', 'K00300', 'K00301', 'K00302', 'K00303', 'K00304', 'K00305', 'K00306', 'K00307', 'K00308', 'K00309', 'K00310', 'K00311', 'K00312', 'K00313', 'K00314', 'K00315', 'K00316', 'K00317', 'K00318', 'K00319', 'K00320', 'K00321', 'K00322', 'K00323', 'K00324', 'K00325', 'K00326', 'K00327', 'K00328', 'K00329', 'K00330', 'K00331', 'K00332', 'K00333', 'K00334', 'K00335', 'K00336', 'K00337', 'K00338', 'K00339', 'K00340', 'K00341', 'K00342', 'K00343', 'K00344', 'K00345', 'K00346', 'K00347', 'K00348', 'K00349', 'K00350', 'K00351', 'K00352', 'K00353', 'K00354', 'K00355', 'K00356', 'K00357', 'K00358', 'K00359', 'K00360', 'K00361', 'K00362', 'K00363', 'K00364', 'K00365', 'K00366', 'K00367', 'K00368', 'K00369', 'K00370', 'K00371', 'K00372', 'K00373', 'K00374', 'K00375', 'K00376', 'K00377', 'K00378', 'K00379', 'K00380', 'K00381', 'K00382', 'K00383', 'K00384', 'K00385', 'K00386', 'K00387', 'K00388', 'K00389', 'K00390', 'K00391', 'K00392', 'K00393', 'K00394', 'K00395', 'K00396', 'K00397', 'K00398', 'K00399', 'K00400', 'K00401', 'K00402', 'K00403', 'K00404', 'K00405', 'K00406', 'K00407', 'K00408', 'K00409', 'K00410', 'K00411', 'K00412', 'K00413', 'K00414', 'K00415', 'K00416', 'K00417', 'K00418', 'K00419', 'K00420', 'K00421', 'K00422', 'K00423', 'K00424', 'K00425', 'K00426', 'K00427', 'K00428', 'K00429', 'K00430', 'K00431', 'K00432', 'K00433', 'K00434', 'K00435', 'K00436', 'K00437', 'K00438', 'K00439', 'K00440', 'K00441', 'K00442', 'K00443', 'K00444', 'K00445', 'K00446', 'K00447', 'K00448', 'K00449', 'K00450', 'K00451', 'K00452', 'K00453', 'K00454', 'K00455', 'K00456', 'K00457', 'K00458', 'K00459', 'K00460', 'K00461', 'K00462', 'K00463', 'K00464', 'K00465', 'K00466', 'K00467', 'K00468', 'K00469', 'K00470', 'K00471', 'K00472', 'K00473', 'K00474', 'K00475', 'K00476', 'K00477', 'K00478', 'K00479', 'K00480', 'K00481', 'K00482', 'K00483', 'K00484', 'K00485', 'K00486', 'K00487', 'K00488', 'K00489', 'K00490', 'K00491', 'K00492', 'K00493', 'K00494', 'K00495', 'K00496', 'K00497', 'K00498', 'K00499', 'K00500', 'K00501', 'K00502', 'K00503', 'K00504', 'K00505', 'K00506', 'K00507', 'K00508', 'K00509', 'K00510', 'K00511', 'K00512', 'K00513', 'K00514', 'K00515', 'K00516', 'K00517', 'K00518', 'K00519', 'K00520', 'K00521', 'K00522', 'K00523', 'K00524', 'K00525', 'K00526', 'K00527', 'K00528', 'K00529', 'K00530', 'K00531', 'K00532', 'K00533', 'K00534', 'K00535', 'K00536', 'K00537', 'K00538', 'K00539', 'K00540', 'label', 'date', 'odds', 'score'] The number of rows and columns was: (18983, 552) Columns after drop: ['user_id', 'bank', 'K00001', 'K00002', 'K00003', 'K00004', 'K00005', 'K00006', 'K00007', 'K00008', 'K00009', 'K00010', 'K00011', 'K00012', 'K00013', 'K00014', 'K00015', 'K00016', 'K00017', 'K00018', 'K00019', 'K00020', 'K00021', 'K00022', 'K00023', 'K00024', 'K00025', 'K00026', 'K00027', 'K00028', 'K00029', 'K00030', 'K00031', 'K00032', 'K00033', 'K00034', 'K00035', 'K00036', 'K00037', 'K00038', 'K00039', 'K00040', 'K00041', 'K00042', 'K00043', 'K00044', 'K00045', 'K00046', 'K00047', 'K00048', 'K00049', 'K00050', 'K00051', 'K00052', 'K00053', 'K00054', 'K00055', 'K00056', 'K00057', 'K00058', 'K00059', 'K00060', 'K00061', 'K00062', 'K00063', 'K00064', 'K00065', 'K00066', 'K00067', 'K00068', 'K00069', 'K00070', 'K00071', 'K00072', 'K00073', 'K00074', 'K00075', 'K00076', 'K00077', 'K00078', 'K00079', 'K00080', 'K00081', 'K00082', 'K00083', 'K00084', 'K00085', 'K00086', 'K00087', 'K00088', 'K00089', 'K00090', 'K00091', 'K00092', 'K00093', 'K00094', 'K00095', 'K00096', 'K00097', 'K00098', 'K00099', 'K00100', 'K00101', 'K00102', 'K00103', 'K00104', 'K00105', 'K00106', 'K00107', 'K00108', 'K00109', 'K00110', 'K00111', 'K00112', 'K00113', 'K00114', 'K00115', 'K00116', 'K00117', 'K00118', 'K00119', 'K00120', 'K00121', 'K00122', 'K00123', 'K00124', 'K00125', 'K00126', 'K00127', 'K00128', 'K00129', 'K00130', 'K00131', 'K00132', 'K00133', 'K00134', 'K00135', 'K00136', 'K00137', 'K00138', 'K00139', 'K00140', 'K00141', 'K00142', 'K00143', 'K00144', 'K00145', 'K00146', 'K00147', 'K00148', 'K00149', 'K00150', 'K00151', 'K00152', 'K00153', 'K00154', 'K00155', 'K00156', 'K00157', 'K00158', 'K00159', 'K00160', 'K00161', 'K00162', 'K00163', 'K00164', 'K00165', 'K00166', 'K00167', 'K00168', 'K00169', 'K00170', 'K00171', 'K00172', 'K00173', 'K00174', 'K00175', 'K00176', 'K00177', 'K00178', 'K00179', 'K00180', 'K00181', 'K00182', 'K00183', 'K00184', 'K00185', 'K00186', 'K00187', 'K00188', 'K00189', 'K00190', 'K00191', 'K00192', 'K00193', 'K00194', 'K00195', 'K00196', 'K00197', 'K00198', 'K00199', 'K00200', 'K00201', 'K00202', 'K00203', 'K00204', 'K00205', 'K00206', 'K00207', 'K00208', 'K00209', 'K00210', 'K00211', 'K00212', 'K00213', 'K00214', 'K00215', 'K00216', 'K00217', 'K00218', 'K00219', 'K00220', 'K00221', 'K00222', 'K00223', 'K00224', 'K00225', 'K00226', 'K00227', 'K00228', 'K00229', 'K00230', 'K00231', 'K00232', 'K00233', 'K00234', 'K00235', 'K00236', 'K00237', 'K00238', 'K00239', 'K00240', 'K00241', 'K00242', 'K00243', 'K00244', 'K00245', 'K00246', 'K00247', 'K00248', 'K00249', 'K00250', 'K00251', 'K00252', 'K00253', 'K00254', 'K00255', 'K00256', 'K00257', 'K00258', 'K00259', 'K00260', 'K00261', 'K00262', 'K00263', 'K00264', 'K00265', 'K00266', 'K00267', 'K00268', 'K00269', 'K00270', 'K00271', 'K00272', 'K00273', 'K00274', 'K00275', 'K00276', 'K00277', 'K00278', 'K00279', 'K00280', 'K00281', 'K00282', 'K00283', 'K00284', 'K00285', 'K00286', 'K00287', 'K00288', 'K00289', 'K00290', 'K00291', 'K00292', 'K00293', 'K00294', 'K00295', 'K00296', 'K00297', 'K00298', 'K00299', 'K00300', 'K00301', 'K00302', 'K00303', 'K00304', 'K00305', 'K00306', 'K00307', 'K00308', 'K00309', 'K00310', 'K00311', 'K00312', 'K00313', 'K00314', 'K00315', 'K00316', 'K00317', 'K00318', 'K00319', 'K00320', 'K00321', 'K00322', 'K00323', 'K00324', 'K00325', 'K00326', 'K00327', 'K00328', 'K00329', 'K00330', 'K00331', 'K00332', 'K00333', 'K00334', 'K00335', 'K00336', 'K00337', 'K00338', 'K00339', 'K00340', 'K00341', 'K00342', 'K00343', 'K00344', 'K00345', 'K00346', 'K00347', 'K00348', 'K00349', 'K00350', 'K00351', 'K00352', 'K00353', 'K00354', 'K00355', 'K00356', 'K00357', 'K00358', 'K00359', 'K00360', 'K00361', 'K00362', 'K00363', 'K00364', 'K00365', 'K00366', 'K00367', 'K00368', 'K00369', 'K00370', 'K00371', 'K00372', 'K00373', 'K00374', 'K00375', 'K00376', 'K00377', 'K00378', 'K00379', 'K00380', 'K00381', 'K00382', 'K00383', 'K00384', 'K00385', 'K00386', 'K00387', 'K00388', 'K00389', 'K00390', 'K00391', 'K00392', 'K00393', 'K00394', 'K00395', 'K00396', 'K00397', 'K00398', 'K00399', 'K00400', 'K00401', 'K00402', 'K00403', 'K00404', 'K00405', 'K00406', 'K00407', 'K00408', 'K00409', 'K00410', 'K00411', 'K00412', 'K00413', 'K00414', 'K00415', 'K00416', 'K00417', 'K00418', 'K00419', 'K00420', 'K00421', 'K00422', 'K00423', 'K00424', 'K00425', 'K00426', 'K00427', 'K00428', 'K00429', 'K00430', 'K00431', 'K00432', 'K00433', 'K00434', 'K00435', 'K00436', 'K00437', 'K00438', 'K00439', 'K00440', 'K00441', 'K00442', 'K00443', 'K00444', 'K00445', 'K00446', 'K00447', 'K00448', 'K00449', 'K00450', 'K00451', 'K00452', 'K00453', 'K00454', 'K00455', 'K00456', 'K00457', 'K00458', 'K00459', 'K00460', 'K00461', 'K00462', 'K00463', 'K00464', 'K00465', 'K00466', 'K00467', 'K00468', 'K00469', 'K00470', 'K00471', 'K00472', 'K00473', 'K00474', 'K00475', 'K00476', 'K00477', 'K00478', 'K00479', 'K00480', 'K00481', 'K00482', 'K00483', 'K00484', 'K00485', 'K00486', 'K00487', 'K00488', 'K00489', 'K00490', 'K00491', 'K00492', 'K00493', 'K00494', 'K00495', 'K00496', 'K00497', 'K00498', 'K00499', 'K00500', 'K00501', 'K00502', 'K00503', 'K00504', 'K00505', 'K00506', 'K00507', 'K00508', 'K00509', 'K00510', 'K00511', 'K00512', 'K00513', 'K00514', 'K00515', 'K00516', 'K00517', 'K00518', 'K00519', 'K00520', 'K00521', 'K00522', 'K00523', 'K00524', 'K00525', 'K00526', 'K00527', 'K00528', 'K00529', 'K00530', 'K00531', 'K00532', 'K00533', 'K00534', 'K00535', 'K00536', 'K00537', 'K00538', 'K00539', 'K00540', 'label'] The number of rows and columns is: (18983, 543)
# Verificar 5 linhas do df (Check df)
display(df.head())
# Valores faltantes estão identificados, porem não estão uniforme. Lots of -999999 values, it means 'missing values'. Replace it to nan (not a number)
df.replace([-999999, -999999.0, -999999.00], np.nan, inplace=True)
# df after replace -999999 by nan
display('df após trocar -999999 por NaN:', df.head())
| user_id | bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00008 | ... | K00532 | K00533 | K00534 | K00535 | K00536 | K00537 | K00538 | K00539 | K00540 | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | W4s4U64Tv5k6qZFvl9hopJiD/eMwRzhfzgzj | MercadoPago | 14 | 14 | 14.0 | 14.0 | 0.0 | 0.0 | -999999 | -999999 | ... | 3000.00 | -999999.0 | 0.0 | 0.0 | -999999 | 0 | 0 | -999999 | 0.0 | 0 |
| 1 | X4k/UqsXuJIzp597GhwRlfGYV/Tl2LEor+yR | Iti | 19 | 19 | 19.0 | 19.0 | 0.0 | 0.0 | 41 | 41 | ... | 2964.20 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | -999999 | 0.0 | 0 |
| 2 | XIo4VqMZtpg6pZ8jUBLtIAT3+QxAvtJgglyi | MercadoPago | 13 | 13 | 13.0 | 13.0 | 0.0 | 0.0 | -999999 | -999999 | ... | 1280.73 | 0.0 | 0.0 | 20.0 | 0 | 0 | 1 | 1 | 20.0 | 0 |
| 3 | W4IyU64TvZkzppDR7Y0/88NNaw+ppq2KBHRe | Bradesco | 21 | 21 | 21.0 | 21.0 | 0.0 | 0.0 | 310 | 310 | ... | 0.00 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | -999999 | 0.0 | 0 |
| 4 | WYI8UKoXup8zoZtU7kDu0DFpIQqtDCDXw4Ob | Itaú | 1 | 1 | 1.0 | 1.0 | 0.0 | 0.0 | 257 | 257 | ... | 0.00 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | -999999 | 0.0 | 0 |
5 rows × 543 columns
'df após trocar -999999 por NaN:'
| user_id | bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00008 | ... | K00532 | K00533 | K00534 | K00535 | K00536 | K00537 | K00538 | K00539 | K00540 | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | W4s4U64Tv5k6qZFvl9hopJiD/eMwRzhfzgzj | MercadoPago | 14.0 | 14.0 | 14.0 | 14.0 | 0.0 | 0.0 | NaN | NaN | ... | 3000.00 | NaN | 0.0 | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | 0 |
| 1 | X4k/UqsXuJIzp597GhwRlfGYV/Tl2LEor+yR | Iti | 19.0 | 19.0 | 19.0 | 19.0 | 0.0 | 0.0 | 41.0 | 41.0 | ... | 2964.20 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 |
| 2 | XIo4VqMZtpg6pZ8jUBLtIAT3+QxAvtJgglyi | MercadoPago | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | NaN | NaN | ... | 1280.73 | 0.0 | 0.0 | 20.0 | 0.0 | 0.0 | 1.0 | 1.0 | 20.0 | 0 |
| 3 | W4IyU64TvZkzppDR7Y0/88NNaw+ppq2KBHRe | Bradesco | 21.0 | 21.0 | 21.0 | 21.0 | 0.0 | 0.0 | 310.0 | 310.0 | ... | 0.00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 |
| 4 | WYI8UKoXup8zoZtU7kDu0DFpIQqtDCDXw4Ob | Itaú | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 257.0 | 257.0 | ... | 0.00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 |
5 rows × 543 columns
# Discretização de 'user_id'. Discretize 'user_id' column and replace it with integer numbers
user_id_mapping = {user_id: i for i, user_id in enumerate(df['user_id'].unique())}
df['user_id'] = df['user_id'].map(user_id_mapping)
# Create a dictionary with user_id and its respective number
# The 'user_id' values are mapped to integer numbers
user_id_dict = {'user_id': list(user_id_mapping.keys()), 'number': list(user_id_mapping.values())}
# Save the dictionary to a text file
file_path = f'{files_directory}/user_id_mapping.txt'
with open(file_path, 'w') as file:
for key, value in user_id_dict.items():
file.write(f"{key}: {value}\n")
# Discretização de 'bank'. Discretize 'bank' column and replace it with integer numbers
bank_mapping = {bank: i for i, bank in enumerate(df['bank'].unique())}
df['bank'] = df['bank'].map(bank_mapping)
# Create a dictionary with 'bank' and its respective number
# The 'bank' values are mapped to integer numbers
bank_dict = {'bank': list(bank_mapping.keys()), 'number': list(bank_mapping.values())}
# Save the dictionary to a text file
file_path = f'{files_directory}/bank_mapping.txt'
with open(file_path, 'w') as file:
for key, value in bank_dict.items():
file.write(f"{key}: {value}\n")
# Check df
display(df.tail())
display(df.describe())
| user_id | bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00008 | ... | K00532 | K00533 | K00534 | K00535 | K00536 | K00537 | K00538 | K00539 | K00540 | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 26819 | 18978 | 2 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | 95.0 | 95.0 | ... | 0.00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 |
| 26820 | 18979 | 0 | 8.0 | 8.0 | 8.0 | 8.0 | 0.0 | 0.0 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0 |
| 26821 | 18980 | 2 | 16.0 | 16.0 | 16.0 | 16.0 | 0.0 | 0.0 | 217.0 | 217.0 | ... | 3199.50 | 20.0 | 1090.0 | 1155.0 | 1.0 | 6.0 | 10.0 | 3.0 | 385.0 | 0 |
| 26822 | 18981 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0 |
| 26823 | 18982 | 0 | 12.0 | 12.0 | 12.0 | 12.0 | 0.0 | 0.0 | NaN | NaN | ... | 3084.14 | 12.0 | 32.0 | 42.0 | 1.0 | 2.0 | 3.0 | 3.0 | 14.0 | 0 |
5 rows × 543 columns
| user_id | bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00008 | ... | K00532 | K00533 | K00534 | K00535 | K00536 | K00537 | K00538 | K00539 | K00540 | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 18983.000000 | 18983.000000 | 15525.000000 | 15525.000000 | 15525.000000 | 15525.000000 | 15525.000000 | 15525.000000 | 5989.000000 | 5989.000000 | ... | 11770.000000 | 10097.000000 | 11067.000000 | 11770.000000 | 10097.000000 | 11067.000000 | 11770.000000 | 5501.000000 | 11770.000000 | 18983.000000 |
| mean | 9491.000000 | 4.392878 | 10.269050 | 10.212947 | 10.240838 | 10.240837 | 0.027738 | 0.004546 | 124.357322 | 124.357322 | ... | 3113.269925 | 105.428755 | 318.119164 | 534.524194 | 0.740517 | 1.653926 | 2.517077 | 1.936739 | 232.172283 | 0.052679 |
| std | 5480.064416 | 4.448300 | 4.743384 | 4.774049 | 4.745396 | 4.745789 | 0.355435 | 0.058756 | 140.191103 | 140.191103 | ... | 8341.664790 | 541.256947 | 1316.867180 | 2479.869085 | 1.566985 | 3.405173 | 5.240442 | 0.855993 | 998.975890 | 0.223397 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 |
| 25% | 4745.500000 | 0.000000 | 8.000000 | 7.000000 | 8.000000 | 8.000000 | 0.000000 | 0.000000 | 37.000000 | 37.000000 | ... | 227.365000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 |
| 50% | 9491.000000 | 3.000000 | 12.000000 | 12.000000 | 12.000000 | 12.000000 | 0.000000 | 0.000000 | 100.000000 | 100.000000 | ... | 1161.955000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 |
| 75% | 14236.500000 | 6.000000 | 13.000000 | 13.000000 | 13.000000 | 13.000000 | 0.000000 | 0.000000 | 178.000000 | 178.000000 | ... | 3111.315000 | 41.880000 | 120.000000 | 202.862500 | 1.000000 | 2.000000 | 3.000000 | 3.000000 | 117.587500 | 0.000000 |
| max | 18982.000000 | 23.000000 | 24.000000 | 22.000000 | 22.000000 | 22.000000 | 9.000000 | 1.580000 | 1463.000000 | 1463.000000 | ... | 307248.870000 | 23582.060000 | 34980.590000 | 140824.220000 | 33.000000 | 70.000000 | 119.000000 | 3.000000 | 46941.410000 | 1.000000 |
8 rows × 543 columns
# Identifica valor max no df. Find the maximum number in the DataFrame, ignoring NaN ( the ideia is optimize memory usage)
max_value = df.max().max()
print("Maximum value in the DataFrame:", max_value)
# Identifica valor min no df. Find the minimum number in the DataFrame
min_value = df.min().min()
print("Minimum value in the DataFrame:", min_value)
Maximum value in the DataFrame: 1375788.93 Minimum value in the DataFrame: -367104.33
# Memória inicial. Intial memory usage: was 78.8 MB
print(df.info())
<class 'pandas.core.frame.DataFrame'> Index: 18983 entries, 0 to 26823 Columns: 543 entries, user_id to label dtypes: float64(520), int64(23) memory usage: 78.8 MB None
# Para economizar memória, alterar tipo de determinadas variaveis. To save memory use the minimal type needed
numeric_columns = df.select_dtypes(include=[ 'float64'])
# Convert the numeric columns to floatXX or intXX
df[numeric_columns.columns] = numeric_columns.astype('float32')
numeric_columns = df.select_dtypes(include=['int64'])
df[numeric_columns.columns] = numeric_columns.astype('int32')
df = df.round(2)
# Print the data types after conversion
print(df.dtypes.to_dict())
# To optimal results you may specify a type to each column
print(df.info())
# Almost half of the memory usage
{'user_id': dtype('int32'), 'bank': dtype('int32'), 'K00001': dtype('float32'), 'K00002': dtype('float32'), 'K00003': dtype('float32'), 'K00004': dtype('float32'), 'K00005': dtype('float32'), 'K00006': dtype('float32'), 'K00007': dtype('float32'), 'K00008': dtype('float32'), 'K00009': dtype('float32'), 'K00010': dtype('float32'), 'K00011': dtype('float32'), 'K00012': dtype('float32'), 'K00013': dtype('float32'), 'K00014': dtype('float32'), 'K00015': dtype('float32'), 'K00016': dtype('float32'), 'K00017': dtype('float32'), 'K00018': dtype('float32'), 'K00019': dtype('float32'), 'K00020': dtype('float32'), 'K00021': dtype('float32'), 'K00022': dtype('float32'), 'K00023': dtype('float32'), 'K00024': dtype('float32'), 'K00025': dtype('float32'), 'K00026': dtype('int32'), 'K00027': dtype('int32'), 'K00028': dtype('float32'), 'K00029': dtype('float32'), 'K00030': dtype('float32'), 'K00031': dtype('float32'), 'K00032': dtype('float32'), 'K00033': dtype('float32'), 'K00034': dtype('float32'), 'K00035': dtype('float32'), 'K00036': dtype('float32'), 'K00037': dtype('float32'), 'K00038': dtype('float32'), 'K00039': dtype('float32'), 'K00040': dtype('float32'), 'K00041': dtype('float32'), 'K00042': dtype('float32'), 'K00043': dtype('float32'), 'K00044': dtype('float32'), 'K00045': dtype('float32'), 'K00046': dtype('float32'), 'K00047': dtype('float32'), 'K00048': dtype('float32'), 'K00049': dtype('float32'), 'K00050': dtype('float32'), 'K00051': dtype('float32'), 'K00052': dtype('float32'), 'K00053': dtype('float32'), 'K00054': dtype('float32'), 'K00055': dtype('float32'), 'K00056': dtype('float32'), 'K00057': dtype('float32'), 'K00058': dtype('float32'), 'K00059': dtype('float32'), 'K00060': dtype('float32'), 'K00061': dtype('float32'), 'K00062': dtype('float32'), 'K00063': dtype('float32'), 'K00064': dtype('float32'), 'K00065': dtype('float32'), 'K00066': dtype('float32'), 'K00067': dtype('float32'), 'K00068': dtype('float32'), 'K00069': dtype('float32'), 'K00070': dtype('float32'), 'K00071': dtype('float32'), 'K00072': dtype('float32'), 'K00073': dtype('float32'), 'K00074': dtype('float32'), 'K00075': dtype('float32'), 'K00076': dtype('float32'), 'K00077': dtype('float32'), 'K00078': dtype('float32'), 'K00079': dtype('float32'), 'K00080': dtype('float32'), 'K00081': dtype('float32'), 'K00082': dtype('float32'), 'K00083': dtype('float32'), 'K00084': dtype('float32'), 'K00085': dtype('float32'), 'K00086': dtype('float32'), 'K00087': dtype('float32'), 'K00088': dtype('float32'), 'K00089': dtype('float32'), 'K00090': dtype('float32'), 'K00091': dtype('float32'), 'K00092': dtype('float32'), 'K00093': dtype('float32'), 'K00094': dtype('float32'), 'K00095': dtype('float32'), 'K00096': dtype('float32'), 'K00097': dtype('float32'), 'K00098': dtype('float32'), 'K00099': dtype('float32'), 'K00100': dtype('float32'), 'K00101': dtype('float32'), 'K00102': dtype('float32'), 'K00103': dtype('float32'), 'K00104': dtype('float32'), 'K00105': dtype('float32'), 'K00106': dtype('float32'), 'K00107': dtype('float32'), 'K00108': dtype('float32'), 'K00109': dtype('float32'), 'K00110': dtype('float32'), 'K00111': dtype('float32'), 'K00112': dtype('float32'), 'K00113': dtype('float32'), 'K00114': dtype('float32'), 'K00115': dtype('float32'), 'K00116': dtype('float32'), 'K00117': dtype('float32'), 'K00118': dtype('float32'), 'K00119': dtype('float32'), 'K00120': dtype('float32'), 'K00121': dtype('float32'), 'K00122': dtype('float32'), 'K00123': dtype('float32'), 'K00124': dtype('float32'), 'K00125': dtype('float32'), 'K00126': dtype('float32'), 'K00127': dtype('float32'), 'K00128': dtype('float32'), 'K00129': dtype('float32'), 'K00130': dtype('float32'), 'K00131': dtype('float32'), 'K00132': dtype('float32'), 'K00133': dtype('float32'), 'K00134': dtype('float32'), 'K00135': dtype('float32'), 'K00136': dtype('float32'), 'K00137': dtype('float32'), 'K00138': dtype('float32'), 'K00139': dtype('float32'), 'K00140': dtype('float32'), 'K00141': dtype('float32'), 'K00142': dtype('float32'), 'K00143': dtype('float32'), 'K00144': dtype('float32'), 'K00145': dtype('float32'), 'K00146': dtype('float32'), 'K00147': dtype('float32'), 'K00148': dtype('float32'), 'K00149': dtype('float32'), 'K00150': dtype('float32'), 'K00151': dtype('float32'), 'K00152': dtype('float32'), 'K00153': dtype('float32'), 'K00154': dtype('float32'), 'K00155': dtype('float32'), 'K00156': dtype('float32'), 'K00157': dtype('float32'), 'K00158': dtype('float32'), 'K00159': dtype('float32'), 'K00160': dtype('float32'), 'K00161': dtype('float32'), 'K00162': dtype('float32'), 'K00163': dtype('float32'), 'K00164': dtype('float32'), 'K00165': dtype('float32'), 'K00166': dtype('float32'), 'K00167': dtype('float32'), 'K00168': dtype('float32'), 'K00169': dtype('float32'), 'K00170': dtype('float32'), 'K00171': dtype('float32'), 'K00172': dtype('float32'), 'K00173': dtype('float32'), 'K00174': dtype('float32'), 'K00175': dtype('float32'), 'K00176': dtype('float32'), 'K00177': dtype('float32'), 'K00178': dtype('float32'), 'K00179': dtype('float32'), 'K00180': dtype('float32'), 'K00181': dtype('float32'), 'K00182': dtype('float32'), 'K00183': dtype('float32'), 'K00184': dtype('float32'), 'K00185': dtype('int32'), 'K00186': dtype('int32'), 'K00187': dtype('int32'), 'K00188': dtype('int32'), 'K00189': dtype('int32'), 'K00190': dtype('int32'), 'K00191': dtype('float32'), 'K00192': dtype('float32'), 'K00193': dtype('float32'), 'K00194': dtype('float32'), 'K00195': dtype('float32'), 'K00196': dtype('float32'), 'K00197': dtype('int32'), 'K00198': dtype('int32'), 'K00199': dtype('int32'), 'K00200': dtype('int32'), 'K00201': dtype('int32'), 'K00202': dtype('int32'), 'K00203': dtype('int32'), 'K00204': dtype('int32'), 'K00205': dtype('int32'), 'K00206': dtype('int32'), 'K00207': dtype('int32'), 'K00208': dtype('int32'), 'K00209': dtype('float32'), 'K00210': dtype('float32'), 'K00211': dtype('float32'), 'K00212': dtype('float32'), 'K00213': dtype('float32'), 'K00214': dtype('float32'), 'K00215': dtype('float32'), 'K00216': dtype('float32'), 'K00217': dtype('float32'), 'K00218': dtype('float32'), 'K00219': dtype('float32'), 'K00220': dtype('float32'), 'K00221': dtype('float32'), 'K00222': dtype('float32'), 'K00223': dtype('float32'), 'K00224': dtype('float32'), 'K00225': dtype('float32'), 'K00226': dtype('float32'), 'K00227': dtype('float32'), 'K00228': dtype('float32'), 'K00229': dtype('float32'), 'K00230': dtype('float32'), 'K00231': dtype('float32'), 'K00232': dtype('float32'), 'K00233': dtype('float32'), 'K00234': dtype('float32'), 'K00235': dtype('float32'), 'K00236': dtype('float32'), 'K00237': dtype('float32'), 'K00238': dtype('float32'), 'K00239': dtype('float32'), 'K00240': dtype('float32'), 'K00241': dtype('float32'), 'K00242': dtype('float32'), 'K00243': dtype('float32'), 'K00244': dtype('float32'), 'K00245': dtype('float32'), 'K00246': dtype('float32'), 'K00247': dtype('float32'), 'K00248': dtype('float32'), 'K00249': dtype('float32'), 'K00250': dtype('float32'), 'K00251': dtype('float32'), 'K00252': dtype('float32'), 'K00253': dtype('float32'), 'K00254': dtype('float32'), 'K00255': dtype('float32'), 'K00256': dtype('float32'), 'K00257': dtype('float32'), 'K00258': dtype('float32'), 'K00259': dtype('float32'), 'K00260': dtype('float32'), 'K00261': dtype('float32'), 'K00262': dtype('float32'), 'K00263': dtype('float32'), 'K00264': dtype('float32'), 'K00265': dtype('float32'), 'K00266': dtype('float32'), 'K00267': dtype('float32'), 'K00268': dtype('float32'), 'K00269': dtype('float32'), 'K00270': dtype('float32'), 'K00271': dtype('float32'), 'K00272': dtype('float32'), 'K00273': dtype('float32'), 'K00274': dtype('float32'), 'K00275': dtype('float32'), 'K00276': dtype('float32'), 'K00277': dtype('float32'), 'K00278': dtype('float32'), 'K00279': dtype('float32'), 'K00280': dtype('float32'), 'K00281': dtype('float32'), 'K00282': dtype('float32'), 'K00283': dtype('float32'), 'K00284': dtype('float32'), 'K00285': dtype('float32'), 'K00286': dtype('float32'), 'K00287': dtype('float32'), 'K00288': dtype('float32'), 'K00289': dtype('float32'), 'K00290': dtype('float32'), 'K00291': dtype('float32'), 'K00292': dtype('float32'), 'K00293': dtype('float32'), 'K00294': dtype('float32'), 'K00295': dtype('float32'), 'K00296': dtype('float32'), 'K00297': dtype('float32'), 'K00298': dtype('float32'), 'K00299': dtype('float32'), 'K00300': dtype('float32'), 'K00301': dtype('float32'), 'K00302': dtype('float32'), 'K00303': dtype('float32'), 'K00304': dtype('float32'), 'K00305': dtype('float32'), 'K00306': dtype('float32'), 'K00307': dtype('float32'), 'K00308': dtype('float32'), 'K00309': dtype('float32'), 'K00310': dtype('float32'), 'K00311': dtype('float32'), 'K00312': dtype('float32'), 'K00313': dtype('float32'), 'K00314': dtype('float32'), 'K00315': dtype('float32'), 'K00316': dtype('float32'), 'K00317': dtype('float32'), 'K00318': dtype('float32'), 'K00319': dtype('float32'), 'K00320': dtype('float32'), 'K00321': dtype('float32'), 'K00322': dtype('float32'), 'K00323': dtype('float32'), 'K00324': dtype('float32'), 'K00325': dtype('float32'), 'K00326': dtype('float32'), 'K00327': dtype('float32'), 'K00328': dtype('float32'), 'K00329': dtype('float32'), 'K00330': dtype('float32'), 'K00331': dtype('float32'), 'K00332': dtype('float32'), 'K00333': dtype('float32'), 'K00334': dtype('float32'), 'K00335': dtype('float32'), 'K00336': dtype('float32'), 'K00337': dtype('float32'), 'K00338': dtype('float32'), 'K00339': dtype('float32'), 'K00340': dtype('float32'), 'K00341': dtype('float32'), 'K00342': dtype('float32'), 'K00343': dtype('float32'), 'K00344': dtype('float32'), 'K00345': dtype('float32'), 'K00346': dtype('float32'), 'K00347': dtype('float32'), 'K00348': dtype('float32'), 'K00349': dtype('float32'), 'K00350': dtype('float32'), 'K00351': dtype('float32'), 'K00352': dtype('float32'), 'K00353': dtype('float32'), 'K00354': dtype('float32'), 'K00355': dtype('float32'), 'K00356': dtype('float32'), 'K00357': dtype('float32'), 'K00358': dtype('float32'), 'K00359': dtype('float32'), 'K00360': dtype('float32'), 'K00361': dtype('float32'), 'K00362': dtype('float32'), 'K00363': dtype('float32'), 'K00364': dtype('float32'), 'K00365': dtype('float32'), 'K00366': dtype('float32'), 'K00367': dtype('float32'), 'K00368': dtype('float32'), 'K00369': dtype('float32'), 'K00370': dtype('float32'), 'K00371': dtype('float32'), 'K00372': dtype('float32'), 'K00373': dtype('float32'), 'K00374': dtype('float32'), 'K00375': dtype('float32'), 'K00376': dtype('float32'), 'K00377': dtype('float32'), 'K00378': dtype('float32'), 'K00379': dtype('float32'), 'K00380': dtype('float32'), 'K00381': dtype('float32'), 'K00382': dtype('float32'), 'K00383': dtype('float32'), 'K00384': dtype('float32'), 'K00385': dtype('float32'), 'K00386': dtype('float32'), 'K00387': dtype('float32'), 'K00388': dtype('float32'), 'K00389': dtype('float32'), 'K00390': dtype('float32'), 'K00391': dtype('float32'), 'K00392': dtype('float32'), 'K00393': dtype('float32'), 'K00394': dtype('float32'), 'K00395': dtype('float32'), 'K00396': dtype('float32'), 'K00397': dtype('float32'), 'K00398': dtype('float32'), 'K00399': dtype('float32'), 'K00400': dtype('float32'), 'K00401': dtype('float32'), 'K00402': dtype('float32'), 'K00403': dtype('float32'), 'K00404': dtype('float32'), 'K00405': dtype('float32'), 'K00406': dtype('float32'), 'K00407': dtype('float32'), 'K00408': dtype('float32'), 'K00409': dtype('float32'), 'K00410': dtype('float32'), 'K00411': dtype('float32'), 'K00412': dtype('float32'), 'K00413': dtype('float32'), 'K00414': dtype('float32'), 'K00415': dtype('float32'), 'K00416': dtype('float32'), 'K00417': dtype('float32'), 'K00418': dtype('float32'), 'K00419': dtype('float32'), 'K00420': dtype('float32'), 'K00421': dtype('float32'), 'K00422': dtype('float32'), 'K00423': dtype('float32'), 'K00424': dtype('float32'), 'K00425': dtype('float32'), 'K00426': dtype('float32'), 'K00427': dtype('float32'), 'K00428': dtype('float32'), 'K00429': dtype('float32'), 'K00430': dtype('float32'), 'K00431': dtype('float32'), 'K00432': dtype('float32'), 'K00433': dtype('float32'), 'K00434': dtype('float32'), 'K00435': dtype('float32'), 'K00436': dtype('float32'), 'K00437': dtype('float32'), 'K00438': dtype('float32'), 'K00439': dtype('float32'), 'K00440': dtype('float32'), 'K00441': dtype('float32'), 'K00442': dtype('float32'), 'K00443': dtype('float32'), 'K00444': dtype('float32'), 'K00445': dtype('float32'), 'K00446': dtype('float32'), 'K00447': dtype('float32'), 'K00448': dtype('float32'), 'K00449': dtype('float32'), 'K00450': dtype('float32'), 'K00451': dtype('float32'), 'K00452': dtype('float32'), 'K00453': dtype('float32'), 'K00454': dtype('float32'), 'K00455': dtype('float32'), 'K00456': dtype('float32'), 'K00457': dtype('float32'), 'K00458': dtype('float32'), 'K00459': dtype('float32'), 'K00460': dtype('float32'), 'K00461': dtype('float32'), 'K00462': dtype('float32'), 'K00463': dtype('float32'), 'K00464': dtype('float32'), 'K00465': dtype('float32'), 'K00466': dtype('float32'), 'K00467': dtype('float32'), 'K00468': dtype('float32'), 'K00469': dtype('float32'), 'K00470': dtype('float32'), 'K00471': dtype('float32'), 'K00472': dtype('float32'), 'K00473': dtype('float32'), 'K00474': dtype('float32'), 'K00475': dtype('float32'), 'K00476': dtype('float32'), 'K00477': dtype('float32'), 'K00478': dtype('float32'), 'K00479': dtype('float32'), 'K00480': dtype('float32'), 'K00481': dtype('float32'), 'K00482': dtype('float32'), 'K00483': dtype('float32'), 'K00484': dtype('float32'), 'K00485': dtype('float32'), 'K00486': dtype('float32'), 'K00487': dtype('float32'), 'K00488': dtype('float32'), 'K00489': dtype('float32'), 'K00490': dtype('float32'), 'K00491': dtype('float32'), 'K00492': dtype('float32'), 'K00493': dtype('float32'), 'K00494': dtype('float32'), 'K00495': dtype('float32'), 'K00496': dtype('float32'), 'K00497': dtype('float32'), 'K00498': dtype('float32'), 'K00499': dtype('float32'), 'K00500': dtype('float32'), 'K00501': dtype('float32'), 'K00502': dtype('float32'), 'K00503': dtype('float32'), 'K00504': dtype('float32'), 'K00505': dtype('float32'), 'K00506': dtype('float32'), 'K00507': dtype('float32'), 'K00508': dtype('float32'), 'K00509': dtype('float32'), 'K00510': dtype('float32'), 'K00511': dtype('float32'), 'K00512': dtype('float32'), 'K00513': dtype('float32'), 'K00514': dtype('float32'), 'K00515': dtype('float32'), 'K00516': dtype('float32'), 'K00517': dtype('float32'), 'K00518': dtype('float32'), 'K00519': dtype('float32'), 'K00520': dtype('float32'), 'K00521': dtype('float32'), 'K00522': dtype('float32'), 'K00523': dtype('float32'), 'K00524': dtype('float32'), 'K00525': dtype('float32'), 'K00526': dtype('float32'), 'K00527': dtype('float32'), 'K00528': dtype('float32'), 'K00529': dtype('float32'), 'K00530': dtype('float32'), 'K00531': dtype('float32'), 'K00532': dtype('float32'), 'K00533': dtype('float32'), 'K00534': dtype('float32'), 'K00535': dtype('float32'), 'K00536': dtype('float32'), 'K00537': dtype('float32'), 'K00538': dtype('float32'), 'K00539': dtype('float32'), 'K00540': dtype('float32'), 'label': dtype('int32')}
<class 'pandas.core.frame.DataFrame'>
Index: 18983 entries, 0 to 26823
Columns: 543 entries, user_id to label
dtypes: float32(520), int32(23)
memory usage: 39.5 MB
None
# Salva o arquivo modificado. Save the cleaned dataframe
df.to_excel(f'{files_directory}/MBA001_cleandata.xlsx')
# df = pd.read_excel(f'{files_directory}/MBA001_cleandata.xlsx', index_col=0)
* Outlier Detection using different statistical or machine learning techniques:
The Z-score measures how many standard deviations a data point is away from the median of the dataset, making it robust to outliers. The values returned are the Z-scores for each data point. A higher Z-score indicates a more extreme deviation from the median.
It fits an Isolation Forest model to the input data and predicts whether each data point is an outlier or not. The values returned are typically -1 for outliers and 1 for inliers (non-outliers). So, you get a list of -1s and 1s indicating the outlier status of each data point.
It fits a One-Class SVM model to the input data and predicts whether each data point is an outlier or not. The values returned are typically -1 for outliers and 1 for inliers. So, like the Isolation Forest, you get a list of -1s and 1s indicating the outlier status of each data point.
It calculates the first quartile (Q1) and third quartile (Q3) of the data and defines a range within which data points are considered normal. Data points falling outside this range are identified as outliers. The values returned are typically boolean (True or False), where True indicates an outlier and False indicates an inlier.
It calculates the Z-scores for each data point by standardizing the data (subtracting the mean and dividing by the standard deviation). Data points with absolute Z-scores greater than a specified threshold are considered outliers. The values returned are typically boolean (True or False), where True indicates an outlier and False indicates an inlier.
from sklearn.ensemble import IsolationForest
from sklearn.svm import OneClassSVM
from sklearn.impute import SimpleImputer
# Identificação de outliers. Outlier detection
# Criação do df outliers_score utilizando 5 metodos distintos. Create the outliers_score DataFrame using 5 different methods
# Apesar desse passo estar sendo realizado agora, idealmente deve ser realizado após a eliminação de variaveis desnecessarias.
# Economizando tempo de processamento. Although this step is being performed now, ideally it should be performed after the elimination of unnecessary variables. Saving processing time.
start_timer()
# Fill missing values with 0
df.fillna(0, inplace=True)
# Specify the range [2:-1] for columns
df_subset = df.iloc[:, 2:-1]
#df_subset = df.iloc[2:-1, :]
# Initialize the results DataFrame
results_df_outliers = pd.DataFrame(index=df_subset.index)
# Define outlier detection functions
def robust_z_score(data):
median = np.median(data)
mad = np.median(np.abs(data - median))
return np.abs(data - median) / mad
def detect_outliers_isolation_forest(data):
isolation_forest = IsolationForest(contamination=0.10)
isolation_forest.fit(data)
return isolation_forest.predict(data)
def detect_outliers_one_class_svm(data):
one_class_svm = OneClassSVM(nu=0.20)
one_class_svm.fit(data)
return one_class_svm.predict(data)
def detect_outliers_iqr(data):
Q1 = np.percentile(data, 25)
Q3 = np.percentile(data, 75)
iqr = Q3 - Q1
lower_bound = Q1 - 1.5 * iqr
upper_bound = Q3 + 1.5 * iqr
return np.where((data < lower_bound) | (data > upper_bound), True, False)
def detect_outliers_z_score(data):
z_scores = (data - np.mean(data)) / np.std(data)
threshold_std = 2 # Adjust threshold as needed
return np.where(np.abs(z_scores) > threshold_std, True, False)
# Initialize an empty dictionary to store results
results_dict = {}
# Iterate through columns and perform outlier detection methods
for col in df_subset.columns:
column_data = df_subset[col].values
# Robust Z-Score
median = np.median(column_data)
mad = np.median(np.abs(column_data - median))
# Handle division by zero by checking if mad is non-zero
if mad != 0:
robust_z_scores = np.abs(column_data - median) / mad
else:
robust_z_scores = np.zeros_like(column_data)
# Isolation Forest
outliers_if = detect_outliers_isolation_forest(column_data.reshape(-1, 1))
# One-Class SVM
outliers_svm = detect_outliers_one_class_svm(column_data.reshape(-1, 1))
# Interquartile Range (IQR)
outliers_iqr = detect_outliers_iqr(column_data)
# Standard Deviation (Z-Score)
#outliers_std = detect_outliers_z_score(column_data)
# Standard Deviation (Z-Score) method
std_dev = np.std(column_data)
if std_dev == 0:
outliers_std = np.zeros_like(column_data)
else:
z_scores = (column_data - np.mean(column_data)) / std_dev
# Convert the Z-Scores to 1 for outliers, 0 for non-outliers
threshold_std = 1 # Adjust threshold as needed
outliers_std = np.where(np.abs(z_scores) > threshold_std, 1, 0)
# Reshape the outliers_std array to be 1-dimensional
outliers_std = outliers_std.flatten()
# Store results in the dictionary
results_dict[f'{col}_RobustZScore'] = robust_z_scores
results_dict[f'{col}_IsolationForest'] = 0.5 -(outliers_if/2) # Convert -1 to 1 to 0 to 1
results_dict[f'{col}_OneClassSVM'] = 0.5 -(outliers_svm/2) # Convert -1 to 1 to 0 to 1
results_dict[f'{col}_IQR'] = outliers_iqr.astype(int)
results_dict[f'{col}_ZScore'] = outliers_std.astype(int)
# Create the results DataFrame from the dictionary
results_df_outliers = pd.DataFrame(results_dict)
# Display the results
display(results_df_outliers)
# Define a function to map the outlier status to 1 or 0 based on threshold
def map_outlier_status(column, threshold):
return column.map(lambda x: 0 if x < threshold else 1)
# Define the threshold for robust_z_score and detect_outliers_z_score
robust_z_score_threshold = 3 # Set the desired threshold
z_score_threshold = 3 # Set the desired threshold
# Apply the mapping function to each relevant column
for col_name in results_df_outliers.columns:
if 'RobustZScore' in col_name:
results_df_outliers[col_name] = map_outlier_status(results_df_outliers[col_name], robust_z_score_threshold)
elif 'IsolationForest' in col_name:
results_df_outliers[col_name] = map_outlier_status(results_df_outliers[col_name], 1)
elif 'OneClassSVM' in col_name:
results_df_outliers[col_name] = map_outlier_status(results_df_outliers[col_name], 1)
elif 'IQR' in col_name:
results_df_outliers[col_name] = map_outlier_status(results_df_outliers[col_name], 1)
elif 'ZScore' in col_name:
results_df_outliers[col_name] = map_outlier_status(results_df_outliers[col_name], z_score_threshold)
# Print updated results_df
display(results_df_outliers)
# Salva o arquivo results_df_outliers
results_df_outliers.to_excel(f'{files_directory}/MBA001_results_df_outliers.xlsx')
# Initialize a new DataFrame to store the summed outlier scores
outliers_score = pd.DataFrame()
# Sum the outlier scores column-wise for each field in the original DataFrame
for field in df_subset.columns:
field_scores = results_df_outliers.filter(like=field)
field_sum = field_scores.sum(axis=1)
outliers_score[field] = field_sum
# Print 'outliers_score' DataFrame
display(outliers_score)
# Salva o arquivo results_df_outliers
outliers_score.to_excel(f'{files_directory}/MBA001_outliers_score.xlsx')
end_timer()
| K00001_RobustZScore | K00001_IsolationForest | K00001_OneClassSVM | K00001_IQR | K00001_ZScore | K00002_RobustZScore | K00002_IsolationForest | K00002_OneClassSVM | K00002_IQR | K00002_ZScore | ... | K00539_RobustZScore | K00539_IsolationForest | K00539_OneClassSVM | K00539_IQR | K00539_ZScore | K00540_RobustZScore | K00540_IsolationForest | K00540_OneClassSVM | K00540_IQR | K00540_ZScore | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.000000 | 0.0 | 0.0 | 0 | 0 | 1.000000 | 0.0 | 0.0 | 0 | 0 | ... | 0.0 | 0.0 | 1.0 | 0 | 0 | 0.0 | 0.0 | 1.0 | 0 | 0 |
| 1 | 2.666667 | 1.0 | 1.0 | 0 | 1 | 2.666667 | 1.0 | 1.0 | 0 | 1 | ... | 0.0 | 0.0 | 1.0 | 0 | 0 | 0.0 | 0.0 | 1.0 | 0 | 0 |
| 2 | 0.666667 | 0.0 | 0.0 | 0 | 0 | 0.666667 | 0.0 | 0.0 | 0 | 0 | ... | 0.0 | 0.0 | 0.0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 3 | 3.333333 | 1.0 | 1.0 | 0 | 1 | 3.333333 | 1.0 | 1.0 | 0 | 1 | ... | 0.0 | 0.0 | 1.0 | 0 | 0 | 0.0 | 0.0 | 1.0 | 0 | 0 |
| 4 | 3.333333 | 0.0 | 0.0 | 0 | 1 | 3.333333 | 0.0 | 0.0 | 0 | 1 | ... | 0.0 | 0.0 | 1.0 | 0 | 0 | 0.0 | 0.0 | 1.0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 0.666667 | 0.0 | 0.0 | 0 | 0 | 0.666667 | 0.0 | 0.0 | 0 | 0 | ... | 0.0 | 0.0 | 1.0 | 0 | 0 | 0.0 | 0.0 | 1.0 | 0 | 0 |
| 18979 | 1.000000 | 0.0 | 1.0 | 0 | 0 | 1.000000 | 0.0 | 0.0 | 0 | 0 | ... | 0.0 | 0.0 | 1.0 | 0 | 0 | 0.0 | 0.0 | 1.0 | 0 | 0 |
| 18980 | 1.666667 | 0.0 | 0.0 | 0 | 1 | 1.666667 | 0.0 | 0.0 | 0 | 1 | ... | 0.0 | 1.0 | 1.0 | 1 | 1 | 0.0 | 1.0 | 1.0 | 1 | 0 |
| 18981 | 3.666667 | 0.0 | 0.0 | 0 | 1 | 3.666667 | 0.0 | 1.0 | 0 | 1 | ... | 0.0 | 0.0 | 1.0 | 0 | 0 | 0.0 | 0.0 | 1.0 | 0 | 0 |
| 18982 | 0.333333 | 0.0 | 1.0 | 0 | 0 | 0.333333 | 0.0 | 0.0 | 0 | 0 | ... | 0.0 | 1.0 | 1.0 | 1 | 1 | 0.0 | 0.0 | 0.0 | 0 | 0 |
18983 rows × 2700 columns
| K00001_RobustZScore | K00001_IsolationForest | K00001_OneClassSVM | K00001_IQR | K00001_ZScore | K00002_RobustZScore | K00002_IsolationForest | K00002_OneClassSVM | K00002_IQR | K00002_ZScore | ... | K00539_RobustZScore | K00539_IsolationForest | K00539_OneClassSVM | K00539_IQR | K00539_ZScore | K00540_RobustZScore | K00540_IsolationForest | K00540_OneClassSVM | K00540_IQR | K00540_ZScore | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 4 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 18979 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 18980 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 |
| 18981 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 18982 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
18983 rows × 2700 columns
| K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00008 | K00009 | K00010 | ... | K00531 | K00532 | K00533 | K00534 | K00535 | K00536 | K00537 | K00538 | K00539 | K00540 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | ... | 2 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 |
| 1 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 3 | 3 | 3 | 3 | 3 | 0 | 0 | 3 | 3 | 3 | 3 | ... | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 |
| 4 | 1 | 1 | 1 | 1 | 0 | 0 | 3 | 3 | 3 | 3 | ... | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 3 | 2 | ... | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 |
| 18979 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | ... | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 |
| 18980 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 3 | ... | 1 | 1 | 1 | 3 | 3 | 1 | 3 | 3 | 3 | 3 |
| 18981 | 1 | 2 | 1 | 2 | 0 | 0 | 1 | 1 | 1 | 1 | ... | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 |
| 18982 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 3 | 0 |
18983 rows × 540 columns
Tempo de execução: 4748.098927497864 segundos
df = pd.read_excel(f'{files_directory}/MBA001_cleandata.xlsx', index_col=0)
df
| user_id | bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00008 | ... | K00532 | K00533 | K00534 | K00535 | K00536 | K00537 | K00538 | K00539 | K00540 | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 14.0 | 14.0 | 14.0 | 14.0 | 0.0 | 0.0 | NaN | NaN | ... | 3000.000000 | NaN | 0.0 | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | 0 |
| 1 | 1 | 1 | 19.0 | 19.0 | 19.0 | 19.0 | 0.0 | 0.0 | 41.0 | 41.0 | ... | 2964.199951 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 |
| 2 | 2 | 0 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | NaN | NaN | ... | 1280.729980 | 0.0 | 0.0 | 20.0 | 0.0 | 0.0 | 1.0 | 1.0 | 20.0 | 0 |
| 3 | 3 | 2 | 21.0 | 21.0 | 21.0 | 21.0 | 0.0 | 0.0 | 310.0 | 310.0 | ... | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 |
| 4 | 4 | 3 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 257.0 | 257.0 | ... | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 26819 | 18978 | 2 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | 95.0 | 95.0 | ... | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 |
| 26820 | 18979 | 0 | 8.0 | 8.0 | 8.0 | 8.0 | 0.0 | 0.0 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0 |
| 26821 | 18980 | 2 | 16.0 | 16.0 | 16.0 | 16.0 | 0.0 | 0.0 | 217.0 | 217.0 | ... | 3199.500000 | 20.0 | 1090.0 | 1155.0 | 1.0 | 6.0 | 10.0 | 3.0 | 385.0 | 0 |
| 26822 | 18981 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0 |
| 26823 | 18982 | 0 | 12.0 | 12.0 | 12.0 | 12.0 | 0.0 | 0.0 | NaN | NaN | ... | 3084.139893 | 12.0 | 32.0 | 42.0 | 1.0 | 2.0 | 3.0 | 3.0 | 14.0 | 0 |
18983 rows × 543 columns
# Analise exploratoria inicial. Initial exploratory analysis
# Adiciona a moda de cada variavel ao describe. Add the mode of each variable to the describe
modes = df.mode()
# Percentis com passo de 5% no describe. Percentiles in describe with steps of 5%
describe = round(df.describe(include='all', percentiles=np.arange(0, 1.05, 0.05))).T
describe['mode'] = modes.T[0]
display(describe.head())
| count | mean | std | min | 0% | 5% | 10% | 15% | 20% | 25% | ... | 65% | 70% | 75% | 80% | 85% | 90% | 95% | 100% | max | mode | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| user_id | 18983.0 | 9491.0 | 5480.0 | 0.0 | 0.0 | 949.0 | 1898.0 | 2847.0 | 3796.0 | 4746.0 | ... | 12338.0 | 13287.0 | 14236.0 | 15186.0 | 16135.0 | 17084.0 | 18033.0 | 18982.0 | 18982.0 | 0.0 |
| bank | 18983.0 | 4.0 | 4.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 5.0 | 5.0 | 6.0 | 9.0 | 10.0 | 11.0 | 11.0 | 23.0 | 23.0 | 0.0 |
| K00001 | 15525.0 | 10.0 | 5.0 | 0.0 | 0.0 | 1.0 | 2.0 | 4.0 | 6.0 | 8.0 | ... | 12.0 | 13.0 | 13.0 | 13.0 | 14.0 | 15.0 | 17.0 | 24.0 | 24.0 | 12.0 |
| K00002 | 15525.0 | 10.0 | 5.0 | 0.0 | 0.0 | 1.0 | 2.0 | 4.0 | 6.0 | 7.0 | ... | 12.0 | 13.0 | 13.0 | 13.0 | 14.0 | 15.0 | 17.0 | 22.0 | 22.0 | 12.0 |
| K00003 | 15525.0 | 10.0 | 5.0 | 0.0 | 0.0 | 1.0 | 2.0 | 4.0 | 6.0 | 8.0 | ... | 12.0 | 13.0 | 13.0 | 13.0 | 14.0 | 15.0 | 17.0 | 22.0 | 22.0 | 12.0 |
5 rows × 27 columns
describe.columns
Index(['count', 'mean', 'std', 'min', '0%', '5%', '10%', '15%', '20%', '25%',
'30%', '35%', '40%', '45%', '50%', '55%', '60%', '65%', '70%', '75%',
'80%', '85%', '90%', '95%', '100%', 'max', 'mode'],
dtype='object')
# Analise explorátoria abrangente. Comprehensive exploratory analysis
def calculate_woe_iv(data, feature, target):
"""
Calculate the Weight of Evidence (WOE) and Information Value (IV) for a given feature.
Parameters:
data (DataFrame): The input DataFrame containing the feature and target columns.
feature (str): The name of the feature column (continuous variable).
target (str): The name of the target column (dependent variable).
Returns:
float: Information Value (IV) for the given feature.
"""
# Cria uma cópia dos dados de entrada para evitar modificar o DataFrame original
# Create a copy of the input data to avoid modifying the original DataFrame
data_copy = data.copy()
# Preenche valores faltantes na coluna feature com zero (não faz sentido pois a informação de valores faltantes seria perdida)
# Fill missing values in the feature column with zeros
# data_copy[feature].fillna(0, inplace=True)
# Calcula o número total de instâncias positivas e negativas na variável alvo
# Calculate the total number of positive and negative instances in the target variable
total_positives = data_copy[target].sum()
total_negatives = data_copy[target].count() - total_positives
# Lida com caso especial se não houver instâncias positivas ou negativas
# Handle special case if there are no positive or negative instances
if total_positives == 0 or total_negatives == 0:
raise ValueError("The target variable should have both positive and negative instances.")
# Verifica se existem valores válidos na coluna antes de calcular os quantis
# Check if there are valid values in the column before calculating quantiles
if data_copy[feature].notnull().any():
# Calculate the total number of bins to be used
# Use the following to create 20 equal-frequency bins:
data_copy['bins'] = pd.qcut(data_copy[feature], q=20, duplicates='drop')
# Calcula o número de instâncias positivas e negativas em cada bin
# Calculate the number of positive and negative instances in each bin
grouped = data_copy.groupby('bins')
bin_counts = grouped[target].agg(['sum', 'count'])
# Calcula o WoE para cada bin e lida com o caso em que o WoE é infinito (log(0))
# Calculate the WoE for each bin and handle the case where WoE is infinite (log(0))
bin_counts['woe'] = np.log((bin_counts['sum'] / total_positives) / (bin_counts['count'] / total_negatives))
bin_counts.replace([np.inf, -np.inf], 0, inplace=True)
# Calcula o IV para a variável somando as contribuições de cada bin
# Calculate the IV for the feature by summing up the contributions from each bin
iv = ((bin_counts['sum'] / total_positives) - (bin_counts['count'] / total_negatives)) * bin_counts['woe']
iv = iv.sum()
else:
# Se não houver valores válidos na coluna, define IV como 0
# If there are no valid values in the column, set IV to 0
iv = 0
return iv
# Calculate IV for each continuous feature in the DataFrame 'df'
iv_values = {}
continuous_features = df.select_dtypes(include=[np.number]).columns.tolist()
for feature in continuous_features:
iv = calculate_woe_iv(df, feature, 'label')
iv_values[feature] = iv
# Cria um DataFrame para armazenar as estatísticas de uma analise exploratoria abrangente
# Create a DataFrame to store summary statistics
describe = round(df.describe(include='all', percentiles=np.arange(0, 1.05, 0.05))).T
# Adiciona o valores iguais a zero, número de valores faltantes e % de valores faltantes
# Add Number of number of zeros, Number of missing, and % missing columns
describe['num_zeros'] = (df == 0).sum()
describe['num_NaN'] = df.isnull().sum()
describe['perc_NaN'] = (df.isnull().sum() / len(df)) * 100
# Adiciona a coluna IV ao DataFrame describe
# Add the IV column to the describe DataFrame
describe['IV'] = pd.Series(iv_values)
# Adiciona a coluna Mode ao DataFrame describe
# Add the Mode column to the describe DataFrame
describe['mode'] = df.mode().T[0]
# Reordena as colunas conforme a necessidade
# Reorder the columns as per the requirement
column_order = ['count', 'num_zeros', 'num_NaN', 'perc_NaN',
'IV', 'mode', 'mean', 'std', 'min', '0%', '5%', '10%', '15%', '20%', '25%',
'30%', '35%', '40%', '45%', '50%', '55%', '60%', '65%', '70%', '75%',
'80%', '85%', '90%', '95%', '100%', 'max']
describe = describe[column_order]
# Display the updated describe DataFrame
display(round(describe,2).head(10))
# Salva o arquivo describe em formato excel
describe.to_excel(f'{files_directory}/MBA001_describe.xlsx', index=False)
| count | num_zeros | num_NaN | perc_NaN | IV | mode | mean | std | min | 0% | ... | 60% | 65% | 70% | 75% | 80% | 85% | 90% | 95% | 100% | max | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| user_id | 18983.0 | 1 | 0 | 0.00 | 0.02 | 0.0 | 9491.0 | 5480.0 | 0.0 | 0.0 | ... | 11389.0 | 12338.0 | 13287.0 | 14236.0 | 15186.0 | 16135.0 | 17084.0 | 18033.0 | 18982.0 | 18982.0 |
| bank | 18983.0 | 4952 | 0 | 0.00 | 0.04 | 0.0 | 4.0 | 4.0 | 0.0 | 0.0 | ... | 4.0 | 5.0 | 5.0 | 6.0 | 9.0 | 10.0 | 11.0 | 11.0 | 23.0 | 23.0 |
| K00001 | 15525.0 | 685 | 3458 | 18.22 | 0.09 | 12.0 | 10.0 | 5.0 | 0.0 | 0.0 | ... | 12.0 | 12.0 | 13.0 | 13.0 | 13.0 | 14.0 | 15.0 | 17.0 | 24.0 | 24.0 |
| K00002 | 15525.0 | 719 | 3458 | 18.22 | 0.10 | 12.0 | 10.0 | 5.0 | 0.0 | 0.0 | ... | 12.0 | 12.0 | 13.0 | 13.0 | 13.0 | 14.0 | 15.0 | 17.0 | 22.0 | 22.0 |
| K00003 | 15525.0 | 685 | 3458 | 18.22 | 0.09 | 12.0 | 10.0 | 5.0 | 0.0 | 0.0 | ... | 12.0 | 12.0 | 13.0 | 13.0 | 13.0 | 14.0 | 15.0 | 17.0 | 22.0 | 22.0 |
| K00004 | 15525.0 | 685 | 3458 | 18.22 | 0.09 | 12.0 | 10.0 | 5.0 | 0.0 | 0.0 | ... | 12.0 | 12.0 | 13.0 | 13.0 | 13.0 | 14.0 | 15.0 | 17.0 | 22.0 | 22.0 |
| K00005 | 15525.0 | 15387 | 3458 | 18.22 | 0.00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 9.0 | 9.0 |
| K00006 | 15525.0 | 15387 | 3458 | 18.22 | 0.00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 2.0 |
| K00007 | 5989.0 | 4 | 12994 | 68.45 | 0.05 | 39.0 | 124.0 | 140.0 | 0.0 | 0.0 | ... | 132.0 | 146.0 | 162.0 | 178.0 | 196.0 | 219.0 | 256.0 | 301.0 | 1463.0 | 1463.0 |
| K00008 | 5989.0 | 4 | 12994 | 68.45 | 0.05 | 39.0 | 124.0 | 140.0 | 0.0 | 0.0 | ... | 132.0 | 146.0 | 162.0 | 178.0 | 196.0 | 219.0 | 256.0 | 301.0 | 1463.0 | 1463.0 |
10 rows × 31 columns
| Faixa de IV | Interpretação |
|---|---|
| 0 até 0.02 | Sem poder preditivo |
| 0.02 até 0.1 | Poder preditivo fraco |
| 0.1 até 0.3 | Poder preditivo moderado |
| 0.3 até 0.5 | Poder preditivo forte |
| 0.5 e acima | (Suspeito) Poder preditivo muito forte |
| Range of IV | Interpretation |
|---|---|
| 0 to 0.02 | No predictive power |
| 0.02 to 0.1 | Weak predictive power |
| 0.1 to 0.3 | Moderate predictive power |
| 0.3 to 0.5 | Strong predictive power |
| 0.5 and above | (Suspicious) Very strong predictive power |
# Count occurrences of 0 in columns from index 2 to 541
columns_to_count = df.columns[2:542]
count_of_zeros = (df[columns_to_count] == 0).sum()
print('Counts\n', 'Count occurrences of 0 in columns\n',count_of_zeros.to_dict())
# Count occurrences of NaN in columns from index 2 to 541
columns_to_count = df.columns[2:542]
count_of_nans = df[columns_to_count].isna().sum()
print('\nCount occurrences of NaN in columns\n',count_of_nans.to_dict())
# Count occurrences of NaN and zero in columns from index 2 to 541
print('\nCount occurrences of sum of NaN and zero in columns\n',(count_of_zeros+count_of_nans).to_dict())
Counts
Count occurrences of 0 in columns
{'K00001': 685, 'K00002': 719, 'K00003': 685, 'K00004': 685, 'K00005': 15387, 'K00006': 15387, 'K00007': 4, 'K00008': 4, 'K00009': 4, 'K00010': 4, 'K00011': 5989, 'K00012': 5989, 'K00013': 728, 'K00014': 870, 'K00015': 728, 'K00016': 739, 'K00017': 7660, 'K00018': 7660, 'K00019': 5, 'K00020': 5, 'K00021': 5, 'K00022': 5, 'K00023': 8144, 'K00024': 8144, 'K00025': 12488, 'K00026': 2701, 'K00027': 6495, 'K00028': 5666, 'K00029': 102, 'K00030': 362, 'K00031': 485, 'K00032': 358, 'K00033': 361, 'K00034': 4623, 'K00035': 4631, 'K00036': 207, 'K00037': 724, 'K00038': 198, 'K00039': 221, 'K00040': 1228, 'K00041': 1362, 'K00042': 166, 'K00043': 890, 'K00044': 157, 'K00045': 258, 'K00046': 678, 'K00047': 768, 'K00048': 1183, 'K00049': 1439, 'K00050': 1183, 'K00051': 1210, 'K00052': 3909, 'K00053': 3909, 'K00054': 1029, 'K00055': 1836, 'K00056': 1029, 'K00057': 1064, 'K00058': 1918, 'K00059': 1918, 'K00060': 990, 'K00061': 2119, 'K00062': 990, 'K00063': 1229, 'K00064': 1388, 'K00065': 1388, 'K00066': 0, 'K00067': 0, 'K00068': 0, 'K00069': 0, 'K00070': 2871, 'K00071': 2875, 'K00072': 0, 'K00073': 0, 'K00074': 0, 'K00075': 0, 'K00076': 1466, 'K00077': 1524, 'K00078': 0, 'K00079': 0, 'K00080': 0, 'K00081': 0, 'K00082': 911, 'K00083': 943, 'K00084': 1421, 'K00085': 457, 'K00086': 389, 'K00087': 348, 'K00088': 0, 'K00089': 0, 'K00090': 0, 'K00091': 0, 'K00092': 816, 'K00093': 602, 'K00094': 472, 'K00095': 812, 'K00096': 596, 'K00097': 471, 'K00098': 241, 'K00099': 201, 'K00100': 163, 'K00101': 241, 'K00102': 201, 'K00103': 163, 'K00104': 1023, 'K00105': 762, 'K00106': 611, 'K00107': 1018, 'K00108': 754, 'K00109': 609, 'K00110': 295, 'K00111': 241, 'K00112': 214, 'K00113': 295, 'K00114': 241, 'K00115': 214, 'K00116': 1232, 'K00117': 947, 'K00118': 775, 'K00119': 1232, 'K00120': 947, 'K00121': 775, 'K00122': 342, 'K00123': 271, 'K00124': 236, 'K00125': 342, 'K00126': 271, 'K00127': 236, 'K00128': 10651, 'K00129': 10483, 'K00130': 10566, 'K00131': 10638, 'K00132': 10465, 'K00133': 10549, 'K00134': 10712, 'K00135': 11014, 'K00136': 11262, 'K00137': 10711, 'K00138': 11013, 'K00139': 11262, 'K00140': 863, 'K00141': 820, 'K00142': 781, 'K00143': 10957, 'K00144': 11106, 'K00145': 11442, 'K00146': 10957, 'K00147': 11106, 'K00148': 11442, 'K00149': 11048, 'K00150': 11012, 'K00151': 10998, 'K00152': 11048, 'K00153': 11012, 'K00154': 10998, 'K00155': 2444, 'K00156': 2011, 'K00157': 1804, 'K00158': 2444, 'K00159': 2011, 'K00160': 1804, 'K00161': 2205, 'K00162': 1839, 'K00163': 1671, 'K00164': 2205, 'K00165': 1839, 'K00166': 1671, 'K00167': 8829, 'K00168': 8687, 'K00169': 8928, 'K00170': 8829, 'K00171': 8687, 'K00172': 8928, 'K00173': 7966, 'K00174': 7956, 'K00175': 7902, 'K00176': 7966, 'K00177': 7956, 'K00178': 7902, 'K00179': 2273, 'K00180': 1893, 'K00181': 1658, 'K00182': 2273, 'K00183': 1893, 'K00184': 1658, 'K00185': 18983, 'K00186': 18983, 'K00187': 18983, 'K00188': 18983, 'K00189': 18983, 'K00190': 18983, 'K00191': 11533, 'K00192': 11936, 'K00193': 12430, 'K00194': 11533, 'K00195': 11936, 'K00196': 12430, 'K00197': 18983, 'K00198': 18983, 'K00199': 18983, 'K00200': 18983, 'K00201': 18983, 'K00202': 18983, 'K00203': 18983, 'K00204': 18983, 'K00205': 18983, 'K00206': 18983, 'K00207': 18983, 'K00208': 18983, 'K00209': 1626, 'K00210': 0, 'K00211': 0, 'K00212': 3762, 'K00213': 4048, 'K00214': 4050, 'K00215': 3762, 'K00216': 4048, 'K00217': 4050, 'K00218': 0, 'K00219': 0, 'K00220': 0, 'K00221': 12645, 'K00222': 13322, 'K00223': 13972, 'K00224': 12645, 'K00225': 13322, 'K00226': 13972, 'K00227': 12558, 'K00228': 13222, 'K00229': 13867, 'K00230': 12558, 'K00231': 13222, 'K00232': 13867, 'K00233': 12593, 'K00234': 13239, 'K00235': 13866, 'K00236': 12593, 'K00237': 13239, 'K00238': 13866, 'K00239': 10916, 'K00240': 10803, 'K00241': 11243, 'K00242': 10915, 'K00243': 10802, 'K00244': 11242, 'K00245': 12260, 'K00246': 12584, 'K00247': 13152, 'K00248': 12258, 'K00249': 12582, 'K00250': 13150, 'K00251': 12475, 'K00252': 13083, 'K00253': 13680, 'K00254': 12475, 'K00255': 13083, 'K00256': 13680, 'K00257': 12408, 'K00258': 12026, 'K00259': 11918, 'K00260': 12408, 'K00261': 12026, 'K00262': 11918, 'K00263': 11825, 'K00264': 11690, 'K00265': 11900, 'K00266': 11822, 'K00267': 11688, 'K00268': 11898, 'K00269': 12548, 'K00270': 13156, 'K00271': 13732, 'K00272': 12548, 'K00273': 13156, 'K00274': 13732, 'K00275': 12597, 'K00276': 13270, 'K00277': 13830, 'K00278': 12597, 'K00279': 13270, 'K00280': 13830, 'K00281': 9, 'K00282': 12, 'K00283': 6, 'K00284': 0, 'K00285': 0, 'K00286': 0, 'K00287': 0, 'K00288': 0, 'K00289': 0, 'K00290': 8589, 'K00291': 9439, 'K00292': 9922, 'K00293': 8589, 'K00294': 9439, 'K00295': 9922, 'K00296': 0, 'K00297': 9922, 'K00298': 7857, 'K00299': 8443, 'K00300': 8739, 'K00301': 7857, 'K00302': 8443, 'K00303': 8739, 'K00304': 0, 'K00305': 8739, 'K00306': 2062, 'K00307': 1797, 'K00308': 1891, 'K00309': 2062, 'K00310': 1797, 'K00311': 1891, 'K00312': 0, 'K00313': 1891, 'K00314': 9644, 'K00315': 10501, 'K00316': 10995, 'K00317': 9644, 'K00318': 10501, 'K00319': 10995, 'K00320': 0, 'K00321': 10995, 'K00322': 9993, 'K00323': 11061, 'K00324': 11741, 'K00325': 9993, 'K00326': 11061, 'K00327': 11741, 'K00328': 0, 'K00329': 11741, 'K00330': 10086, 'K00331': 11175, 'K00332': 11865, 'K00333': 10086, 'K00334': 11175, 'K00335': 11865, 'K00336': 0, 'K00337': 11865, 'K00338': 10081, 'K00339': 11168, 'K00340': 11856, 'K00341': 10081, 'K00342': 11168, 'K00343': 11856, 'K00344': 0, 'K00345': 11856, 'K00346': 10086, 'K00347': 11175, 'K00348': 11865, 'K00349': 10086, 'K00350': 11175, 'K00351': 11865, 'K00352': 0, 'K00353': 11865, 'K00354': 9163, 'K00355': 9868, 'K00356': 10262, 'K00357': 9163, 'K00358': 9868, 'K00359': 10262, 'K00360': 0, 'K00361': 10262, 'K00362': 7894, 'K00363': 8307, 'K00364': 8506, 'K00365': 7882, 'K00366': 8291, 'K00367': 8490, 'K00368': 0, 'K00369': 8507, 'K00370': 14257, 'K00371': 14928, 'K00372': 15471, 'K00373': 14257, 'K00374': 14928, 'K00375': 15471, 'K00376': 13729, 'K00377': 14045, 'K00378': 14357, 'K00379': 13729, 'K00380': 14045, 'K00381': 14357, 'K00382': 13336, 'K00383': 13468, 'K00384': 13659, 'K00385': 13336, 'K00386': 13468, 'K00387': 13659, 'K00388': 14112, 'K00389': 14667, 'K00390': 15104, 'K00391': 14112, 'K00392': 14667, 'K00393': 15104, 'K00394': 11051, 'K00395': 10340, 'K00396': 10054, 'K00397': 11051, 'K00398': 10340, 'K00399': 10054, 'K00400': 14086, 'K00401': 14697, 'K00402': 15180, 'K00403': 14086, 'K00404': 14697, 'K00405': 15180, 'K00406': 13523, 'K00407': 13890, 'K00408': 14202, 'K00409': 13523, 'K00410': 13890, 'K00411': 14202, 'K00412': 13932, 'K00413': 14313, 'K00414': 14670, 'K00415': 13932, 'K00416': 14313, 'K00417': 14670, 'K00418': 14133, 'K00419': 14749, 'K00420': 15184, 'K00421': 14133, 'K00422': 14749, 'K00423': 15184, 'K00424': 14253, 'K00425': 14927, 'K00426': 15474, 'K00427': 14253, 'K00428': 14927, 'K00429': 15474, 'K00430': 14192, 'K00431': 14827, 'K00432': 15337, 'K00433': 14192, 'K00434': 14827, 'K00435': 15337, 'K00436': 14073, 'K00437': 14627, 'K00438': 15076, 'K00439': 14073, 'K00440': 14627, 'K00441': 15076, 'K00442': 13888, 'K00443': 14327, 'K00444': 14659, 'K00445': 13888, 'K00446': 14327, 'K00447': 14659, 'K00448': 14212, 'K00449': 14865, 'K00450': 15398, 'K00451': 14212, 'K00452': 14865, 'K00453': 15398, 'K00454': 10932, 'K00455': 10412, 'K00456': 10206, 'K00457': 10932, 'K00458': 10412, 'K00459': 10206, 'K00460': 13947, 'K00461': 14526, 'K00462': 14940, 'K00463': 13946, 'K00464': 14525, 'K00465': 14940, 'K00466': 14121, 'K00467': 14740, 'K00468': 15266, 'K00469': 14121, 'K00470': 14740, 'K00471': 15266, 'K00472': 14255, 'K00473': 14928, 'K00474': 15477, 'K00475': 14255, 'K00476': 14928, 'K00477': 15477, 'K00478': 13351, 'K00479': 13713, 'K00480': 14102, 'K00481': 13296, 'K00482': 13713, 'K00483': 14102, 'K00484': 0, 'K00485': 0, 'K00486': 0, 'K00487': 0, 'K00488': 0, 'K00489': 0, 'K00490': 0, 'K00491': 0, 'K00492': 0, 'K00493': 6338, 'K00494': 6482, 'K00495': 6771, 'K00496': 6338, 'K00497': 6482, 'K00498': 6771, 'K00499': 0, 'K00500': 6771, 'K00501': 9778, 'K00502': 10646, 'K00503': 11210, 'K00504': 9778, 'K00505': 10646, 'K00506': 11210, 'K00507': 0, 'K00508': 11210, 'K00509': 10071, 'K00510': 11034, 'K00511': 11732, 'K00512': 10071, 'K00513': 11034, 'K00514': 11732, 'K00515': 0, 'K00516': 11732, 'K00517': 8455, 'K00518': 9070, 'K00519': 9381, 'K00520': 8455, 'K00521': 9070, 'K00522': 9381, 'K00523': 0, 'K00524': 9381, 'K00525': 2186, 'K00526': 1641, 'K00527': 1778, 'K00528': 2186, 'K00529': 1641, 'K00530': 1778, 'K00531': 0, 'K00532': 1778, 'K00533': 6677, 'K00534': 6401, 'K00535': 6269, 'K00536': 6677, 'K00537': 6401, 'K00538': 6269, 'K00539': 0, 'K00540': 6269}
Count occurrences of NaN in columns
{'K00001': 3458, 'K00002': 3458, 'K00003': 3458, 'K00004': 3458, 'K00005': 3458, 'K00006': 3458, 'K00007': 12994, 'K00008': 12994, 'K00009': 12994, 'K00010': 12994, 'K00011': 12994, 'K00012': 12994, 'K00013': 9774, 'K00014': 9774, 'K00015': 9774, 'K00016': 9774, 'K00017': 9774, 'K00018': 9774, 'K00019': 10839, 'K00020': 10839, 'K00021': 10839, 'K00022': 10839, 'K00023': 10839, 'K00024': 10839, 'K00025': 6495, 'K00026': 0, 'K00027': 0, 'K00028': 7117, 'K00029': 15012, 'K00030': 13206, 'K00031': 13207, 'K00032': 13204, 'K00033': 13204, 'K00034': 13204, 'K00035': 13204, 'K00036': 12536, 'K00037': 12540, 'K00038': 12535, 'K00039': 12535, 'K00040': 12535, 'K00041': 12535, 'K00042': 12296, 'K00043': 12301, 'K00044': 12294, 'K00045': 12294, 'K00046': 12294, 'K00047': 12294, 'K00048': 14191, 'K00049': 14191, 'K00050': 14191, 'K00051': 14191, 'K00052': 14191, 'K00053': 14191, 'K00054': 13591, 'K00055': 13591, 'K00056': 13591, 'K00057': 13591, 'K00058': 13591, 'K00059': 13591, 'K00060': 13372, 'K00061': 13372, 'K00062': 13372, 'K00063': 13372, 'K00064': 13372, 'K00065': 13372, 'K00066': 15374, 'K00067': 15374, 'K00068': 15374, 'K00069': 15374, 'K00070': 15374, 'K00071': 15374, 'K00072': 14620, 'K00073': 14620, 'K00074': 14620, 'K00075': 14620, 'K00076': 14620, 'K00077': 14620, 'K00078': 14362, 'K00079': 14362, 'K00080': 14362, 'K00081': 14362, 'K00082': 14362, 'K00083': 14362, 'K00084': 12851, 'K00085': 6701, 'K00086': 6029, 'K00087': 5383, 'K00088': 17513, 'K00089': 18243, 'K00090': 18764, 'K00091': 18935, 'K00092': 6448, 'K00093': 5771, 'K00094': 5120, 'K00095': 6448, 'K00096': 5771, 'K00097': 5120, 'K00098': 6448, 'K00099': 5771, 'K00100': 5120, 'K00101': 6448, 'K00102': 5771, 'K00103': 5120, 'K00104': 6448, 'K00105': 5771, 'K00106': 5120, 'K00107': 6448, 'K00108': 5771, 'K00109': 5120, 'K00110': 6448, 'K00111': 5771, 'K00112': 5120, 'K00113': 6448, 'K00114': 5771, 'K00115': 5120, 'K00116': 6448, 'K00117': 5771, 'K00118': 5120, 'K00119': 6448, 'K00120': 5771, 'K00121': 5120, 'K00122': 6448, 'K00123': 5771, 'K00124': 5120, 'K00125': 6448, 'K00126': 5771, 'K00127': 5120, 'K00128': 6448, 'K00129': 5771, 'K00130': 5120, 'K00131': 6448, 'K00132': 5771, 'K00133': 5120, 'K00134': 6448, 'K00135': 5771, 'K00136': 5120, 'K00137': 6448, 'K00138': 5771, 'K00139': 5120, 'K00140': 6448, 'K00141': 5771, 'K00142': 5120, 'K00143': 6448, 'K00144': 5771, 'K00145': 5120, 'K00146': 6448, 'K00147': 5771, 'K00148': 5120, 'K00149': 6448, 'K00150': 5771, 'K00151': 5120, 'K00152': 6448, 'K00153': 5771, 'K00154': 5120, 'K00155': 6448, 'K00156': 5771, 'K00157': 5120, 'K00158': 6448, 'K00159': 5771, 'K00160': 5120, 'K00161': 6448, 'K00162': 5771, 'K00163': 5120, 'K00164': 6448, 'K00165': 5771, 'K00166': 5120, 'K00167': 6448, 'K00168': 5771, 'K00169': 5120, 'K00170': 6448, 'K00171': 5771, 'K00172': 5120, 'K00173': 6448, 'K00174': 5771, 'K00175': 5120, 'K00176': 6448, 'K00177': 5771, 'K00178': 5120, 'K00179': 6448, 'K00180': 5771, 'K00181': 5120, 'K00182': 6448, 'K00183': 5771, 'K00184': 5120, 'K00185': 0, 'K00186': 0, 'K00187': 0, 'K00188': 0, 'K00189': 0, 'K00190': 0, 'K00191': 6448, 'K00192': 5771, 'K00193': 5120, 'K00194': 6448, 'K00195': 5771, 'K00196': 5120, 'K00197': 0, 'K00198': 0, 'K00199': 0, 'K00200': 0, 'K00201': 0, 'K00202': 0, 'K00203': 0, 'K00204': 0, 'K00205': 0, 'K00206': 0, 'K00207': 0, 'K00208': 0, 'K00209': 15151, 'K00210': 18983, 'K00211': 18983, 'K00212': 14139, 'K00213': 13498, 'K00214': 13279, 'K00215': 14139, 'K00216': 13498, 'K00217': 13279, 'K00218': 18982, 'K00219': 18982, 'K00220': 18982, 'K00221': 6332, 'K00222': 5647, 'K00223': 4992, 'K00224': 6332, 'K00225': 5647, 'K00226': 4992, 'K00227': 6332, 'K00228': 5647, 'K00229': 4992, 'K00230': 6332, 'K00231': 5647, 'K00232': 4992, 'K00233': 6332, 'K00234': 5647, 'K00235': 4992, 'K00236': 6332, 'K00237': 5647, 'K00238': 4992, 'K00239': 6332, 'K00240': 5647, 'K00241': 4992, 'K00242': 6332, 'K00243': 5647, 'K00244': 4992, 'K00245': 6332, 'K00246': 5647, 'K00247': 4992, 'K00248': 6332, 'K00249': 5647, 'K00250': 4992, 'K00251': 6332, 'K00252': 5647, 'K00253': 4992, 'K00254': 6332, 'K00255': 5647, 'K00256': 4992, 'K00257': 4712, 'K00258': 4029, 'K00259': 3476, 'K00260': 4712, 'K00261': 4029, 'K00262': 3476, 'K00263': 4712, 'K00264': 4029, 'K00265': 3476, 'K00266': 4712, 'K00267': 4029, 'K00268': 3476, 'K00269': 6332, 'K00270': 5647, 'K00271': 4992, 'K00272': 6332, 'K00273': 5647, 'K00274': 4992, 'K00275': 6332, 'K00276': 5647, 'K00277': 4992, 'K00278': 6332, 'K00279': 5647, 'K00280': 4992, 'K00281': 8897, 'K00282': 7808, 'K00283': 7118, 'K00284': 8897, 'K00285': 7808, 'K00286': 7118, 'K00287': 8897, 'K00288': 7808, 'K00289': 7118, 'K00290': 8897, 'K00291': 7808, 'K00292': 7118, 'K00293': 8897, 'K00294': 7808, 'K00295': 7118, 'K00296': 17040, 'K00297': 7118, 'K00298': 8897, 'K00299': 7808, 'K00300': 7118, 'K00301': 8897, 'K00302': 7808, 'K00303': 7118, 'K00304': 15857, 'K00305': 7118, 'K00306': 8897, 'K00307': 7808, 'K00308': 7118, 'K00309': 8897, 'K00310': 7808, 'K00311': 7118, 'K00312': 9009, 'K00313': 7118, 'K00314': 8897, 'K00315': 7808, 'K00316': 7118, 'K00317': 8897, 'K00318': 7808, 'K00319': 7118, 'K00320': 18113, 'K00321': 7118, 'K00322': 8897, 'K00323': 7808, 'K00324': 7118, 'K00325': 8897, 'K00326': 7808, 'K00327': 7118, 'K00328': 18859, 'K00329': 7118, 'K00330': 8897, 'K00331': 7808, 'K00332': 7118, 'K00333': 8897, 'K00334': 7808, 'K00335': 7118, 'K00336': 18983, 'K00337': 7118, 'K00338': 8897, 'K00339': 7808, 'K00340': 7118, 'K00341': 8897, 'K00342': 7808, 'K00343': 7118, 'K00344': 18974, 'K00345': 7118, 'K00346': 8897, 'K00347': 7808, 'K00348': 7118, 'K00349': 8897, 'K00350': 7808, 'K00351': 7118, 'K00352': 18983, 'K00353': 7118, 'K00354': 8897, 'K00355': 7808, 'K00356': 7118, 'K00357': 8897, 'K00358': 7808, 'K00359': 7118, 'K00360': 17380, 'K00361': 7118, 'K00362': 8897, 'K00363': 7808, 'K00364': 7118, 'K00365': 8897, 'K00366': 7808, 'K00367': 7118, 'K00368': 15608, 'K00369': 7118, 'K00370': 4712, 'K00371': 4029, 'K00372': 3476, 'K00373': 4712, 'K00374': 4029, 'K00375': 3476, 'K00376': 4712, 'K00377': 4029, 'K00378': 3476, 'K00379': 4712, 'K00380': 4029, 'K00381': 3476, 'K00382': 4712, 'K00383': 4029, 'K00384': 3476, 'K00385': 4712, 'K00386': 4029, 'K00387': 3476, 'K00388': 4712, 'K00389': 4029, 'K00390': 3476, 'K00391': 4712, 'K00392': 4029, 'K00393': 3476, 'K00394': 4712, 'K00395': 4029, 'K00396': 3476, 'K00397': 4712, 'K00398': 4029, 'K00399': 3476, 'K00400': 4712, 'K00401': 4029, 'K00402': 3476, 'K00403': 4712, 'K00404': 4029, 'K00405': 3476, 'K00406': 4712, 'K00407': 4029, 'K00408': 3476, 'K00409': 4712, 'K00410': 4029, 'K00411': 3476, 'K00412': 4712, 'K00413': 4029, 'K00414': 3476, 'K00415': 4712, 'K00416': 4029, 'K00417': 3476, 'K00418': 4712, 'K00419': 4029, 'K00420': 3476, 'K00421': 4712, 'K00422': 4029, 'K00423': 3476, 'K00424': 4712, 'K00425': 4029, 'K00426': 3476, 'K00427': 4712, 'K00428': 4029, 'K00429': 3476, 'K00430': 4712, 'K00431': 4029, 'K00432': 3476, 'K00433': 4712, 'K00434': 4029, 'K00435': 3476, 'K00436': 4712, 'K00437': 4029, 'K00438': 3476, 'K00439': 4712, 'K00440': 4029, 'K00441': 3476, 'K00442': 4712, 'K00443': 4029, 'K00444': 3476, 'K00445': 4712, 'K00446': 4029, 'K00447': 3476, 'K00448': 4712, 'K00449': 4029, 'K00450': 3476, 'K00451': 4712, 'K00452': 4029, 'K00453': 3476, 'K00454': 4712, 'K00455': 4029, 'K00456': 3476, 'K00457': 4712, 'K00458': 4029, 'K00459': 3476, 'K00460': 4712, 'K00461': 4029, 'K00462': 3476, 'K00463': 4712, 'K00464': 4029, 'K00465': 3476, 'K00466': 4712, 'K00467': 4029, 'K00468': 3476, 'K00469': 4712, 'K00470': 4029, 'K00471': 3476, 'K00472': 4712, 'K00473': 4029, 'K00474': 3476, 'K00475': 4712, 'K00476': 4029, 'K00477': 3476, 'K00478': 4712, 'K00479': 4029, 'K00480': 3476, 'K00481': 4712, 'K00482': 4029, 'K00483': 3476, 'K00484': 8886, 'K00485': 7916, 'K00486': 7213, 'K00487': 8886, 'K00488': 7916, 'K00489': 7213, 'K00490': 8886, 'K00491': 7916, 'K00492': 7213, 'K00493': 8886, 'K00494': 7916, 'K00495': 7213, 'K00496': 8886, 'K00497': 7916, 'K00498': 7213, 'K00499': 13984, 'K00500': 7213, 'K00501': 8886, 'K00502': 7916, 'K00503': 7213, 'K00504': 8886, 'K00505': 7916, 'K00506': 7213, 'K00507': 18423, 'K00508': 7213, 'K00509': 8886, 'K00510': 7916, 'K00511': 7213, 'K00512': 8886, 'K00513': 7916, 'K00514': 7213, 'K00515': 18945, 'K00516': 7213, 'K00517': 8886, 'K00518': 7916, 'K00519': 7213, 'K00520': 8886, 'K00521': 7916, 'K00522': 7213, 'K00523': 16594, 'K00524': 7213, 'K00525': 8886, 'K00526': 7916, 'K00527': 7213, 'K00528': 8886, 'K00529': 7916, 'K00530': 7213, 'K00531': 8991, 'K00532': 7213, 'K00533': 8886, 'K00534': 7916, 'K00535': 7213, 'K00536': 8886, 'K00537': 7916, 'K00538': 7213, 'K00539': 13482, 'K00540': 7213}
Count occurrences of sum of NaN and zero in columns
{'K00001': 4143, 'K00002': 4177, 'K00003': 4143, 'K00004': 4143, 'K00005': 18845, 'K00006': 18845, 'K00007': 12998, 'K00008': 12998, 'K00009': 12998, 'K00010': 12998, 'K00011': 18983, 'K00012': 18983, 'K00013': 10502, 'K00014': 10644, 'K00015': 10502, 'K00016': 10513, 'K00017': 17434, 'K00018': 17434, 'K00019': 10844, 'K00020': 10844, 'K00021': 10844, 'K00022': 10844, 'K00023': 18983, 'K00024': 18983, 'K00025': 18983, 'K00026': 2701, 'K00027': 6495, 'K00028': 12783, 'K00029': 15114, 'K00030': 13568, 'K00031': 13692, 'K00032': 13562, 'K00033': 13565, 'K00034': 17827, 'K00035': 17835, 'K00036': 12743, 'K00037': 13264, 'K00038': 12733, 'K00039': 12756, 'K00040': 13763, 'K00041': 13897, 'K00042': 12462, 'K00043': 13191, 'K00044': 12451, 'K00045': 12552, 'K00046': 12972, 'K00047': 13062, 'K00048': 15374, 'K00049': 15630, 'K00050': 15374, 'K00051': 15401, 'K00052': 18100, 'K00053': 18100, 'K00054': 14620, 'K00055': 15427, 'K00056': 14620, 'K00057': 14655, 'K00058': 15509, 'K00059': 15509, 'K00060': 14362, 'K00061': 15491, 'K00062': 14362, 'K00063': 14601, 'K00064': 14760, 'K00065': 14760, 'K00066': 15374, 'K00067': 15374, 'K00068': 15374, 'K00069': 15374, 'K00070': 18245, 'K00071': 18249, 'K00072': 14620, 'K00073': 14620, 'K00074': 14620, 'K00075': 14620, 'K00076': 16086, 'K00077': 16144, 'K00078': 14362, 'K00079': 14362, 'K00080': 14362, 'K00081': 14362, 'K00082': 15273, 'K00083': 15305, 'K00084': 14272, 'K00085': 7158, 'K00086': 6418, 'K00087': 5731, 'K00088': 17513, 'K00089': 18243, 'K00090': 18764, 'K00091': 18935, 'K00092': 7264, 'K00093': 6373, 'K00094': 5592, 'K00095': 7260, 'K00096': 6367, 'K00097': 5591, 'K00098': 6689, 'K00099': 5972, 'K00100': 5283, 'K00101': 6689, 'K00102': 5972, 'K00103': 5283, 'K00104': 7471, 'K00105': 6533, 'K00106': 5731, 'K00107': 7466, 'K00108': 6525, 'K00109': 5729, 'K00110': 6743, 'K00111': 6012, 'K00112': 5334, 'K00113': 6743, 'K00114': 6012, 'K00115': 5334, 'K00116': 7680, 'K00117': 6718, 'K00118': 5895, 'K00119': 7680, 'K00120': 6718, 'K00121': 5895, 'K00122': 6790, 'K00123': 6042, 'K00124': 5356, 'K00125': 6790, 'K00126': 6042, 'K00127': 5356, 'K00128': 17099, 'K00129': 16254, 'K00130': 15686, 'K00131': 17086, 'K00132': 16236, 'K00133': 15669, 'K00134': 17160, 'K00135': 16785, 'K00136': 16382, 'K00137': 17159, 'K00138': 16784, 'K00139': 16382, 'K00140': 7311, 'K00141': 6591, 'K00142': 5901, 'K00143': 17405, 'K00144': 16877, 'K00145': 16562, 'K00146': 17405, 'K00147': 16877, 'K00148': 16562, 'K00149': 17496, 'K00150': 16783, 'K00151': 16118, 'K00152': 17496, 'K00153': 16783, 'K00154': 16118, 'K00155': 8892, 'K00156': 7782, 'K00157': 6924, 'K00158': 8892, 'K00159': 7782, 'K00160': 6924, 'K00161': 8653, 'K00162': 7610, 'K00163': 6791, 'K00164': 8653, 'K00165': 7610, 'K00166': 6791, 'K00167': 15277, 'K00168': 14458, 'K00169': 14048, 'K00170': 15277, 'K00171': 14458, 'K00172': 14048, 'K00173': 14414, 'K00174': 13727, 'K00175': 13022, 'K00176': 14414, 'K00177': 13727, 'K00178': 13022, 'K00179': 8721, 'K00180': 7664, 'K00181': 6778, 'K00182': 8721, 'K00183': 7664, 'K00184': 6778, 'K00185': 18983, 'K00186': 18983, 'K00187': 18983, 'K00188': 18983, 'K00189': 18983, 'K00190': 18983, 'K00191': 17981, 'K00192': 17707, 'K00193': 17550, 'K00194': 17981, 'K00195': 17707, 'K00196': 17550, 'K00197': 18983, 'K00198': 18983, 'K00199': 18983, 'K00200': 18983, 'K00201': 18983, 'K00202': 18983, 'K00203': 18983, 'K00204': 18983, 'K00205': 18983, 'K00206': 18983, 'K00207': 18983, 'K00208': 18983, 'K00209': 16777, 'K00210': 18983, 'K00211': 18983, 'K00212': 17901, 'K00213': 17546, 'K00214': 17329, 'K00215': 17901, 'K00216': 17546, 'K00217': 17329, 'K00218': 18982, 'K00219': 18982, 'K00220': 18982, 'K00221': 18977, 'K00222': 18969, 'K00223': 18964, 'K00224': 18977, 'K00225': 18969, 'K00226': 18964, 'K00227': 18890, 'K00228': 18869, 'K00229': 18859, 'K00230': 18890, 'K00231': 18869, 'K00232': 18859, 'K00233': 18925, 'K00234': 18886, 'K00235': 18858, 'K00236': 18925, 'K00237': 18886, 'K00238': 18858, 'K00239': 17248, 'K00240': 16450, 'K00241': 16235, 'K00242': 17247, 'K00243': 16449, 'K00244': 16234, 'K00245': 18592, 'K00246': 18231, 'K00247': 18144, 'K00248': 18590, 'K00249': 18229, 'K00250': 18142, 'K00251': 18807, 'K00252': 18730, 'K00253': 18672, 'K00254': 18807, 'K00255': 18730, 'K00256': 18672, 'K00257': 17120, 'K00258': 16055, 'K00259': 15394, 'K00260': 17120, 'K00261': 16055, 'K00262': 15394, 'K00263': 16537, 'K00264': 15719, 'K00265': 15376, 'K00266': 16534, 'K00267': 15717, 'K00268': 15374, 'K00269': 18880, 'K00270': 18803, 'K00271': 18724, 'K00272': 18880, 'K00273': 18803, 'K00274': 18724, 'K00275': 18929, 'K00276': 18917, 'K00277': 18822, 'K00278': 18929, 'K00279': 18917, 'K00280': 18822, 'K00281': 8906, 'K00282': 7820, 'K00283': 7124, 'K00284': 8897, 'K00285': 7808, 'K00286': 7118, 'K00287': 8897, 'K00288': 7808, 'K00289': 7118, 'K00290': 17486, 'K00291': 17247, 'K00292': 17040, 'K00293': 17486, 'K00294': 17247, 'K00295': 17040, 'K00296': 17040, 'K00297': 17040, 'K00298': 16754, 'K00299': 16251, 'K00300': 15857, 'K00301': 16754, 'K00302': 16251, 'K00303': 15857, 'K00304': 15857, 'K00305': 15857, 'K00306': 10959, 'K00307': 9605, 'K00308': 9009, 'K00309': 10959, 'K00310': 9605, 'K00311': 9009, 'K00312': 9009, 'K00313': 9009, 'K00314': 18541, 'K00315': 18309, 'K00316': 18113, 'K00317': 18541, 'K00318': 18309, 'K00319': 18113, 'K00320': 18113, 'K00321': 18113, 'K00322': 18890, 'K00323': 18869, 'K00324': 18859, 'K00325': 18890, 'K00326': 18869, 'K00327': 18859, 'K00328': 18859, 'K00329': 18859, 'K00330': 18983, 'K00331': 18983, 'K00332': 18983, 'K00333': 18983, 'K00334': 18983, 'K00335': 18983, 'K00336': 18983, 'K00337': 18983, 'K00338': 18978, 'K00339': 18976, 'K00340': 18974, 'K00341': 18978, 'K00342': 18976, 'K00343': 18974, 'K00344': 18974, 'K00345': 18974, 'K00346': 18983, 'K00347': 18983, 'K00348': 18983, 'K00349': 18983, 'K00350': 18983, 'K00351': 18983, 'K00352': 18983, 'K00353': 18983, 'K00354': 18060, 'K00355': 17676, 'K00356': 17380, 'K00357': 18060, 'K00358': 17676, 'K00359': 17380, 'K00360': 17380, 'K00361': 17380, 'K00362': 16791, 'K00363': 16115, 'K00364': 15624, 'K00365': 16779, 'K00366': 16099, 'K00367': 15608, 'K00368': 15608, 'K00369': 15625, 'K00370': 18969, 'K00371': 18957, 'K00372': 18947, 'K00373': 18969, 'K00374': 18957, 'K00375': 18947, 'K00376': 18441, 'K00377': 18074, 'K00378': 17833, 'K00379': 18441, 'K00380': 18074, 'K00381': 17833, 'K00382': 18048, 'K00383': 17497, 'K00384': 17135, 'K00385': 18048, 'K00386': 17497, 'K00387': 17135, 'K00388': 18824, 'K00389': 18696, 'K00390': 18580, 'K00391': 18824, 'K00392': 18696, 'K00393': 18580, 'K00394': 15763, 'K00395': 14369, 'K00396': 13530, 'K00397': 15763, 'K00398': 14369, 'K00399': 13530, 'K00400': 18798, 'K00401': 18726, 'K00402': 18656, 'K00403': 18798, 'K00404': 18726, 'K00405': 18656, 'K00406': 18235, 'K00407': 17919, 'K00408': 17678, 'K00409': 18235, 'K00410': 17919, 'K00411': 17678, 'K00412': 18644, 'K00413': 18342, 'K00414': 18146, 'K00415': 18644, 'K00416': 18342, 'K00417': 18146, 'K00418': 18845, 'K00419': 18778, 'K00420': 18660, 'K00421': 18845, 'K00422': 18778, 'K00423': 18660, 'K00424': 18965, 'K00425': 18956, 'K00426': 18950, 'K00427': 18965, 'K00428': 18956, 'K00429': 18950, 'K00430': 18904, 'K00431': 18856, 'K00432': 18813, 'K00433': 18904, 'K00434': 18856, 'K00435': 18813, 'K00436': 18785, 'K00437': 18656, 'K00438': 18552, 'K00439': 18785, 'K00440': 18656, 'K00441': 18552, 'K00442': 18600, 'K00443': 18356, 'K00444': 18135, 'K00445': 18600, 'K00446': 18356, 'K00447': 18135, 'K00448': 18924, 'K00449': 18894, 'K00450': 18874, 'K00451': 18924, 'K00452': 18894, 'K00453': 18874, 'K00454': 15644, 'K00455': 14441, 'K00456': 13682, 'K00457': 15644, 'K00458': 14441, 'K00459': 13682, 'K00460': 18659, 'K00461': 18555, 'K00462': 18416, 'K00463': 18658, 'K00464': 18554, 'K00465': 18416, 'K00466': 18833, 'K00467': 18769, 'K00468': 18742, 'K00469': 18833, 'K00470': 18769, 'K00471': 18742, 'K00472': 18967, 'K00473': 18957, 'K00474': 18953, 'K00475': 18967, 'K00476': 18957, 'K00477': 18953, 'K00478': 18063, 'K00479': 17742, 'K00480': 17578, 'K00481': 18008, 'K00482': 17742, 'K00483': 17578, 'K00484': 8886, 'K00485': 7916, 'K00486': 7213, 'K00487': 8886, 'K00488': 7916, 'K00489': 7213, 'K00490': 8886, 'K00491': 7916, 'K00492': 7213, 'K00493': 15224, 'K00494': 14398, 'K00495': 13984, 'K00496': 15224, 'K00497': 14398, 'K00498': 13984, 'K00499': 13984, 'K00500': 13984, 'K00501': 18664, 'K00502': 18562, 'K00503': 18423, 'K00504': 18664, 'K00505': 18562, 'K00506': 18423, 'K00507': 18423, 'K00508': 18423, 'K00509': 18957, 'K00510': 18950, 'K00511': 18945, 'K00512': 18957, 'K00513': 18950, 'K00514': 18945, 'K00515': 18945, 'K00516': 18945, 'K00517': 17341, 'K00518': 16986, 'K00519': 16594, 'K00520': 17341, 'K00521': 16986, 'K00522': 16594, 'K00523': 16594, 'K00524': 16594, 'K00525': 11072, 'K00526': 9557, 'K00527': 8991, 'K00528': 11072, 'K00529': 9557, 'K00530': 8991, 'K00531': 8991, 'K00532': 8991, 'K00533': 15563, 'K00534': 14317, 'K00535': 13482, 'K00536': 15563, 'K00537': 14317, 'K00538': 13482, 'K00539': 13482, 'K00540': 13482}
# Visualização de contagens de zeros, NaN e soma de ambas as contagens no dataframe
# Visualization of counts of zeros, NaN and sum of both counts in the dataframe
columns_to_count = df.columns[2:542]
column_labels = columns_to_count.tolist()
count_of_zeros = (df[columns_to_count] == 0).sum().astype(int)
count_of_nans = df[columns_to_count].isna().sum().astype(int)
count_of_zeros_and_nans = (count_of_zeros + count_of_nans).astype(int)
# Create the dataframe
data = pd.DataFrame({
'Columns': column_labels,
'Count of 0': count_of_zeros,
'Count of NaN': count_of_nans,
'Count of 0 + NaN': count_of_zeros_and_nans
})
# Create subplots with three vertical bars
fig = make_subplots(rows=3, cols=1, subplot_titles=('Count of 0', 'Count of NaN', 'Count of 0 + NaN'))
fig.add_trace(go.Bar(
x=data['Columns'],
y=data['Count of 0'],
name='Count of 0',
), row=1, col=1)
fig.add_trace(go.Bar(
x=data['Columns'],
y=data['Count of NaN'],
name='Count of NaN',
), row=2, col=1)
fig.add_trace(go.Bar(
x=data['Columns'],
y=data['Count of 0 + NaN'],
name='Count of 0 + NaN',
), row=3, col=1)
# Set y-axis range to 0 to number of rows in df for each subplot
fig.update_yaxes(range=[0, df.shape[0]], row=1, col=1)
fig.update_yaxes(range=[0, df.shape[0]], row=2, col=1)
fig.update_yaxes(range=[0, df.shape[0]], row=3, col=1)
fig.update_layout(
title='Counts of 0, NaN, and 0 + NaN occurrences in columns',
xaxis_title='Columns',
showlegend=False,
height=800,
width=8000
)
fig.show()
# fig to hmtl
pyo.plot(fig, filename=f'{files_directory}/MBA001_Counts_of_0_NaN_and_0_NaN_occurrences_in_columns.html', auto_open=False)
'C:/Temp/Codes/MBA/MBA001_Counts_of_0_NaN_and_0_NaN_occurrences_in_columns.html'
# Count the columns where 'Count of 0 + NaN' == 18983 (total number of rows)
zero_nan_count_columns = data[data['Count of 0 + NaN'] == 18983].shape[0]
print('Number of columns with 18983 zeros and NaNs: {}'.format(zero_nan_count_columns))
# Count the columns where 'Count of 0' == 18983 (total number of rows)
zero_count_columns = data[data['Count of 0'] == 18983].shape[0]
print('Number of columns with 18983 zeros: {}'.format(zero_count_columns))
# Count the columns where 'Count of NaN' == 18983 (total number of rows)
nan_count_columns = data[data['Count of NaN'] == 18983].shape[0]
print('Number of columns with 18983 NaN: {}'.format(nan_count_columns))
# Create a dictionary with index as keys and 'Count of 0 + NaN' counts as values
result_dict_count_zero_nan = {index: count for index, count in zip(data.index, data['Count of 0 + NaN'])}
result_dict_count_zero = {index: count for index, count in zip(data.index, data['Count of 0'])}
result_dict_count_nan = {index: count for index, count in zip(data.index, data['Count of NaN'])}
def write_dict_to_file(data_dict, filename):
with open(filename, 'w') as f:
for key, value in data_dict.items():
f.write(f"{key}: {value}\n")
# Write dictionaries to files
write_dict_to_file(result_dict_count_zero_nan, 'result_dict_count_zero_nan.txt')
write_dict_to_file(result_dict_count_zero, 'result_dict_count_zero.txt')
write_dict_to_file(result_dict_count_nan, 'result_dict_count_nan.txt')
print('Dictionary of counts of zeros and NaNs:', result_dict_count_zero_nan)
Number of columns with 18983 zeros and NaNs: 41
Number of columns with 18983 zeros: 18
Number of columns with 18983 NaN: 4
Dictionary of counts of zeros and NaNs: {'K00001': 4143, 'K00002': 4177, 'K00003': 4143, 'K00004': 4143, 'K00005': 18845, 'K00006': 18845, 'K00007': 12998, 'K00008': 12998, 'K00009': 12998, 'K00010': 12998, 'K00011': 18983, 'K00012': 18983, 'K00013': 10502, 'K00014': 10644, 'K00015': 10502, 'K00016': 10513, 'K00017': 17434, 'K00018': 17434, 'K00019': 10844, 'K00020': 10844, 'K00021': 10844, 'K00022': 10844, 'K00023': 18983, 'K00024': 18983, 'K00025': 18983, 'K00026': 2701, 'K00027': 6495, 'K00028': 12783, 'K00029': 15114, 'K00030': 13568, 'K00031': 13692, 'K00032': 13562, 'K00033': 13565, 'K00034': 17827, 'K00035': 17835, 'K00036': 12743, 'K00037': 13264, 'K00038': 12733, 'K00039': 12756, 'K00040': 13763, 'K00041': 13897, 'K00042': 12462, 'K00043': 13191, 'K00044': 12451, 'K00045': 12552, 'K00046': 12972, 'K00047': 13062, 'K00048': 15374, 'K00049': 15630, 'K00050': 15374, 'K00051': 15401, 'K00052': 18100, 'K00053': 18100, 'K00054': 14620, 'K00055': 15427, 'K00056': 14620, 'K00057': 14655, 'K00058': 15509, 'K00059': 15509, 'K00060': 14362, 'K00061': 15491, 'K00062': 14362, 'K00063': 14601, 'K00064': 14760, 'K00065': 14760, 'K00066': 15374, 'K00067': 15374, 'K00068': 15374, 'K00069': 15374, 'K00070': 18245, 'K00071': 18249, 'K00072': 14620, 'K00073': 14620, 'K00074': 14620, 'K00075': 14620, 'K00076': 16086, 'K00077': 16144, 'K00078': 14362, 'K00079': 14362, 'K00080': 14362, 'K00081': 14362, 'K00082': 15273, 'K00083': 15305, 'K00084': 14272, 'K00085': 7158, 'K00086': 6418, 'K00087': 5731, 'K00088': 17513, 'K00089': 18243, 'K00090': 18764, 'K00091': 18935, 'K00092': 7264, 'K00093': 6373, 'K00094': 5592, 'K00095': 7260, 'K00096': 6367, 'K00097': 5591, 'K00098': 6689, 'K00099': 5972, 'K00100': 5283, 'K00101': 6689, 'K00102': 5972, 'K00103': 5283, 'K00104': 7471, 'K00105': 6533, 'K00106': 5731, 'K00107': 7466, 'K00108': 6525, 'K00109': 5729, 'K00110': 6743, 'K00111': 6012, 'K00112': 5334, 'K00113': 6743, 'K00114': 6012, 'K00115': 5334, 'K00116': 7680, 'K00117': 6718, 'K00118': 5895, 'K00119': 7680, 'K00120': 6718, 'K00121': 5895, 'K00122': 6790, 'K00123': 6042, 'K00124': 5356, 'K00125': 6790, 'K00126': 6042, 'K00127': 5356, 'K00128': 17099, 'K00129': 16254, 'K00130': 15686, 'K00131': 17086, 'K00132': 16236, 'K00133': 15669, 'K00134': 17160, 'K00135': 16785, 'K00136': 16382, 'K00137': 17159, 'K00138': 16784, 'K00139': 16382, 'K00140': 7311, 'K00141': 6591, 'K00142': 5901, 'K00143': 17405, 'K00144': 16877, 'K00145': 16562, 'K00146': 17405, 'K00147': 16877, 'K00148': 16562, 'K00149': 17496, 'K00150': 16783, 'K00151': 16118, 'K00152': 17496, 'K00153': 16783, 'K00154': 16118, 'K00155': 8892, 'K00156': 7782, 'K00157': 6924, 'K00158': 8892, 'K00159': 7782, 'K00160': 6924, 'K00161': 8653, 'K00162': 7610, 'K00163': 6791, 'K00164': 8653, 'K00165': 7610, 'K00166': 6791, 'K00167': 15277, 'K00168': 14458, 'K00169': 14048, 'K00170': 15277, 'K00171': 14458, 'K00172': 14048, 'K00173': 14414, 'K00174': 13727, 'K00175': 13022, 'K00176': 14414, 'K00177': 13727, 'K00178': 13022, 'K00179': 8721, 'K00180': 7664, 'K00181': 6778, 'K00182': 8721, 'K00183': 7664, 'K00184': 6778, 'K00185': 18983, 'K00186': 18983, 'K00187': 18983, 'K00188': 18983, 'K00189': 18983, 'K00190': 18983, 'K00191': 17981, 'K00192': 17707, 'K00193': 17550, 'K00194': 17981, 'K00195': 17707, 'K00196': 17550, 'K00197': 18983, 'K00198': 18983, 'K00199': 18983, 'K00200': 18983, 'K00201': 18983, 'K00202': 18983, 'K00203': 18983, 'K00204': 18983, 'K00205': 18983, 'K00206': 18983, 'K00207': 18983, 'K00208': 18983, 'K00209': 16777, 'K00210': 18983, 'K00211': 18983, 'K00212': 17901, 'K00213': 17546, 'K00214': 17329, 'K00215': 17901, 'K00216': 17546, 'K00217': 17329, 'K00218': 18982, 'K00219': 18982, 'K00220': 18982, 'K00221': 18977, 'K00222': 18969, 'K00223': 18964, 'K00224': 18977, 'K00225': 18969, 'K00226': 18964, 'K00227': 18890, 'K00228': 18869, 'K00229': 18859, 'K00230': 18890, 'K00231': 18869, 'K00232': 18859, 'K00233': 18925, 'K00234': 18886, 'K00235': 18858, 'K00236': 18925, 'K00237': 18886, 'K00238': 18858, 'K00239': 17248, 'K00240': 16450, 'K00241': 16235, 'K00242': 17247, 'K00243': 16449, 'K00244': 16234, 'K00245': 18592, 'K00246': 18231, 'K00247': 18144, 'K00248': 18590, 'K00249': 18229, 'K00250': 18142, 'K00251': 18807, 'K00252': 18730, 'K00253': 18672, 'K00254': 18807, 'K00255': 18730, 'K00256': 18672, 'K00257': 17120, 'K00258': 16055, 'K00259': 15394, 'K00260': 17120, 'K00261': 16055, 'K00262': 15394, 'K00263': 16537, 'K00264': 15719, 'K00265': 15376, 'K00266': 16534, 'K00267': 15717, 'K00268': 15374, 'K00269': 18880, 'K00270': 18803, 'K00271': 18724, 'K00272': 18880, 'K00273': 18803, 'K00274': 18724, 'K00275': 18929, 'K00276': 18917, 'K00277': 18822, 'K00278': 18929, 'K00279': 18917, 'K00280': 18822, 'K00281': 8906, 'K00282': 7820, 'K00283': 7124, 'K00284': 8897, 'K00285': 7808, 'K00286': 7118, 'K00287': 8897, 'K00288': 7808, 'K00289': 7118, 'K00290': 17486, 'K00291': 17247, 'K00292': 17040, 'K00293': 17486, 'K00294': 17247, 'K00295': 17040, 'K00296': 17040, 'K00297': 17040, 'K00298': 16754, 'K00299': 16251, 'K00300': 15857, 'K00301': 16754, 'K00302': 16251, 'K00303': 15857, 'K00304': 15857, 'K00305': 15857, 'K00306': 10959, 'K00307': 9605, 'K00308': 9009, 'K00309': 10959, 'K00310': 9605, 'K00311': 9009, 'K00312': 9009, 'K00313': 9009, 'K00314': 18541, 'K00315': 18309, 'K00316': 18113, 'K00317': 18541, 'K00318': 18309, 'K00319': 18113, 'K00320': 18113, 'K00321': 18113, 'K00322': 18890, 'K00323': 18869, 'K00324': 18859, 'K00325': 18890, 'K00326': 18869, 'K00327': 18859, 'K00328': 18859, 'K00329': 18859, 'K00330': 18983, 'K00331': 18983, 'K00332': 18983, 'K00333': 18983, 'K00334': 18983, 'K00335': 18983, 'K00336': 18983, 'K00337': 18983, 'K00338': 18978, 'K00339': 18976, 'K00340': 18974, 'K00341': 18978, 'K00342': 18976, 'K00343': 18974, 'K00344': 18974, 'K00345': 18974, 'K00346': 18983, 'K00347': 18983, 'K00348': 18983, 'K00349': 18983, 'K00350': 18983, 'K00351': 18983, 'K00352': 18983, 'K00353': 18983, 'K00354': 18060, 'K00355': 17676, 'K00356': 17380, 'K00357': 18060, 'K00358': 17676, 'K00359': 17380, 'K00360': 17380, 'K00361': 17380, 'K00362': 16791, 'K00363': 16115, 'K00364': 15624, 'K00365': 16779, 'K00366': 16099, 'K00367': 15608, 'K00368': 15608, 'K00369': 15625, 'K00370': 18969, 'K00371': 18957, 'K00372': 18947, 'K00373': 18969, 'K00374': 18957, 'K00375': 18947, 'K00376': 18441, 'K00377': 18074, 'K00378': 17833, 'K00379': 18441, 'K00380': 18074, 'K00381': 17833, 'K00382': 18048, 'K00383': 17497, 'K00384': 17135, 'K00385': 18048, 'K00386': 17497, 'K00387': 17135, 'K00388': 18824, 'K00389': 18696, 'K00390': 18580, 'K00391': 18824, 'K00392': 18696, 'K00393': 18580, 'K00394': 15763, 'K00395': 14369, 'K00396': 13530, 'K00397': 15763, 'K00398': 14369, 'K00399': 13530, 'K00400': 18798, 'K00401': 18726, 'K00402': 18656, 'K00403': 18798, 'K00404': 18726, 'K00405': 18656, 'K00406': 18235, 'K00407': 17919, 'K00408': 17678, 'K00409': 18235, 'K00410': 17919, 'K00411': 17678, 'K00412': 18644, 'K00413': 18342, 'K00414': 18146, 'K00415': 18644, 'K00416': 18342, 'K00417': 18146, 'K00418': 18845, 'K00419': 18778, 'K00420': 18660, 'K00421': 18845, 'K00422': 18778, 'K00423': 18660, 'K00424': 18965, 'K00425': 18956, 'K00426': 18950, 'K00427': 18965, 'K00428': 18956, 'K00429': 18950, 'K00430': 18904, 'K00431': 18856, 'K00432': 18813, 'K00433': 18904, 'K00434': 18856, 'K00435': 18813, 'K00436': 18785, 'K00437': 18656, 'K00438': 18552, 'K00439': 18785, 'K00440': 18656, 'K00441': 18552, 'K00442': 18600, 'K00443': 18356, 'K00444': 18135, 'K00445': 18600, 'K00446': 18356, 'K00447': 18135, 'K00448': 18924, 'K00449': 18894, 'K00450': 18874, 'K00451': 18924, 'K00452': 18894, 'K00453': 18874, 'K00454': 15644, 'K00455': 14441, 'K00456': 13682, 'K00457': 15644, 'K00458': 14441, 'K00459': 13682, 'K00460': 18659, 'K00461': 18555, 'K00462': 18416, 'K00463': 18658, 'K00464': 18554, 'K00465': 18416, 'K00466': 18833, 'K00467': 18769, 'K00468': 18742, 'K00469': 18833, 'K00470': 18769, 'K00471': 18742, 'K00472': 18967, 'K00473': 18957, 'K00474': 18953, 'K00475': 18967, 'K00476': 18957, 'K00477': 18953, 'K00478': 18063, 'K00479': 17742, 'K00480': 17578, 'K00481': 18008, 'K00482': 17742, 'K00483': 17578, 'K00484': 8886, 'K00485': 7916, 'K00486': 7213, 'K00487': 8886, 'K00488': 7916, 'K00489': 7213, 'K00490': 8886, 'K00491': 7916, 'K00492': 7213, 'K00493': 15224, 'K00494': 14398, 'K00495': 13984, 'K00496': 15224, 'K00497': 14398, 'K00498': 13984, 'K00499': 13984, 'K00500': 13984, 'K00501': 18664, 'K00502': 18562, 'K00503': 18423, 'K00504': 18664, 'K00505': 18562, 'K00506': 18423, 'K00507': 18423, 'K00508': 18423, 'K00509': 18957, 'K00510': 18950, 'K00511': 18945, 'K00512': 18957, 'K00513': 18950, 'K00514': 18945, 'K00515': 18945, 'K00516': 18945, 'K00517': 17341, 'K00518': 16986, 'K00519': 16594, 'K00520': 17341, 'K00521': 16986, 'K00522': 16594, 'K00523': 16594, 'K00524': 16594, 'K00525': 11072, 'K00526': 9557, 'K00527': 8991, 'K00528': 11072, 'K00529': 9557, 'K00530': 8991, 'K00531': 8991, 'K00532': 8991, 'K00533': 15563, 'K00534': 14317, 'K00535': 13482, 'K00536': 15563, 'K00537': 14317, 'K00538': 13482, 'K00539': 13482, 'K00540': 13482}
- As variáveis com 100% de valores zeros ou 100% NaN, podem ser descartadas. Essas colunas não agregam valor ou informação ao modelo.
- Para colunas com 18983 zeros e NaNs altos, é necessário investigar para decidir se deve ser descartado.
- The columns with 100% counts of zeros or 100% NaN, could be dropped. Those columns do not add value or information to the model.
- For columns with 18983 zeros and NaNs high counts, is necessary to investigate to decide if it should be dropped.
# Filter columns with more than 99% of zeros and NaNs
zero_nan_count_great99_columns = data[data['Count of 0 + NaN'] > (18983 * 0.99)].shape[0]
# Create a new dictionary with the count
result_dict_count_zero_nan_great99 = {
'Number of columns with more than 99% of zeros and NaNs': zero_nan_count_great99_columns
}
write_dict_to_file(result_dict_count_zero_nan_great99, 'zero_nan_count_great99.txt')
# Print the number of columns with more than 99% of zeros and NaNs
print('Number of columns with more than 99% of zeros and NaNs: {}'.format(zero_nan_count_great99_columns))
Number of columns with more than 99% of zeros and NaNs: 139
# Separate rows based on the 'label' column value
df_label_1 = df[df['label'] == 1]
df_label_0 = df[df['label'] == 0]
# Calculate the percentage of missing values for each group and describe the statistics
missing_percent_label_1 = (df_label_1.loc[:, 'K00001':'K00540'].isna().sum(axis=1) / 5.40).describe()
missing_percent_label_0 = (df_label_0.loc[:, 'K00001':'K00540'].isna().sum(axis=1) / 5.40).describe()
print("Missing value percentages for 'label' == 1:")
display(round(missing_percent_label_1, 1))
print("\nMissing value percentages for 'label' == 0:")
display(round(missing_percent_label_0, 1))
# Calculate the difference between 'label' == 0 (The Good) and 'label' == 1 (The Bad) by user_id
result = df.groupby('label').apply(lambda group: (group.loc[:, 'K00001':'K00540'].isna().sum(axis=1)).describe().round(1))
diff_df = result.loc[1] - result.loc[0]
diff_df.name = 'Difference (%)'
result = pd.concat([result, diff_df.to_frame().T])
# Remove the 'count' row
result = result.drop(columns='count')
# Create a horizontal box plot using Plotly
fig = px.box(result.T, orientation='h', title='Difference between Classes by user_id in counts of NaN')
fig.show()
Missing value percentages for 'label' == 1:
count 1000.0 mean 51.7 std 34.1 min 3.9 25% 18.9 50% 40.9 75% 93.7 max 96.3 dtype: float64
Missing value percentages for 'label' == 0:
count 17983.0 mean 37.6 std 31.1 min 2.8 25% 16.5 50% 19.6 75% 59.4 max 96.3 dtype: float64
- É notável que a distribuição de valores em uma classe exibe variações significativas em relação aos valores NaN.
* MNAR Significa que os dados faltantes estão relacionados sistematicamente aos dados não observados, ou seja, a falta de dados está relacionada a eventos ou fatores que não são medidos, ou seja, erro humano.
- It is noteworthy that the distribution of values in one class exhibits significant variations in relation to NaN values.
* Missing not at random (MNAR). Data are missing is systematically related to the unobserved data, that is, the missingness is related to events or factors which are not measured, i.e. Human error.
# Calculate the count of zeros per row
count_of_zeros = (df.loc[:, 'K00001':'K00540'] == 0).sum(axis=1)
# Create a horizontal box plot
fig = go.Figure()
# Add box plot for label == 0
fig.add_trace(go.Box(
x=count_of_zeros[df['label'] == 0],
orientation='h',
name='Count of Zeros (label 0)',
))
# Add box plot for label == 1
fig.add_trace(go.Box(
x=count_of_zeros[df['label'] == 1],
orientation='h',
name='Count of Zeros (label 1)',
))
fig.update_layout(
title='Count of Zeros per Row by Label',
xaxis_title='Count of Zeros',
yaxis_title='Label',
)
fig.show()
- A distribuição de valores zero exibe variações significativas entre as classes.
- The distribution of zero values counts in one class exhibits significant variations.
# Convert the 'bank' values to strings
df['bank'] = df['bank'].astype(str)
# Calculate the count of NaN values for each row
df['count_of_nan'] = df.drop(columns=['bank', 'label']).isna().sum(axis=1)
# Calculate the count of zero values for each row
df['count_of_zero'] = (df.drop(columns=['bank', 'label']) == 0).sum(axis=1)
# Calculate the count of NaN + zero values for each row
df['count_of_nan_zero'] = df['count_of_nan'] + df['count_of_zero']
# Create subplots for each unique 'bank' value
fig = make_subplots(rows=1, cols=len(df['bank'].unique()), subplot_titles=df['bank'].unique())
for i, bank_value in enumerate(df['bank'].unique(), 1):
filtered_df = df[df['bank'] == bank_value]
fig.add_trace(go.Box(
x=filtered_df['count_of_nan'],
y=filtered_df['label'],
orientation='h',
name=f'{bank_value} - Count of NaN',
), row=1, col=i)
fig.add_trace(go.Box(
x=filtered_df['count_of_zero'],
y=filtered_df['label'],
orientation='h',
name=f'{bank_value} - Count of Zero',
), row=1, col=i)
'''
fig.add_trace(go.Box(
x=filtered_df['count_of_nan_zero'],
y=filtered_df['label'],
orientation='h',
name=f'{bank_value} - Count of NaN + Zero',
), row=1, col=i)
'''
# Update layout for the whole figure
fig.update_layout(
title='Horizontal Box Plots of Count of NaN, Zero, for Each Bank',# and NaN + Zero for Each Bank',
yaxis_title='Class',
height=400, # Adjust the height
width=6000, # Adjust the width
)
# Update subplot titles
for i, bank_value in enumerate(df['bank'].unique(), 1):
fig.update_xaxes(title_text='Count', row=1, col=i)
fig.update_yaxes(title_text='Class', row=1, col=i)
fig.show()
- The differences in counts of zeros and NaN values are more evident for some banks, like bank: 0, 7, 12, 13, 14
# Convert the 'bank' values to strings
df['bank'] = df['bank'].astype(str)
# Filter the DataFrame to only include rows where 'bank' is equal to 1
df_bank_1 = df[df['bank'] == '0']
# Calculate the count of NaN values for each row
df_bank_1['count_of_nan'] = df_bank_1.drop(columns=['bank', 'label']).isna().sum(axis=1)
# Calculate the count of zero values for each row
df_bank_1['count_of_zero'] = (df_bank_1.drop(columns=['bank', 'label']) == 0).sum(axis=1)
# Calculate the count of NaN + zero values for each row
df_bank_1['count_of_nan_zero'] = df_bank_1['count_of_nan'] + df_bank_1['count_of_zero']
# Create subplots for bank 0
fig = make_subplots(rows=1, cols=1)
fig.add_trace(go.Box(
x=df_bank_1['count_of_nan'],
y=df_bank_1['label'],
orientation='h', # Horizontal box plot
name='Count of NaN',
))
fig.add_trace(go.Box(
x=df_bank_1['count_of_zero'],
y=df_bank_1['label'],
orientation='h', # Horizontal box plot
name='Count of Zero',
))
'''fig.add_trace(go.Box(
x=df_bank_1['count_of_nan_zero'],
y=df_bank_1['label'],
orientation='h', # Horizontal box plot
name='Count of NaN + Zero',
))'''
# Update layout for the whole figure
fig.update_layout(
title='Count of NaN and Zero for Bank 0', #, and NaN + Zero for Bank 1',
yaxis_title='Class',
)
# Update x-axis titles
fig.update_xaxes(title_text='Count', row=1, col=1)
# Update y-axis titles
fig.update_yaxes(title_text='Class', row=1, col=1)
fig.show()
# Sum of bank users values
bank_counts = df['bank'].value_counts().head(12)
sum_of_bank_users_values = bank_counts.sum()
print('The number of banks is:', df.bank.nunique(),'\nPercentage of coverage if the banks with more than 140 users are keeped:',round(100*sum_of_bank_users_values/df.shape[0],1),'%', '\nThe number of remaining banks is:',bank_counts.nunique())
# List of banks with more than 140 users
print('\nBanks with more than 140 users:', bank_counts.to_dict())
The number of banks is: 24
Percentage of coverage if the banks with more than 140 users are keeped: 95.6 %
The number of remaining banks is: 12
Banks with more than 140 users: {'0': 4952, '3': 3279, '4': 2039, '11': 1861, '5': 1594, '2': 1078, '9': 1061, '6': 909, '1': 806, '8': 223, '10': 181, '13': 168}
- Mantendo apenas os dados correspondentes a 12 bancos específicos, podemos garantir que mais de 95% dos IDs de usuário permaneçam dentro do dataframe. A remoção de dados associados a outros bancos não apenas contribui para a economia de memória e recursos computacionais, mas também aprimora a adequação do conjunto de dados para fins de aprendizado de máquina.
- By retaining only the data corresponding to 12 specific banks, we can ensure that over 95% of user IDs remain within the dataframe. The removal of data associated with other banks not only contributes to memory and computational resource savings but also enhances the suitability of the dataset for machine learning purposes.
# Group the DataFrame by 'bank' and 'label' and calculate the counts
counts_df = df.groupby(['bank', 'label']).size().unstack(fill_value=0)
# Calculate the sum of counts for each 'bank'
counts_df['total_counts'] = counts_df.sum(axis=1)
# Sort the DataFrame by the total counts in descending order
counts_df = counts_df.sort_values(by='total_counts', ascending=False)
# Create a stacked bar graph
fig = go.Figure()
for label in counts_df.columns[:-1]: # Exclude the last column 'total_counts'
fig.add_trace(go.Bar(
x=counts_df.index[0:12],
y=counts_df[label],
name=f'Label {label}',
))
fig.update_layout(
title='Bank by Counts of Classes',
xaxis_title='Bank',
yaxis_title='Counts',
barmode='stack',
)
fig.show()
# Group the DataFrame by 'bank' and 'label' and calculate the counts
counts_df = df.groupby(['bank', 'label']).size().unstack(fill_value=0)
counts_df['total_counts'] = counts_df[1] + counts_df[0]
# Calculate the percentage of label '1' over label '0' for each bank
counts_df['percentage_1_over_0'] = round(100*(counts_df[1] /counts_df['total_counts']),1)
#counts_df = counts_df.sort_values(by='percentage_1_over_0', ascending=False)
counts_df = counts_df.sort_values(by='total_counts', ascending=False)
display(counts_df.head(12))
| label | 0 | 1 | total_counts | percentage_1_over_0 |
|---|---|---|---|---|
| bank | ||||
| 0 | 4660 | 292 | 4952 | 5.9 |
| 3 | 3106 | 173 | 3279 | 5.3 |
| 4 | 1951 | 88 | 2039 | 4.3 |
| 11 | 1800 | 61 | 1861 | 3.3 |
| 5 | 1492 | 102 | 1594 | 6.4 |
| 2 | 1031 | 47 | 1078 | 4.4 |
| 9 | 1005 | 56 | 1061 | 5.3 |
| 6 | 850 | 59 | 909 | 6.5 |
| 1 | 749 | 57 | 806 | 7.1 |
| 8 | 210 | 13 | 223 | 5.8 |
| 10 | 171 | 10 | 181 | 5.5 |
| 13 | 155 | 13 | 168 | 7.7 |
Some variables have high NaN, some case more than 95%. It may be grouped in 4 groups:
K00210 - K00211 and K00218 - K00220 (Pre-approved credit and Financial instability): Almost 100%
K00296, K00304, K00320, K00336, K00344, K00352, K00360, K00368 (Income): many with almost 100%
- Date of payment
- Date of payment 2, in the case of multiple dates
- Date of payment 3, in the case of multiple dates
- Date of payment 4, in the case of multiple dates
- Higher limit .... of in all conected accounts
- Higher limit of personal loan in all conected accounts
-
-
-
- Number of completed months with transactions of salary in last 90 days
- Number of completed months with transactions of business income in last 90 days
- Number of completed months with transactions of bonus in last 90 days
- Number of completed months with transactions of retirement??? in last 90 days
- Number of completed months with transactions of alamony??? in last 90 days
- Number of completed months with transactions of governemental supprot aid??? in last 90 days
- Number of completed months with transactions of other income in last 90 days
- Number of completed months with transactions of .... investment in last 90 days
- Number of completed months with transactions of .... investment in last 90 days
- Number of completed months with transactions of credit card in last 90 days
- Number of completed months with transactions of morgage in last 90 days
- Number of completed months with transactions of studant loan...in last 90 days
- Number of completed months with transactions of loan in last 90 days
- Number of months with rental passive and residential bills in last 60 days
Columns with 100% zero values: K00185, K00186, K00187, K00188, K00189, K00190, K00197, K00198, K00199, K00200, K00201, K00202, K00203, K00204, K00205, K00206, K00207, K00208.
Columns with 100% NaN values: K00210, K00211, K00336, K00352.
- As variaveis contento 100% de valores zero e 100% de valores NaN serão descartadas.
- Existem 41 colunas que contêm exclusivamente zeros e NaNs. Essas colunas serão mantidas por enquanto.
- These columns consist entirely of zeros or NaNs, which means they do not provide any variability for modeling purposes. Thus, it is advisable to exclude them from consideration.
- Currently, there are 41 columns that contain exclusively zeros and NaNs. These columns will be retained for the time being.
# Mover coluna label para o final, manter user_id e bank no inicio
# Move column label to last column, keep user_id and bank at the beggining
df = df[df.columns.difference(['label']).tolist() + ['label']]
df = df[['user_id', 'bank'] + [col for col in df.columns if col not in ['user_id', 'bank']]]
# Muda o tipo da coluna bank para categorica
# Change type of column bank to category
df['bank'] = df['bank'].astype('category')
display(df.head())
| user_id | bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00008 | ... | K00535 | K00536 | K00537 | K00538 | K00539 | K00540 | count_of_nan | count_of_nan_zero | count_of_zero | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 14.0 | 14.0 | 14.0 | 14.0 | 0.0 | 0.0 | NaN | NaN | ... | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | 129 | 453 | 324 | 0 |
| 1 | 1 | 1 | 19.0 | 19.0 | 19.0 | 19.0 | 0.0 | 0.0 | 41.0 | 41.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 25 | 375 | 350 | 0 |
| 2 | 2 | 0 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | NaN | NaN | ... | 20.0 | 0.0 | 0.0 | 1.0 | 1.0 | 20.0 | 91 | 426 | 335 | 0 |
| 3 | 3 | 2 | 21.0 | 21.0 | 21.0 | 21.0 | 0.0 | 0.0 | 310.0 | 310.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 180 | 472 | 292 | 0 |
| 4 | 4 | 3 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 257.0 | 257.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 95 | 382 | 287 | 0 |
5 rows × 546 columns
# Lista de variaveis que serão removidas
# List of K features to be dropped
# All features to be dropped going to be in that cell.
display("Df inicial", df.shape, df.head())
k_features_to_drop = [
'K00296', 'K00304', 'K00320', 'K00336', 'K00344', 'K00352', 'K00360', 'K00368', # Provavel MNAR
'K00499', 'K00507', 'K00515', 'K00523', 'K00539', # Provavel MNAR
'K00210', 'K00211', 'K00218', 'K00219', 'K00220', # Praticamente NaN. Pre-approved credit and Financial instability
'K00088', 'K00089', 'K00090', 'K00091', #
'K00185', 'K00186', 'K00187', 'K00188', 'K00189', 'K00190', 'K00197', 'K00198', 'K00199', 'K00200', 'K00201', 'K00202', 'K00203', 'K00204', 'K00205', 'K00206', 'K00207', 'K00208', # Only zeros or only NaNs
'K00011', 'K00012', 'K00023', 'K00024', 'K00025', 'K00330', 'K00331', 'K00332', 'K00333', 'K00334', 'K00335', 'K00337', 'K00346', 'K00347', 'K00348', 'K00349', 'K00350', 'K00351', 'K00353', #Correlation equal NaN
'K00008', 'K00009', 'K00010', 'K00020', 'K00021', 'K00022' # Equal correlation value
]
# Drop the specified K features from the DataFrame
df_droped_001 = df.drop(columns=k_features_to_drop).copy()
display("Df pós drop", df_droped_001.shape, df_droped_001.head(), "Numero de colunas removidas:", len(k_features_to_drop))
# Salva o df após a remoção das colunas
# Save the df after droping the columns
df_droped_001.to_csv(f'{files_directory}/df_droped_001.csv', index=False)
'Df inicial'
(18983, 546)
| user_id | bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00008 | ... | K00535 | K00536 | K00537 | K00538 | K00539 | K00540 | count_of_nan | count_of_nan_zero | count_of_zero | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 14.0 | 14.0 | 14.0 | 14.0 | 0.0 | 0.0 | NaN | NaN | ... | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | 129 | 453 | 324 | 0 |
| 1 | 1 | 1 | 19.0 | 19.0 | 19.0 | 19.0 | 0.0 | 0.0 | 41.0 | 41.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 25 | 375 | 350 | 0 |
| 2 | 2 | 0 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | NaN | NaN | ... | 20.0 | 0.0 | 0.0 | 1.0 | 1.0 | 20.0 | 91 | 426 | 335 | 0 |
| 3 | 3 | 2 | 21.0 | 21.0 | 21.0 | 21.0 | 0.0 | 0.0 | 310.0 | 310.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 180 | 472 | 292 | 0 |
| 4 | 4 | 3 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 257.0 | 257.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 95 | 382 | 287 | 0 |
5 rows × 546 columns
'Df pós drop'
(18983, 481)
| user_id | bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00013 | ... | K00534 | K00535 | K00536 | K00537 | K00538 | K00540 | count_of_nan | count_of_nan_zero | count_of_zero | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 14.0 | 14.0 | 14.0 | 14.0 | 0.0 | 0.0 | NaN | 0.0 | ... | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 129 | 453 | 324 | 0 |
| 1 | 1 | 1 | 19.0 | 19.0 | 19.0 | 19.0 | 0.0 | 0.0 | 41.0 | 14.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 25 | 375 | 350 | 0 |
| 2 | 2 | 0 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | NaN | 5.0 | ... | 0.0 | 20.0 | 0.0 | 0.0 | 1.0 | 20.0 | 91 | 426 | 335 | 0 |
| 3 | 3 | 2 | 21.0 | 21.0 | 21.0 | 21.0 | 0.0 | 0.0 | 310.0 | NaN | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 180 | 472 | 292 | 0 |
| 4 | 4 | 3 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 257.0 | NaN | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 95 | 382 | 287 | 0 |
5 rows × 481 columns
'Numero de colunas removidas:'
65
* Algumas variaveis removidas são altamente correlacionadas com outras variaveis. Isso será discutido a seguir. Para facilitar a visualiazação e controle, foram agrupadas nessa célula.
* Some features are highly correlated with others.
# Verifica a correlação entre as variaveis e rotulos utilizando 3 metodos distintos 'pearson', 'kendall' e 'spearman'
# Check the correlation with labels using 3 different methods 'pearson', 'kendall' and 'spearman'
correlation_methods = ['pearson', 'kendall', 'spearman']
sum_of_corr = {}
sum_of_corr_abs = {}
for method in correlation_methods:
# Create a dataFrame containing correlation values with columns as feature names
correlation_with_label = df_droped_001.drop(columns='user_id').corr(method=method)['label']
# Fill NaN values to avoid errors NaN values are caused by features with 0 variance
#correlation_with_label = correlation_with_label.fillna(0)
highest_correlations = correlation_with_label.nlargest(50)
lowest_correlations = correlation_with_label.nsmallest(50)
highest_correlations_df = pd.DataFrame(highest_correlations)
lowest_correlations_df = pd.DataFrame(lowest_correlations)
# Save the top 50 highest correlations to a text file
with open(f'{files_directory}/top_50_highest_correlations_{method}.txt', 'w') as f:
f.write(f"Top 50 highest correlations ({method}):\n")
f.write(highest_correlations_df.to_string())
# Save the top 50 lowest correlations to a text file
with open(f'{files_directory}/top_50_lowest_correlations_{method}.txt', 'w') as f:
f.write(f"Top 50 lowest correlations ({method}):\n")
f.write(lowest_correlations_df.to_string())
# Print the DataFrames
#print(f"\nTop 50 highest correlations ({method}):")
#print(highest_correlations_df)
#print(f"\nTop 50 lowest correlations ({method}):")
#print(lowest_correlations_df)
# Calculate the sum of correlation values for each feature
sum_of_corr[method] = correlation_with_label.drop('label').sum()
# Calculate the sum of absolute correlation values for each feature
sum_of_corr_abs[method] = correlation_with_label.drop('label').abs().sum()
# Print the sums of correlation values
print("\nSum of correlation values for each feature:")
print(sum_of_corr)
# Print the sums of absolute correlation values
print("\nSum of absolute correlation values for each feature:")
print(sum_of_corr_abs)
Sum of correlation values for each feature:
{'pearson': -3.7681564988963743, 'kendall': -7.932923432420395, 'spearman': -9.192178821040425}
Sum of absolute correlation values for each feature:
{'pearson': 8.544891749416726, 'kendall': 11.173423231243863, 'spearman': 12.712738085766198}
- The lowest value of sum of **Pearson's** correlation indicates that the label may not have a strong linear correlation with features
# Correlação entre as variaveis
# Correlation of features
start_timer()
for method in correlation_methods:
print(f"Correlation Method: {method}")
# Calculate the correlation matrix
correlation_matrix = df_droped_001.drop(columns=['user_id']).corr(method=method)
# Save the correlation matrix to a DataFrame
correlation_matrix_df = pd.DataFrame(correlation_matrix)
# Add a new column for the sum of each row
correlation_matrix_df['Sum'] = correlation_matrix_df.sum(axis=1)
# Add a new column for the sum of absolute values of each row
correlation_matrix_df['Sum_abs'] = correlation_matrix_df.abs().sum(axis=1)
# Print the correlation matrix DataFrame
#print("Correlation Matrix:")
display(correlation_matrix_df.head())
# Save the correlation matrix DataFrame to a CSV file
correlation_matrix_df.to_csv(f'{files_directory}/correlation_matrix_{method}.csv', index=True)
end_timer()
Correlation Method: pearson
| bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00013 | K00014 | ... | K00536 | K00537 | K00538 | K00540 | count_of_nan | count_of_nan_zero | count_of_zero | label | Sum | Sum_abs | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bank | 1.000000 | 0.000306 | -0.013739 | -0.006725 | -0.006654 | 0.093561 | 0.098165 | -0.349894 | 0.123056 | 0.097912 | ... | -0.045770 | -0.019802 | -0.010929 | 0.019038 | 0.136366 | 0.084881 | -0.153446 | -0.025069 | 16.491215 | 40.968669 |
| K00001 | 0.000306 | 1.000000 | 0.988602 | 0.997065 | 0.996989 | 0.033200 | 0.001374 | 0.024237 | 0.236718 | 0.229680 | ... | 0.020486 | 0.059671 | 0.075644 | -0.015687 | -0.344764 | -0.348567 | 0.277069 | -0.057694 | 20.514920 | 44.350152 |
| K00002 | -0.013739 | 0.988602 | 1.000000 | 0.997181 | 0.997095 | -0.117459 | -0.133339 | 0.022320 | 0.228092 | 0.226284 | ... | 0.016680 | 0.056504 | 0.072670 | -0.015983 | -0.346588 | -0.351055 | 0.278088 | -0.058246 | 19.192749 | 42.889091 |
| K00003 | -0.006725 | 0.997065 | 0.997181 | 1.000000 | 0.999965 | -0.042824 | -0.067564 | 0.023302 | 0.233297 | 0.228900 | ... | 0.018513 | 0.058147 | 0.074282 | -0.015890 | -0.346652 | -0.350788 | 0.278370 | -0.058126 | 19.895962 | 43.625725 |
| K00004 | -0.006654 | 0.996989 | 0.997095 | 0.999965 | 1.000000 | -0.042691 | -0.068103 | 0.023302 | 0.233638 | 0.229296 | ... | 0.018394 | 0.058051 | 0.074189 | -0.015911 | -0.346620 | -0.350722 | 0.278367 | -0.058121 | 19.890949 | 43.617091 |
5 rows × 482 columns
Correlation Method: kendall
| bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00013 | K00014 | ... | K00536 | K00537 | K00538 | K00540 | count_of_nan | count_of_nan_zero | count_of_zero | label | Sum | Sum_abs | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bank | 1.000000 | 0.015741 | 0.008667 | 0.011287 | 0.011317 | 0.094475 | 0.094515 | -0.357972 | 0.088785 | 0.046519 | ... | 0.012287 | 0.051247 | 0.052780 | 0.104050 | 0.066009 | 0.033411 | -0.168107 | -0.019689 | 26.756921 | 68.048190 |
| K00001 | 0.015741 | 1.000000 | 0.988760 | 0.993006 | 0.993045 | -0.001263 | -0.001529 | 0.147826 | 0.188408 | 0.173633 | ... | 0.070153 | 0.105425 | 0.125985 | 0.110310 | -0.244324 | -0.258224 | 0.123906 | -0.050728 | 28.188426 | 60.015640 |
| K00002 | 0.008667 | 0.988760 | 1.000000 | 0.996139 | 0.996126 | -0.090401 | -0.090631 | 0.145985 | 0.183760 | 0.173100 | ... | 0.068412 | 0.102437 | 0.122582 | 0.106690 | -0.246793 | -0.259606 | 0.126451 | -0.050982 | 27.549275 | 59.397995 |
| K00003 | 0.011287 | 0.993006 | 0.996139 | 1.000000 | 0.999916 | -0.058499 | -0.058769 | 0.146557 | 0.185520 | 0.173557 | ... | 0.068922 | 0.103270 | 0.123522 | 0.107637 | -0.246372 | -0.259432 | 0.126144 | -0.050829 | 27.754885 | 59.571441 |
| K00004 | 0.011317 | 0.993045 | 0.996126 | 0.999916 | 1.000000 | -0.058330 | -0.058598 | 0.146557 | 0.185598 | 0.173634 | ... | 0.068867 | 0.103221 | 0.123472 | 0.107592 | -0.246367 | -0.259413 | 0.126147 | -0.050834 | 27.752297 | 59.566785 |
5 rows × 482 columns
Correlation Method: spearman
| bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00013 | K00014 | ... | K00536 | K00537 | K00538 | K00540 | count_of_nan | count_of_nan_zero | count_of_zero | label | Sum | Sum_abs | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bank | 1.000000 | 0.023824 | 0.013890 | 0.017503 | 0.017544 | 0.108101 | 0.108130 | -0.466259 | 0.110389 | 0.055778 | ... | 0.010902 | 0.051133 | 0.054243 | 0.104787 | 0.090837 | 0.053303 | -0.228824 | -0.022700 | 32.565850 | 83.406895 |
| K00001 | 0.023824 | 1.000000 | 0.991139 | 0.995799 | 0.995794 | -0.001527 | -0.001789 | 0.201014 | 0.261068 | 0.241096 | ... | 0.086221 | 0.134749 | 0.164304 | 0.141749 | -0.347728 | -0.357697 | 0.187914 | -0.059422 | 35.150522 | 75.071861 |
| K00002 | 0.013890 | 0.991139 | 1.000000 | 0.998883 | 0.998845 | -0.106005 | -0.106105 | 0.198237 | 0.254915 | 0.240370 | ... | 0.084085 | 0.131006 | 0.159978 | 0.137037 | -0.351544 | -0.359849 | 0.191962 | -0.059736 | 34.423049 | 74.402290 |
| K00003 | 0.017503 | 0.995799 | 0.998883 | 1.000000 | 0.999986 | -0.068729 | -0.068884 | 0.199135 | 0.257284 | 0.241123 | ... | 0.084692 | 0.132070 | 0.161188 | 0.138341 | -0.350911 | -0.359614 | 0.191304 | -0.059570 | 34.666556 | 74.608073 |
| K00004 | 0.017544 | 0.995794 | 0.998845 | 0.999986 | 1.000000 | -0.068526 | -0.068683 | 0.199135 | 0.257405 | 0.241241 | ... | 0.084624 | 0.132008 | 0.161124 | 0.138277 | -0.350892 | -0.359584 | 0.191305 | -0.059574 | 34.662795 | 74.601148 |
5 rows × 482 columns
# Create a dataframe of sum of correlations for each method
df_sum_of_corrs = pd.DataFrame()
for method in correlation_methods:
# Load the correlation matrix DataFrame from the CSV file
correlation_matrix_df = pd.read_csv(f'{files_directory}/correlation_matrix_{method}.csv', index_col=0)
# Add columns Sum and Sum_abs to the correlation_matrix_df DataFrame for each method
df_sum_of_corrs['Sum_' + method] = correlation_matrix_df['Sum'].round(1)
df_sum_of_corrs['Sum_abs_' + method] = correlation_matrix_df['Sum_abs'].round(1)
# Save the df_sum_of_corrs DataFrame to a CSV file
df_sum_of_corrs.to_csv(f'{files_directory}/df_sum_of_corrs.csv', index=True)
display(df_sum_of_corrs.tail())
| Sum_pearson | Sum_abs_pearson | Sum_kendall | Sum_abs_kendall | Sum_spearman | Sum_abs_spearman | |
|---|---|---|---|---|---|---|
| K00540 | 38.2 | 81.1 | 70.2 | 145.0 | 84.2 | 174.6 |
| count_of_nan | -32.2 | 71.5 | -51.1 | 108.4 | -69.6 | 146.4 |
| count_of_nan_zero | -64.5 | 134.2 | -84.3 | 174.2 | -111.9 | 230.3 |
| count_of_zero | -26.8 | 65.7 | -46.2 | 103.2 | -58.9 | 132.7 |
| label | -2.8 | 12.3 | -6.9 | 19.1 | -8.2 | 21.9 |
Adequado para capturar relacionamentos entre recursos e variável de destino na modelagem de risco de crédito quando a natureza do relacionamento não é necessariamente linear.
Na modelagem de risco de crédito e seleção de recursos, a escolha do método de correlação depende da natureza dos dados e dos relacionamentos entre as variáveis. Por exemplo:
Ao lidar com tipos de dados mistos (categóricos, ordinais, contínuos), Kendall tau pode ser mais adequado devido à sua natureza não paramétrica.
Devido aos tipos de dados mistos, a natureza do relacionamento entre recursos e destino não é necessariamente linear e a distribuição não homogênea, Kendall deve ser mais apropriada para lidar com o conjunto de dados klavi.
Suitable for capturing relationships between features and target variable in credit risk modeling when the nature of the relationship is not necessarily linear.
In credit risk modeling and feature selection, the choice of correlation method depends on the nature of the data and the relationships between variables. For example:
When dealing with mixed data types (categorical, ordinal, continuous), Kendall tau might be more suitable due to its non-parametric nature.
Due to mixed data types, nature of the relationship features target is not necessarily linear and non homogeneous distribution Kendall should be more appropriate to deal with klavi dataset.
# Create a DataFrame to store correlation results
correlation_methods = ['pearson', 'spearman', 'kendall']
correlation_results = pd.DataFrame()
# Iterate over each correlation method
for method in correlation_methods:
correlation_matrix_label_0 = df_droped_001[df_droped_001['label'] == 0].corr(method=method)
correlation_matrix_label_1 = df_droped_001[df_droped_001['label'] == 1].corr(method=method)
# Calculate the sum of correlations for each class
sum_corr_label_0 = correlation_matrix_label_0.sum()
sum_corr_label_1 = correlation_matrix_label_1.sum()
# Calculate the difference in sum of correlations between classes
diff_sum_corr = sum_corr_label_0 - sum_corr_label_1
# Create a dictionary for the correlation results
correlation_result = {
f'Corr_{method}_label_0': sum_corr_label_0,
f'Corr_{method}_label_1': sum_corr_label_1,
f'Diff_Corr_{method}': diff_sum_corr
}
# Append the dictionary to the correlation_results DataFrame
correlation_results = pd.concat([correlation_results, pd.DataFrame(correlation_result)], axis=1)
# Save the correlation results DataFrame to a CSV file
correlation_results.to_csv(f'{files_directory}/correlation_results_with_labels_0_1.csv', index=False)
# Display the correlation results DataFrame
display(correlation_results.head())
"\n# Iterate over each correlation method\nfor method in correlation_methods:\n correlation_matrix_label_0 = df_droped_001[df_droped_001['label'] == 0].corr(method=method)\n correlation_matrix_label_1 = df_droped_001[df_droped_001['label'] == 1].corr(method=method)\n \n # Calculate the sum of correlations for each class\n sum_corr_label_0 = correlation_matrix_label_0.sum()\n sum_corr_label_1 = correlation_matrix_label_1.sum()\n \n # Calculate the difference in sum of correlations between classes\n diff_sum_corr = sum_corr_label_0 - sum_corr_label_1\n \n # Create a dictionary for the correlation results\n correlation_result = {\n f'Corr_{method}_label_0': sum_corr_label_0,\n f'Corr_{method}_label_1': sum_corr_label_1,\n f'Diff_Corr_{method}': diff_sum_corr\n }\n \n # Append the dictionary to the correlation_results DataFrame\n correlation_results = pd.concat([correlation_results, pd.DataFrame(correlation_result)], axis=1)\n\n# Save the correlation results DataFrame to a CSV file\ncorrelation_results.to_csv(f'{files_directory}/correlation_results_with_labels_0_1.csv', index=False)\n\n# Display the correlation results DataFrame\ndisplay(correlation_results.head())"
correlation_results.describe().round(0)
| Corr_pearson_label_0 | Corr_pearson_label_1 | Diff_Corr_pearson | Corr_spearman_label_0 | Corr_spearman_label_1 | Diff_Corr_spearman | Corr_kendall_label_0 | Corr_kendall_label_1 | Diff_Corr_kendall | |
|---|---|---|---|---|---|---|---|---|---|
| count | 481.0 | 481.0 | 481.0 | 481.0 | 481.0 | 481.0 | 481.0 | 481.0 | 481.0 |
| mean | 31.0 | 26.0 | 6.0 | 46.0 | 37.0 | 9.0 | 39.0 | 32.0 | 7.0 |
| std | 23.0 | 20.0 | 11.0 | 31.0 | 28.0 | 10.0 | 24.0 | 22.0 | 9.0 |
| min | -65.0 | -62.0 | -42.0 | -112.0 | -105.0 | -32.0 | -84.0 | -79.0 | -24.0 |
| 25% | 17.0 | 13.0 | -1.0 | 23.0 | 18.0 | 4.0 | 21.0 | 17.0 | 3.0 |
| 50% | 26.0 | 22.0 | 6.0 | 42.0 | 33.0 | 10.0 | 36.0 | 30.0 | 8.0 |
| 75% | 42.0 | 36.0 | 12.0 | 67.0 | 53.0 | 15.0 | 56.0 | 46.0 | 13.0 |
| max | 95.0 | 87.0 | 64.0 | 117.0 | 105.0 | 81.0 | 91.0 | 82.0 | 68.0 |
.
# Cria mapa de calor com as correlções para cada método
# Create a heatmap of correlations for each method
# Drop bank column
df_droped_001_filled_0 = df_droped_001.drop(columns=['bank']).copy()
# Fill NaN values with 0.
df_droped_001_filled_0 = df_droped_001_filled_0.fillna(0) # Isso resulta em uma matriz de correlação diferente da matriz de correlação original
for method in correlation_methods:
plt.figure(figsize=(150, 140))
sns.heatmap(df_droped_001_filled_0.corr(method=method), annot=False, cmap='coolwarm', fmt=".2f")
plt.title(f'Correlation Heatmap ({method.capitalize()})')
print(f'Correlation Heatmap ({method.capitalize()})')
# Save the heatmap as a PNG file
plt.savefig(f'{files_directory}/correlation_matrix_{method}_heatmap.png')
# Show the heatmap
plt.show()
Correlation Heatmap (Pearson)
Correlation Heatmap (Spearman)
Correlation Heatmap (Kendall)
High Correlation and Feature Selection:
Diverse Correlation Values:
Information Value (IV):
Specific Example:
Weight of evidence (WOE) by transforming continuous variables into categorical features. The continuous variable is split into bins and based on their WOE , new variables are created. Also, Information Value (IV) helps to determine which feature is useful in prediction. The information value for the independent variables are indicted below. Variables with IV less than 0.02 will not be included in the model because they have no prediction power. -Siddiqi(2006)
# WOE: Weight of Evidence (WOE) and Information Value (IV)
def calculate_woe_iv(data, feature, target):
"""
Calculate the Weight of Evidence (WOE) and Information Value (IV) for a given feature.
Parameters:
data (DataFrame): The input DataFrame containing the feature and target columns.
feature (str): The name of the feature column (continuous variable).
target (str): The name of the target column (dependent variable).
Returns:
float: Information Value (IV) for the given feature.
"""
# Create a copy of the input data to avoid modifying the original DataFrame
data_copy = df_droped_001_filled_0.copy()
# Fill missing values in the feature column with zeros
#data_copy[feature].fillna(0, inplace=True)
# Calculate the total number of positive and negative instances in the target variable
total_positives = data_copy[target].sum()
total_negatives = data_copy[target].count() - total_positives
# Handle special case if there are no positive or negative instances
if total_positives == 0 or total_negatives == 0:
raise ValueError("The target variable should have both positive and negative instances.")
# Calculate the total number of bins to be used
# Use the following to create 20 equal-frequency bins:
data_copy['bins'] = pd.qcut(data_copy[feature], q=20, duplicates='drop')
# Calculate the number of positive and negative instances in each bin
grouped = data_copy.groupby('bins')
bin_counts = grouped[target].agg(['sum', 'count'])
# Calculate the WoE for each bin and handle the case where WoE is infinite (log(0))
bin_counts['woe'] = np.log((bin_counts['sum'] / total_positives) / (bin_counts['count'] / total_negatives))
bin_counts.replace([np.inf, -np.inf], 0, inplace=True)
# Calculate the IV for the feature by summing up the contributions from each bin
iv = ((bin_counts['sum'] / total_positives) - (bin_counts['count'] / total_negatives)) * bin_counts['woe']
iv = iv.sum()
return iv
# Calculate IV for each continuous feature in the DataFrame 'df'
iv_values = {}
continuous_features = df_droped_001_filled_0.select_dtypes(include=[np.number]).columns.tolist()
for feature in continuous_features:
iv = calculate_woe_iv(df_droped_001_filled_0, feature, 'label')
iv_values[feature] = iv
# Save the IV values to a text file
with open('IV_values.txt', 'w') as file:
for feature, iv in iv_values.items():
file.write(f"Information Value (IV) for {feature}: {iv}\n")
# Create the DataFrame df_IV_001
data = {'Feature': list(iv_values.keys()), 'IV': list(iv_values.values())}
df_IV_001 = pd.DataFrame(data)
# Display the DataFrame df_IV_001
print(df_IV_001)
Feature IV 0 user_id 0.020526 1 K00001 0.091833 2 K00002 0.090353 3 K00003 0.091075 4 K00004 0.091174 5 K00005 0.003009 6 K00006 0.003009 7 K00007 0.028465 8 K00013 0.057652 9 K00014 0.059134 10 K00015 0.054569 11 K00016 0.051786 12 K00017 0.008913 13 K00018 0.007743 14 K00019 0.046770 15 K00026 0.003090 16 K00027 0.022579 17 K00028 0.006833 18 K00029 0.046416 19 K00030 0.120599 20 K00031 0.117248 21 K00032 0.125969 22 K00033 0.129081 23 K00034 0.027625 24 K00035 0.035197 25 K00036 0.128550 26 K00037 0.115540 27 K00038 0.125732 28 K00039 0.128030 29 K00040 0.105282 30 K00041 0.105304 31 K00042 0.135852 32 K00043 0.110997 33 K00044 0.128731 34 K00045 0.133440 35 K00046 0.118222 36 K00047 0.114272 37 K00048 0.044286 38 K00049 0.030771 39 K00050 0.040749 40 K00051 0.043353 41 K00052 0.003009 42 K00053 0.003009 43 K00054 0.065690 44 K00055 0.029135 45 K00056 0.057856 46 K00057 0.052124 47 K00058 0.065017 48 K00059 0.052508 49 K00060 0.049522 50 K00061 0.027951 51 K00062 0.047493 52 K00063 0.040768 53 K00064 0.053086 54 K00065 0.054384 55 K00066 0.065106 56 K00067 0.057558 57 K00068 0.061027 58 K00069 0.061064 59 K00070 0.003009 60 K00071 0.003009 61 K00072 0.076059 62 K00073 0.062009 63 K00074 0.072305 64 K00075 0.068626 65 K00076 0.053683 66 K00077 0.035929 67 K00078 0.060682 68 K00079 0.050290 69 K00080 0.061969 70 K00081 0.061371 71 K00082 0.044993 72 K00083 0.046491 73 K00084 0.048754 74 K00085 0.133490 75 K00086 0.133494 76 K00087 0.130009 77 K00092 0.230129 78 K00093 0.237117 79 K00094 0.230759 80 K00095 0.171099 81 K00096 0.180354 82 K00097 0.176811 83 K00098 0.216294 84 K00099 0.217680 85 K00100 0.226909 86 K00101 0.127636 87 K00102 0.140002 88 K00103 0.135174 89 K00104 0.230779 90 K00105 0.239790 91 K00106 0.224047 92 K00107 0.165097 93 K00108 0.179036 94 K00109 0.183326 95 K00110 0.217470 96 K00111 0.213073 97 K00112 0.209657 98 K00113 0.122427 99 K00114 0.138751 100 K00115 0.129169 101 K00116 0.265698 102 K00117 0.293708 103 K00118 0.257652 104 K00119 0.176604 105 K00120 0.194380 106 K00121 0.189049 107 K00122 0.228140 108 K00123 0.226998 109 K00124 0.218431 110 K00125 0.136065 111 K00126 0.142363 112 K00127 0.131074 113 K00128 0.024510 114 K00129 0.040023 115 K00130 0.033256 116 K00131 0.018936 117 K00132 0.031464 118 K00133 0.046961 119 K00134 0.007983 120 K00135 0.009672 121 K00136 0.011919 122 K00137 0.005617 123 K00138 0.003249 124 K00139 0.003591 125 K00140 0.139547 126 K00141 0.139200 127 K00142 0.132486 128 K00143 0.034890 129 K00144 0.057567 130 K00145 0.052723 131 K00146 0.016109 132 K00147 0.037724 133 K00148 0.047565 134 K00149 0.006490 135 K00150 0.016956 136 K00151 0.016593 137 K00152 0.005365 138 K00153 0.006152 139 K00154 0.010536 140 K00155 0.245358 141 K00156 0.262254 142 K00157 0.244759 143 K00158 0.170801 144 K00159 0.193100 145 K00160 0.191877 146 K00161 0.205365 147 K00162 0.211513 148 K00163 0.210402 149 K00164 0.143525 150 K00165 0.158824 151 K00166 0.157135 152 K00167 0.082346 153 K00168 0.103279 154 K00169 0.114813 155 K00170 0.024664 156 K00171 0.084430 157 K00172 0.101503 158 K00173 0.011784 159 K00174 0.015258 160 K00175 0.018745 161 K00176 0.012090 162 K00177 0.012545 163 K00178 0.010709 164 K00179 0.208522 165 K00180 0.233894 166 K00181 0.221063 167 K00182 0.141067 168 K00183 0.170483 169 K00184 0.155907 170 K00191 0.013190 171 K00192 0.008790 172 K00193 0.012793 173 K00194 0.005873 174 K00195 0.007767 175 K00196 0.009227 176 K00209 0.006772 177 K00212 0.003517 178 K00213 0.003517 179 K00214 0.003090 180 K00215 0.003091 181 K00216 0.003272 182 K00217 0.004152 183 K00221 0.003009 184 K00222 0.003009 185 K00223 0.003009 186 K00224 0.003009 187 K00225 0.003009 188 K00226 0.003009 189 K00227 0.003009 190 K00228 0.003009 191 K00229 0.003009 192 K00230 0.003009 193 K00231 0.003009 194 K00232 0.003009 195 K00233 0.003009 196 K00234 0.003009 197 K00235 0.003009 198 K00236 0.003009 199 K00237 0.003009 200 K00238 0.003009 201 K00239 0.004630 202 K00240 0.003122 203 K00241 0.003529 204 K00242 0.004558 205 K00243 0.004254 206 K00244 0.003626 207 K00245 0.003009 208 K00246 0.003009 209 K00247 0.003009 210 K00248 0.003009 211 K00249 0.003009 212 K00250 0.003009 213 K00251 0.003009 214 K00252 0.003009 215 K00253 0.003009 216 K00254 0.003009 217 K00255 0.003009 218 K00256 0.003009 219 K00257 0.015204 220 K00258 0.041617 221 K00259 0.038843 222 K00260 0.024260 223 K00261 0.025448 224 K00262 0.027956 225 K00263 0.009633 226 K00264 0.014252 227 K00265 0.017516 228 K00266 0.007117 229 K00267 0.011013 230 K00268 0.007666 231 K00269 0.003009 232 K00270 0.003009 233 K00271 0.003009 234 K00272 0.003009 235 K00273 0.003009 236 K00274 0.003009 237 K00275 0.003009 238 K00276 0.003009 239 K00277 0.003009 240 K00278 0.003009 241 K00279 0.003009 242 K00280 0.003009 243 K00281 0.235114 244 K00282 0.259555 245 K00283 0.233654 246 K00284 0.154692 247 K00285 0.195715 248 K00286 0.186899 249 K00287 0.094677 250 K00288 0.109018 251 K00289 0.090107 252 K00290 0.023469 253 K00291 0.021600 254 K00292 0.027754 255 K00293 0.014335 256 K00294 0.010337 257 K00295 0.021833 258 K00297 0.022798 259 K00298 0.017480 260 K00299 0.035221 261 K00300 0.039400 262 K00301 0.015967 263 K00302 0.024552 264 K00303 0.028800 265 K00305 0.041010 266 K00306 0.219670 267 K00307 0.285362 268 K00308 0.231528 269 K00309 0.156597 270 K00310 0.177023 271 K00311 0.186745 272 K00312 0.202736 273 K00313 0.236045 274 K00314 0.003009 275 K00315 0.003009 276 K00316 0.003009 277 K00317 0.003009 278 K00318 0.003009 279 K00319 0.003009 280 K00321 0.003009 281 K00322 0.003009 282 K00323 0.003009 283 K00324 0.003009 284 K00325 0.003009 285 K00326 0.003009 286 K00327 0.003009 287 K00328 0.003009 288 K00329 0.003009 289 K00338 0.003009 290 K00339 0.003009 291 K00340 0.003009 292 K00341 0.003009 293 K00342 0.003009 294 K00343 0.003009 295 K00345 0.003009 296 K00354 0.003009 297 K00355 0.014006 298 K00356 0.011669 299 K00357 0.003009 300 K00358 0.011370 301 K00359 0.006031 302 K00361 0.013954 303 K00362 0.033631 304 K00363 0.045233 305 K00364 0.035195 306 K00365 0.024386 307 K00366 0.039465 308 K00367 0.039758 309 K00369 0.039873 310 K00370 0.003009 311 K00371 0.003009 312 K00372 0.003009 313 K00373 0.003009 314 K00374 0.003009 315 K00375 0.003009 316 K00376 0.003009 317 K00377 0.003009 318 K00378 0.015314 319 K00379 0.003009 320 K00380 0.003009 321 K00381 0.028566 322 K00382 0.003009 323 K00383 0.021600 324 K00384 0.029932 325 K00385 0.003009 326 K00386 0.012377 327 K00387 0.013938 328 K00388 0.003009 329 K00389 0.003009 330 K00390 0.003009 331 K00391 0.003009 332 K00392 0.003009 333 K00393 0.003009 334 K00394 0.056760 335 K00395 0.072951 336 K00396 0.066822 337 K00397 0.042201 338 K00398 0.066536 339 K00399 0.074456 340 K00400 0.003009 341 K00401 0.003009 342 K00402 0.003009 343 K00403 0.003009 344 K00404 0.003009 345 K00405 0.003009 346 K00406 0.003009 347 K00407 0.003588 348 K00408 0.004162 349 K00409 0.003009 350 K00410 0.004489 351 K00411 0.003552 352 K00412 0.003009 353 K00413 0.003009 354 K00414 0.003009 355 K00415 0.003009 356 K00416 0.003009 357 K00417 0.003009 358 K00418 0.003009 359 K00419 0.003009 360 K00420 0.003009 361 K00421 0.003009 362 K00422 0.003009 363 K00423 0.003009 364 K00424 0.003009 365 K00425 0.003009 366 K00426 0.003009 367 K00427 0.003009 368 K00428 0.003009 369 K00429 0.003009 370 K00430 0.003009 371 K00431 0.003009 372 K00432 0.003009 373 K00433 0.003009 374 K00434 0.003009 375 K00435 0.003009 376 K00436 0.003009 377 K00437 0.003009 378 K00438 0.003009 379 K00439 0.003009 380 K00440 0.003009 381 K00441 0.003009 382 K00442 0.003009 383 K00443 0.003009 384 K00444 0.003009 385 K00445 0.003009 386 K00446 0.003009 387 K00447 0.003009 388 K00448 0.003009 389 K00449 0.003009 390 K00450 0.003009 391 K00451 0.003009 392 K00452 0.003009 393 K00453 0.003009 394 K00454 0.031972 395 K00455 0.046293 396 K00456 0.048118 397 K00457 0.027574 398 K00458 0.041490 399 K00459 0.050857 400 K00460 0.003009 401 K00461 0.003009 402 K00462 0.003009 403 K00463 0.003009 404 K00464 0.003009 405 K00465 0.003009 406 K00466 0.003009 407 K00467 0.003009 408 K00468 0.003009 409 K00469 0.003009 410 K00470 0.003009 411 K00471 0.003009 412 K00472 0.003009 413 K00473 0.003009 414 K00474 0.003009 415 K00475 0.003009 416 K00476 0.003009 417 K00477 0.003009 418 K00478 0.003009 419 K00479 0.012793 420 K00480 0.013954 421 K00481 0.006411 422 K00482 0.011662 423 K00483 0.012501 424 K00484 0.183749 425 K00485 0.200730 426 K00486 0.181226 427 K00487 0.131255 428 K00488 0.154718 429 K00489 0.137803 430 K00490 0.100991 431 K00491 0.105218 432 K00492 0.101988 433 K00493 0.083080 434 K00494 0.115885 435 K00495 0.121836 436 K00496 0.026020 437 K00497 0.089054 438 K00498 0.105900 439 K00500 0.110731 440 K00501 0.003009 441 K00502 0.003009 442 K00503 0.003009 443 K00504 0.003009 444 K00505 0.003009 445 K00506 0.003009 446 K00508 0.003009 447 K00509 0.003009 448 K00510 0.003009 449 K00511 0.003009 450 K00512 0.003009 451 K00513 0.003009 452 K00514 0.003009 453 K00516 0.003009 454 K00517 0.004952 455 K00518 0.007359 456 K00519 0.009700 457 K00520 0.008050 458 K00521 0.003745 459 K00522 0.003155 460 K00524 0.007995 461 K00525 0.159694 462 K00526 0.195744 463 K00527 0.187860 464 K00528 0.119034 465 K00529 0.152939 466 K00530 0.145067 467 K00531 0.138057 468 K00532 0.165676 469 K00533 0.074703 470 K00534 0.076538 471 K00535 0.069820 472 K00536 0.053365 473 K00537 0.064541 474 K00538 0.069492 475 K00540 0.065302 476 count_of_nan 0.264868 477 count_of_nan_zero 0.223807 478 count_of_zero 0.174835 479 label 0.003009
df_IV_001 = df_IV_001.sort_values(by='IV', ascending=False)
display(round(df_IV_001.head(25),2), round(df_IV_001.tail(25),2))
# Save df_IV_001 to a CSV file
df_IV_001.to_csv(f'{files_directory}/df_IV_001.csv', index=False)
| Feature | IV | |
|---|---|---|
| 102 | K00117 | 0.29 |
| 267 | K00307 | 0.29 |
| 101 | K00116 | 0.27 |
| 476 | count_of_nan | 0.26 |
| 141 | K00156 | 0.26 |
| 244 | K00282 | 0.26 |
| 103 | K00118 | 0.26 |
| 140 | K00155 | 0.25 |
| 142 | K00157 | 0.24 |
| 90 | K00105 | 0.24 |
| 78 | K00093 | 0.24 |
| 273 | K00313 | 0.24 |
| 243 | K00281 | 0.24 |
| 165 | K00180 | 0.23 |
| 245 | K00283 | 0.23 |
| 268 | K00308 | 0.23 |
| 89 | K00104 | 0.23 |
| 79 | K00094 | 0.23 |
| 77 | K00092 | 0.23 |
| 107 | K00122 | 0.23 |
| 108 | K00123 | 0.23 |
| 85 | K00100 | 0.23 |
| 91 | K00106 | 0.22 |
| 477 | count_of_nan_zero | 0.22 |
| 166 | K00181 | 0.22 |
| Feature | IV | |
|---|---|---|
| 284 | K00325 | 0.0 |
| 285 | K00326 | 0.0 |
| 286 | K00327 | 0.0 |
| 287 | K00328 | 0.0 |
| 288 | K00329 | 0.0 |
| 289 | K00338 | 0.0 |
| 290 | K00339 | 0.0 |
| 291 | K00340 | 0.0 |
| 292 | K00341 | 0.0 |
| 293 | K00342 | 0.0 |
| 294 | K00343 | 0.0 |
| 296 | K00354 | 0.0 |
| 322 | K00382 | 0.0 |
| 299 | K00357 | 0.0 |
| 310 | K00370 | 0.0 |
| 311 | K00371 | 0.0 |
| 312 | K00372 | 0.0 |
| 313 | K00373 | 0.0 |
| 314 | K00374 | 0.0 |
| 315 | K00375 | 0.0 |
| 316 | K00376 | 0.0 |
| 317 | K00377 | 0.0 |
| 319 | K00379 | 0.0 |
| 320 | K00380 | 0.0 |
| 479 | label | 0.0 |
- Os valores de IV foram gerados com o dataframe com valores NaN preenchidos com zero. Assim, é possivel uma posterior comparação entre os valores de IV com e sem os valores NaN.
- The IV values were generated with the dataframe with NaN values filled with zero. Thus, it is possible a later comparison between the IV values with and without the NaN values.
print('Intial:',df.shape, 'Current:', df_droped_001.shape)
Intial: (18983, 546) Current: (18983, 481)
df = pd.read_csv(f'{files_directory}/df_droped_001.csv')
# Sampling to reduce the number of rows and run faster
df = df.sample(frac=1, random_state=42)
df.head()
| user_id | bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00013 | ... | K00534 | K00535 | K00536 | K00537 | K00538 | K00540 | count_of_nan | count_of_nan_zero | count_of_zero | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15695 | 15695 | 3 | 17.0 | 17.0 | 17.0 | 17.0 | 0.0 | 0.0 | 249.0 | 5.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 23 | 328 | 305 | 0 |
| 16205 | 16205 | 3 | NaN | NaN | NaN | NaN | NaN | NaN | 194.0 | 16.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 312 | 443 | 131 | 0 |
| 9060 | 9060 | 5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 519 | 539 | 20 | 0 |
| 11597 | 11597 | 5 | 14.0 | 14.0 | 14.0 | 14.0 | 0.0 | 0.0 | 60.0 | 8.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 258 | 450 | 192 | 0 |
| 8561 | 8561 | 3 | 8.0 | 8.0 | 8.0 | 8.0 | 0.0 | 0.0 | 1463.0 | NaN | ... | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 160 | 447 | 287 | 0 |
5 rows × 481 columns
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler, StandardScaler, QuantileTransformer, KBinsDiscretizer, PowerTransformer
from sklearn.decomposition import PCA, TruncatedSVD
from sklearn.linear_model import LogisticRegression
from sklearn.feature_selection import RFE
from sklearn.model_selection import cross_val_score, RepeatedStratifiedKFold
from sklearn.metrics import matthews_corrcoef, cohen_kappa_score, balanced_accuracy_score, precision_recall_curve, roc_curve, class_likelihood_ratios, det_curve, roc_auc_score
import matplotlib.pyplot as plt
start_timer()
# Define a class to impute missing values with zeros
class ZeroImputer:
def __init__(self):
pass
def fit(self, X, y=None):
return self
def transform(self, X):
return np.nan_to_num(X)
def get_pipelines(model):
pipelines = list()
# Add an imputer to handle missing values
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', MinMaxScaler()), ('m', model)])
pipelines.append(('norm', p))
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', StandardScaler()), ('m', model)])
pipelines.append(('std', p))
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', QuantileTransformer(n_quantiles=100, output_distribution='normal')), ('m', model)])
pipelines.append(('quan', p))
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', KBinsDiscretizer(n_bins=10, encode='ordinal', strategy='uniform')), ('m', model)])
pipelines.append(('kbins', p))
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', PCA(n_components=7)), ('m', model)])
pipelines.append(('pca', p))
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', TruncatedSVD(n_components=7)), ('m', model)])
pipelines.append(('svd', p))
# Alto custo computacional, será utilizada separadamente posteriormente
# p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('r', RFE(estimator=LogisticRegression(solver='liblinear'), n_features_to_select=10)), ('m',model)])
# pipelines.append(('RFE', p))
# p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', StandardScaler()), ('r', RFE(estimator=LogisticRegression(solver='liblinear'), n_features_to_select=10)), ('m',model)])
# pipelines.append(('stdRFE', p))
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('p', PowerTransformer()), ('m',model)])
pipelines.append(('power', p))
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', MinMaxScaler((1,2))), ('p', PowerTransformer()), ('m',model)])
pipelines.append(('MinMaxpower', p))
return pipelines
def evaluate_model(X, y, model):
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
scores = cross_val_score(model, X, y, scoring='balanced_accuracy', cv=cv, n_jobs=-1)
return scores
# Split the DataFrame into features (X) and target (y)
X = df.iloc[:, :-1]
y = df.iloc[:, -1]
# Convert X and y to numpy arrays
X = np.array(X)
y = np.array(y)
# Define the model
model = LogisticRegression(class_weight='balanced')
# Get the modeling pipelines
pipelines = get_pipelines(model)
# Evaluate each pipeline
results, names = list(), list()
for name, pipeline in pipelines:
# Evaluate
scores = evaluate_model(X, y, pipeline)
# Summarize
print('>%s: %.3f (%.3f)' % (name, np.mean(scores), np.std(scores)))
# Store
results.append(scores)
names.append(name)
# Plot the result
plt.boxplot(results, labels=names, showmeans=True)
plt.title('Model Performance Comparison')
plt.xlabel('Pipeline')
plt.ylabel('Balanced Accuracy')
plt.xticks(rotation=45)
plt.show()
end_timer()
>norm: 0.629 (0.017) >std: 0.630 (0.015) >quan: 0.633 (0.021) >kbins: 0.618 (0.019) >pca: 0.572 (0.012) >svd: 0.562 (0.047) >power: 0.632 (0.019) >MinMaxpower: 0.616 (0.017)
# CORRIGIR
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler, StandardScaler, QuantileTransformer, KBinsDiscretizer, PowerTransformer
from sklearn.decomposition import PCA, TruncatedSVD
from sklearn.linear_model import LogisticRegression
from sklearn.feature_selection import RFE
from sklearn.model_selection import cross_val_score, RepeatedStratifiedKFold
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc
from scipy.stats import ks_2samp
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore", category=FutureWarning, module="sklearn.linear_model._sag")
warnings.filterwarnings("ignore", category=FutureWarning, message="In version 1.5 onwards, subsample=200_000 will be used by default.*")
class ZeroImputer:
def __init__(self):
pass
def fit(self, X, y=None):
return self
def transform(self, X):
return np.nan_to_num(X)
def get_pipelines(model, imputation_strategies):
pipelines = list()
for strategy in imputation_strategies:
imputer = SimpleImputer(strategy=strategy)
pipeline = Pipeline([
('imputer', imputer),
('s', MinMaxScaler()),
('m', model)
])
pipelines.append((strategy, pipeline))
# Add an imputer to handle missing values
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', MinMaxScaler()), ('m', model)])
pipelines.append(('norm', p))
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', StandardScaler()), ('m', model)])
pipelines.append(('std', p))
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', QuantileTransformer(n_quantiles=100, output_distribution='normal')), ('m', model)])
pipelines.append(('quan', p))
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', KBinsDiscretizer(n_bins=10, encode='ordinal', strategy='uniform')), ('m', model)])
pipelines.append(('kbins', p))
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', PCA(n_components=7)), ('m', model)])
pipelines.append(('pca', p))
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', TruncatedSVD(n_components=7)), ('m', model)])
pipelines.append(('svd', p))
# p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('r', RFE(estimator=LogisticRegression(solver='liblinear'), n_features_to_select=10)), ('m',model)])
# pipelines.append(('RFE', p))
# p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', StandardScaler()), ('r', RFE(estimator=LogisticRegression(solver='liblinear'), n_features_to_select=10)), ('m',model)])
# pipelines.append(('stdRFE', p))
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('p', PowerTransformer()), ('m',model)])
pipelines.append(('power', p))
p = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)), ('s', MinMaxScaler((1,2))), ('p', PowerTransformer()), ('m',model)])
pipelines.append(('MinMaxpower', p))
return pipelines
# Modify the evaluate_model function to return all metrics
def evaluate_model(X, y, model):
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
acc_scores, prec_scores, rec_scores, f1_scores, ks_scores, gini_scores, auc_scores = [], [], [], [], [], [], []
for train_idx, test_idx in cv.split(X, y):
X_train, X_test = X[train_idx], X[test_idx]
y_train, y_test = y[train_idx], y[test_idx]
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
acc_scores.append(accuracy_score(y_test, y_pred))
prec_scores.append(precision_score(y_test, y_pred, zero_division=1.0)) # Set zero_division to 1.0
rec_scores.append(recall_score(y_test, y_pred))
f1_scores.append(f1_score(y_test, y_pred))
# Calculate KS statistic and Gini coefficient using the predicted probabilities
y_prob = model.predict_proba(X_test)[:, 1]
ks_stat, _ = ks_2samp(y_prob[y_test == 0], y_prob[y_test == 1])
gini_coeff = 2 * roc_auc_score(y_test, y_prob) - 1
ks_scores.append(ks_stat)
gini_scores.append(gini_coeff)
auc_scores.append(roc_auc_score(y_test, y_prob))
return {
'accuracy': acc_scores,
'precision': prec_scores,
'recall': rec_scores,
'f1': f1_scores,
'ks': ks_scores,
'gini': gini_scores,
'auc': auc_scores
}
# Split the DataFrame into features (X) and target (y)
X = df.iloc[:, :-1]
y = df.iloc[:, -1]
# Convert X and y to numpy arrays
X = np.array(X)
y = np.array(y)
y = y.ravel()
# Define the solvers and imputer strategies to iterate over
solvers = ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']
imputer_strategies = ['mean', 'median', 'most_frequent', 'constant']
# Initialize a list to store the results for each solver and strategy
all_results = []
raw_results = []
names = []
# Iterate over each imputation strategy
for imputer_strategy in imputer_strategies:
pipelines = get_pipelines(LogisticRegression(class_weight='balanced', max_iter=10000), [imputer_strategy])
# Iterate over each solver and pipeline for the current imputation strategy
for solver in solvers:
for pipeline_strategy, pipeline in pipelines:
# Update the imputer strategy, solver, and pipeline strategy in the result_dict
result_dict = {
'Imputer Strategy': imputer_strategy,
'Solver': solver,
'Pipeline': pipeline_strategy,
}
# Evaluate
metrics = evaluate_model(X, y, pipeline)
# Store the results for each pipeline
result_dict['Accuracy'] = np.mean(metrics['accuracy'])
result_dict['Precision'] = np.mean(metrics['precision'])
result_dict['Recall'] = np.mean(metrics['recall'])
result_dict['F1'] = np.mean(metrics['f1'])
result_dict['KS Statistic'] = np.mean(metrics['ks'])
result_dict['Gini Coefficient'] = np.mean(metrics['gini'])
result_dict['AUC'] = np.mean(metrics['auc'])
# Store the results for plotting
all_results.append(result_dict)
# Store the results for plotting
raw_results.append(metrics)
names.append(pipeline_strategy)
# Create a DataFrame with all the results
df_results = pd.DataFrame(all_results)
# Plot the result
plt.boxplot([m['accuracy'] for m in raw_results], labels=names, showmeans=True)
plt.title('Model Performance Comparison - Accuracy')
plt.xlabel('Pipeline')
plt.ylabel('Accuracy Score')
plt.xticks(rotation=45) # Rotate the labels for better visibility
plt.show()
plt.boxplot([m['f1'] for m in raw_results], labels=names, showmeans=True)
plt.title('Model Performance Comparison - F1')
plt.xlabel('Pipeline')
plt.ylabel('F1 Score')
plt.xticks(rotation=45) # Rotate the labels for better visibility
plt.show()
plt.boxplot([m['ks'] for m in raw_results], labels=names, showmeans=True)
plt.title('Model Performance Comparison - KS')
plt.xlabel('Pipeline')
plt.ylabel('KS Score')
plt.xticks(rotation=45) # Rotate the labels for better visibility
plt.show()
df_results.head(15)
| Imputer Strategy | Solver | Pipeline | Accuracy | Precision | Recall | F1 | KS Statistic | Gini Coefficient | AUC | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | mean | newton-cg | mean | 0.931335 | 0.600000 | 0.002778 | 0.005128 | 0.269583 | 0.071454 | 0.535727 |
| 1 | mean | newton-cg | norm | 0.931160 | 0.600000 | 0.000000 | 0.000000 | 0.276055 | 0.113549 | 0.556775 |
| 2 | mean | newton-cg | std | 0.910081 | 0.026944 | 0.013675 | 0.017743 | 0.251698 | 0.120369 | 0.560184 |
| 3 | mean | newton-cg | quan | 0.907793 | 0.092103 | 0.048291 | 0.061166 | 0.261790 | 0.079012 | 0.539506 |
| 4 | mean | newton-cg | kbins | 0.921145 | 0.083333 | 0.002778 | 0.004762 | 0.259589 | 0.088270 | 0.544135 |
| 5 | mean | newton-cg | pca | 0.601877 | 0.347858 | 0.429274 | 0.090246 | 0.262850 | 0.113502 | 0.556751 |
| 6 | mean | newton-cg | svd | 0.933791 | 0.700000 | 0.016239 | 0.030037 | 0.250993 | 0.149492 | 0.574746 |
| 7 | mean | newton-cg | power | 0.916578 | 0.097910 | 0.032051 | 0.046799 | 0.259335 | 0.130656 | 0.565328 |
| 8 | mean | newton-cg | MinMaxpower | 0.934846 | 0.933333 | 0.008120 | 0.015018 | 0.249254 | 0.116394 | 0.558197 |
| 9 | mean | lbfgs | mean | 0.931335 | 0.600000 | 0.002778 | 0.005128 | 0.269583 | 0.071454 | 0.535727 |
| 10 | mean | lbfgs | norm | 0.931160 | 0.600000 | 0.000000 | 0.000000 | 0.276055 | 0.113549 | 0.556775 |
| 11 | mean | lbfgs | std | 0.910081 | 0.026944 | 0.013675 | 0.017743 | 0.251698 | 0.120369 | 0.560184 |
| 12 | mean | lbfgs | quan | 0.907793 | 0.092103 | 0.048291 | 0.061166 | 0.261790 | 0.079012 | 0.539506 |
| 13 | mean | lbfgs | kbins | 0.921145 | 0.083333 | 0.002778 | 0.004762 | 0.259589 | 0.088270 | 0.544135 |
| 14 | mean | lbfgs | pca | 0.601877 | 0.347858 | 0.429274 | 0.090246 | 0.262850 | 0.113471 | 0.556735 |
# DATA FAKE to test
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.model_selection import RepeatedStratifiedKFold, cross_val_predict
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
from scipy.stats import ks_2samp
# Função para obter pipelines com diferentes estratégias de imputação
def get_pipelines(model, imputation_strategies):
pipelines = list()
for strategy in imputation_strategies:
imputer = SimpleImputer(strategy=strategy)
pipeline = Pipeline([
('imputer', imputer),
('s', MinMaxScaler()),
('m', model)
])
pipelines.append((strategy, pipeline))
return pipelines
# Criar um conjunto de dados fictício
#X, y = make_classification(n_samples=2000, n_features=400, n_informative=350, n_redundant=10, random_state=42)
# Definir estratégias de imputação e solvers para iterar
imputer_strategies = ['mean', 'median', 'most_frequent', 'constant']
solvers = ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']
# Inicializar listas para armazenar resultados
all_results = []
# Iterar sobre cada estratégia de imputação
for imputer_strategy in imputer_strategies:
pipelines = get_pipelines(LogisticRegression(max_iter=10000, solver='liblinear'), [imputer_strategy])
# Iterar sobre cada solver e pipeline para a estratégia de imputação atual
for solver in solvers:
for pipeline_strategy, pipeline in pipelines:
# Inicializar listas para armazenar resultados para cada fold
acc_scores, prec_scores, rec_scores, f1_scores, ks_scores, gini_scores, auc_scores = [], [], [], [], [], [], []
# Realizar a validação cruzada repetida manualmente
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
for train_idx, test_idx in cv.split(X, y):
X_train, X_test = X[train_idx], X[test_idx]
y_train, y_test = y[train_idx], y[test_idx]
# Treinar o modelo
pipeline.fit(X_train, y_train)
# Obter as previsões de probabilidade
y_pred_prob = pipeline.predict_proba(X_test)[:, 1]
# Calcular métricas usando a função calculate_classification_metrics
metrics_df_example = calculate_classification_metrics_one_class(y_test, y_pred_prob, target_class=1)
# Armazenar as métricas para cada fold
acc_scores.append(metrics_df_example.iloc[0, 1])
prec_scores.append(metrics_df_example.iloc[0, 2])
rec_scores.append(metrics_df_example.iloc[0, 3])
f1_scores.append(metrics_df_example.iloc[0, 4])
ks_scores.append(metrics_df_example.iloc[0, 5])
gini_scores.append(metrics_df_example.iloc[0, 6])
auc_scores.append(metrics_df_example.iloc[0, 7])
# Calcular as médias das métricas para todos os folds
result_dict = {
'Imputer Strategy': imputer_strategy,
'Solver': solver,
'Pipeline': pipeline_strategy,
'Accuracy': np.mean(acc_scores),
'Precision': np.mean(prec_scores),
'Recall': np.mean(rec_scores),
'F1': np.mean(f1_scores),
'KS Statistic': np.mean(ks_scores),
'Gini Coefficient': np.mean(gini_scores),
'AUC': np.mean(auc_scores),
}
# Armazenar os resultados para cada pipeline
all_results.append(result_dict)
# Criar um DataFrame com todos os resultados
df_results = pd.DataFrame(all_results)
# Plotar os resultados
plt.boxplot([df_results[df_results['Pipeline'] == p]['Accuracy'] for p in imputer_strategies], labels=imputer_strategies, showmeans=True)
plt.title('Model Performance Comparison - Accuracy')
plt.xlabel('Imputation Strategy')
plt.ylabel('Accuracy Score')
plt.show()
plt.boxplot([df_results[df_results['Pipeline'] == p]['F1'] for p in imputer_strategies], labels=imputer_strategies, showmeans=True)
plt.title('Model Performance Comparison - F1')
plt.xlabel('Imputation Strategy')
plt.ylabel('F1 Score')
plt.show()
plt.boxplot([df_results[df_results['Pipeline'] == p]['KS Statistic'] for p in imputer_strategies], labels=imputer_strategies, showmeans=True)
plt.title('Model Performance Comparison - KS')
plt.xlabel('Imputation Strategy')
plt.ylabel('KS Score')
plt.show()
Boa parte da depuração dos dados foi realizada em etapas previas, dentre elas: • Eliminação de duplicatas. • Abordagens para lidar com valores faltantes. • Tipificação apropriada das variáveis. • Eliminação de erros, por exemplo, o caso de eliminação de variáveis em possível situação de MNAR. Para evitar redundância na discussão, os tópicos supracitados não serão abordados novamente.
Data cleansing was performed in previous steps, including: • Elimination of duplicates. • Approaches to deal with missing values. • Appropriate typing of variables. • Elimination of errors, for example, the case of elimination of variables in a possible situation of MNAR. To avoid redundancy in the discussion, the aforementioned topics will not be addressed again.
Técnicas de Reamostragem de Dados:
Além disso, é relevante utilizar Ponderação de Classes (Class Weighting) em todos os algoritmos de aprendizado de máquina.
#pip install imbalanced-learn
Collecting imbalanced-learn Downloading imbalanced_learn-0.11.0-py3-none-any.whl.metadata (8.3 kB) Requirement already satisfied: numpy>=1.17.3 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from imbalanced-learn) (1.26.2) Requirement already satisfied: scipy>=1.5.0 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from imbalanced-learn) (1.11.4) Requirement already satisfied: scikit-learn>=1.0.2 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from imbalanced-learn) (1.3.2) Requirement already satisfied: joblib>=1.1.1 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from imbalanced-learn) (1.3.2) Requirement already satisfied: threadpoolctl>=2.0.0 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from imbalanced-learn) (3.2.0) Downloading imbalanced_learn-0.11.0-py3-none-any.whl (235 kB) ---------------------------------------- 0.0/235.6 kB ? eta -:--:-- ----- ---------------------------------- 30.7/235.6 kB 1.3 MB/s eta 0:00:01 ---------- ---------------------------- 61.4/235.6 kB 825.8 kB/s eta 0:00:01 --------------- ----------------------- 92.2/235.6 kB 871.5 kB/s eta 0:00:01 ------------------- ------------------ 122.9/235.6 kB 798.9 kB/s eta 0:00:01 ---------------------------- --------- 174.1/235.6 kB 876.1 kB/s eta 0:00:01 --------------------------------- ---- 204.8/235.6 kB 888.4 kB/s eta 0:00:01 -------------------------------------- 235.6/235.6 kB 847.4 kB/s eta 0:00:00 Installing collected packages: imbalanced-learn Successfully installed imbalanced-learn-0.11.0 Note: you may need to restart the kernel to use updated packages.
'''from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import average_precision_score, accuracy_score, f1_score, precision_score, roc_auc_score, balanced_accuracy_score, cohen_kappa_score, matthews_corrcoef, auc, roc_curve, precision_recall_curve
from imblearn.over_sampling import ADASYN, SMOTE
from imblearn.under_sampling import EditedNearestNeighbours, TomekLinks, ClusterCentroids, NearMiss, RepeatedEditedNearestNeighbours
from imblearn.combine import SMOTETomek, SMOTEENN
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Load dataset
# X = df.drop(columns='label')
# y = df['label']
#X = X.fillna(0)
# Split the DataFrame into features (X) and target (y)
X = df.iloc[:, :-1]
y = df.iloc[:, -1]
# Convert X and y to numpy arrays
X = np.array(X)
y = np.array(y)
y = y.ravel()
X = np.nan_to_num(X, nan=0)
# Define a function for evaluating a given model
def evaluate_model(X_train, y_train, X_test, y_test, model, class_weight=None):
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
y_prob = model.predict_proba(X_test)[:, 1]
accuracy = accuracy_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
roc_auc = roc_auc_score(y_test, y_pred)
balanced_f1 = f1_score(y_test, y_pred, average='weighted')
balanced_accuracy = balanced_accuracy_score(y_test, y_pred)
kappa = cohen_kappa_score(y_test, y_pred)
gini = 2 * roc_auc - 1
mcc = matthews_corrcoef(y_test, y_pred)
ks_stat, _ = ks_2samp(y_prob[y_test == 0], y_prob[y_test == 1])
auc_prc = average_precision_score(y_test, y_prob)
return accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc
# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
results_df = pd.DataFrame()
# Original approach
accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc = evaluate_model(X_train, y_train, X_test, y_test, LogisticRegression(solver='liblinear', class_weight='balanced', max_iter=500), class_weight='balanced')
print("Original Approach:")
print("Accuracy: %.2f%%" % (accuracy * 100))
print("F1 Score: %.3f" % f1)
print("Precision: %.3f" % precision)
print("ROC AUC: %.3f" % roc_auc)
print("Balanced F1 Score: %.3f" % balanced_f1)
print("Balanced Accuracy: %.3f" % balanced_accuracy)
print("Kappa: %.3f" % kappa)
print("Gini Coefficient: %.3f" % gini)
print("MCC: %.3f" % mcc)
print("KS Statistic: %.3f" % ks_stat)
print("AUC PRC: %.3f" % auc_prc)
print()
# List of combined sampling methods
combined_methods = [
ADASYN(),
SMOTETomek(),
SMOTEENN(),
SMOTE(sampling_strategy=0.50), # Specify a custom sampling strategy
EditedNearestNeighbours(),
TomekLinks(),
ClusterCentroids(),
NearMiss(),
RepeatedEditedNearestNeighbours()
]
# Dataframe to store the results
results_df = pd.DataFrame(columns=['Resampling Technique', 'Class Weight', 'Accuracy', 'F1 Score', 'Precision', 'ROC AUC', 'Balanced F1', 'Balanced Accuracy', 'Kappa', 'Gini Coefficient', 'MCC', 'KS Statistic', 'AUC PRC', 'Class0 Shape', 'Class1 Shape'])
# Loop through each combined method
for method in combined_methods:
print(f"Evaluating {method.__class__.__name__}:")
# Apply the combined sampling method to the training data
X_resampled, y_resampled = method.fit_resample(X_train, y_train)
# Evaluate the model using the resampled data without class_weight
accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc = evaluate_model(X_resampled, y_resampled, X_test, y_test, LogisticRegression(solver='liblinear', max_iter=800))
print("Without Class Weight:")
print("Accuracy: %.2f%%" % (accuracy * 100))
print("F1 Score: %.3f" % f1)
print("Precision: %.3f" % precision)
print("ROC AUC: %.3f" % roc_auc)
print("Balanced F1 Score: %.3f" % balanced_f1)
print("Balanced Accuracy: %.3f" % balanced_accuracy)
print("Kappa: %.3f" % kappa)
print("Gini Coefficient: %.3f" % gini)
print("MCC: %.3f" % mcc)
print("KS Statistic: %.3f" % ks_stat)
print("AUC PRC: %.3f" % auc_prc)
# Save the results to the dataframe
results_df = results_df.append({
'Resampling Technique': method.__class__.__name__,
'Class Weight': 'Without Class Weight',
'Accuracy': accuracy,
'F1 Score': f1,
'Precision': precision,
'ROC AUC': roc_auc,
'Balanced F1': balanced_f1,
'Balanced Accuracy': balanced_accuracy,
'Kappa': kappa,
'Gini Coefficient': gini,
'MCC': mcc,
'KS Statistic': ks_stat,
'AUC PRC': auc_prc,
'Class0 Shape': X_resampled[y_resampled == 0].shape,
'Class1 Shape': X_resampled[y_resampled == 1].shape
}, ignore_index=True)
# Evaluate the model using the resampled data with class_weight
accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc = evaluate_model(X_resampled, y_resampled, X_test, y_test, LogisticRegression(solver='liblinear', class_weight='balanced', max_iter=800), class_weight='balanced')
print("With Class Weight:")
print("Accuracy: %.2f%%" % (accuracy * 100))
print("F1 Score: %.3f" % f1)
print("Precision: %.3f" % precision)
print("ROC AUC: %.3f" % roc_auc)
print("Balanced F1 Score: %.3f" % balanced_f1)
print("Balanced Accuracy: %.3f" % balanced_accuracy)
print("Kappa: %.3f" % kappa)
print("Gini Coefficient: %.3f" % gini)
print("MCC: %.3f" % mcc)
print("KS Statistic: %.3f" % ks_stat)
print("AUC PRC: %.3f" % auc_prc)
# Save the results to the dataframe
results_df = results_df.concat({
'Resampling Technique': method.__class__.__name__,
'Class Weight': 'With Class Weight',
'Accuracy': accuracy,
'F1 Score': f1,
'Precision': precision,
'ROC AUC': roc_auc,
'Balanced F1': balanced_f1,
'Balanced Accuracy': balanced_accuracy,
'Kappa': kappa,
'Gini Coefficient': gini,
'MCC': mcc,
'KS Statistic': ks_stat,
'AUC PRC': auc_prc,
'Class0 Shape': X_resampled[y_resampled == 0].shape,
'Class1 Shape': X_resampled[y_resampled == 1].shape
}, ignore_index=True)
print()
# Save the results dataframe to a CSV file
results_df.to_csv(f'{files_directory}/resampling_results.csv', index=False)'''
Original Approach: Accuracy: 58.41% F1 Score: 0.133 Precision: 0.075 ROC AUC: 0.594 Balanced F1 Score: 0.695 Balanced Accuracy: 0.594 Kappa: 0.043 Gini Coefficient: 0.188 MCC: 0.085 KS Statistic: 0.215 AUC PRC: 0.081 Evaluating ADASYN: Without Class Weight: Accuracy: 60.26% F1 Score: 0.140 Precision: 0.079 ROC AUC: 0.608 Balanced F1 Score: 0.710 Balanced Accuracy: 0.608 Kappa: 0.052 Gini Coefficient: 0.217 MCC: 0.099 KS Statistic: 0.223 AUC PRC: 0.079
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) ~\AppData\Local\Temp\ipykernel_18796\3470339331.py in ?() 104 print("KS Statistic: %.3f" % ks_stat) 105 print("AUC PRC: %.3f" % auc_prc) 106 107 # Save the results to the dataframe --> 108 results_df = results_df.append({ 109 'Resampling Technique': method.__class__.__name__, 110 'Class Weight': 'Without Class Weight', 111 'Accuracy': accuracy, c:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\pandas\core\generic.py in ?(self, name) 6200 and name not in self._accessors 6201 and self._info_axis._can_hold_identifiers_and_holds_name(name) 6202 ): 6203 return self[name] -> 6204 return object.__getattribute__(self, name) AttributeError: 'DataFrame' object has no attribute 'append'
| Resampling Technique | Class Weight | Accuracy | F1 Score | Precision | ROC AUC | Balanced F1 Score | Balanced Accuracy | Kappa | Gini Coefficient | MCC | KS Statistic | AUC PRC | Sample shape X class0 | Sample shape X class1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Original | With Class Weight | 0.686842 | 0.131387 | 0.080357 | 0.534930 | 0.764410 | 0.534930 | 0.026690 | 0.069859 | 0.037987 | 0.199437 | 0.082904 | (1419, 480) | (99, 480) |
| 1 | ADASYN | With Class Weight | 0.713158 | 0.141732 | 0.088235 | 0.549014 | 0.782668 | 0.549014 | 0.040315 | 0.098028 | 0.054842 | 0.142535 | 0.070891 | (1419, 480) | (1461, 480) |
| 2 | SMOTETomek | With Class Weight | 0.705263 | 0.125000 | 0.077670 | 0.526197 | 0.776878 | 0.526197 | 0.021384 | 0.052394 | 0.029222 | 0.145352 | 0.071110 | (1379, 480) | (1379, 480) |
| 3 | SMOTEENN | With Class Weight | 0.671053 | 0.125874 | 0.076271 | 0.526479 | 0.753227 | 0.526479 | 0.019405 | 0.052958 | 0.028374 | 0.162817 | 0.075604 | (829, 480) | (1221, 480) |
| 4 | SMOTE | With Class Weight | 0.707895 | 0.125984 | 0.078431 | 0.527606 | 0.778680 | 0.527606 | 0.022706 | 0.055211 | 0.030888 | 0.148169 | 0.074353 | (1419, 480) | (709, 480) |
| 5 | EditedNearestNeighbours | With Class Weight | 0.697368 | 0.135338 | 0.083333 | 0.540563 | 0.771768 | 0.540563 | 0.031901 | 0.081127 | 0.044591 | 0.139718 | 0.069284 | (1168, 480) | (99, 480) |
| 6 | TomekLinks | With Class Weight | 0.694737 | 0.134328 | 0.082569 | 0.539155 | 0.769935 | 0.539155 | 0.030570 | 0.078310 | 0.042924 | 0.182535 | 0.076210 | (1384, 480) | (99, 480) |
| 7 | ClusterCentroids | With Class Weight | 0.442105 | 0.123967 | 0.069124 | 0.515493 | 0.560025 | 0.515493 | 0.006781 | 0.030986 | 0.015521 | 0.120563 | 0.066399 | (99, 480) | (99, 480) |
| 8 | NearMiss | With Class Weight | 0.239474 | 0.132132 | 0.071429 | 0.537183 | 0.310616 | 0.537183 | 0.011877 | 0.074366 | 0.047046 | 0.154930 | 0.071762 | (99, 480) | (99, 480) |
| 9 | RepeatedEditedNearestNeighbours | With Class Weight | 0.681579 | 0.116788 | 0.071429 | 0.513521 | 0.760450 | 0.513521 | 0.010331 | 0.027042 | 0.014705 | 0.116056 | 0.065356 | (1030, 480) | (99, 480) |
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import (
accuracy_score,
f1_score,
precision_score,
roc_auc_score,
balanced_accuracy_score,
cohen_kappa_score,
matthews_corrcoef,
average_precision_score,
)
from imblearn.over_sampling import (
ADASYN,
SMOTE,
)
from imblearn.under_sampling import (
EditedNearestNeighbours,
TomekLinks,
ClusterCentroids,
NearMiss,
RepeatedEditedNearestNeighbours,
RandomUnderSampler,
)
from imblearn.combine import SMOTETomek, SMOTEENN
# Utilizar o pacote imblearn para avaliar várias técnicas de reamostragem
# Use imblearn's to evaluate several resampling techniques
# Load dataset
# X = df.drop(columns='label')
# y = df['label']
#X = X.fillna(0)
# Split the DataFrame into features (X) and target (y)
X = df.iloc[:, :-1]
y = df.iloc[:, -1]
# Convert X and y to numpy arrays
X = np.array(X)
y = np.array(y)
y = y.ravel()
X = np.nan_to_num(X, nan=0)
# Define a function for evaluating a given model
def evaluate_model(X_train, y_train, X_test, y_test, model, class_weight=None):
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
y_prob = model.predict_proba(X_test)[:, 1]
accuracy = accuracy_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
roc_auc = roc_auc_score(y_test, y_pred)
balanced_f1 = f1_score(y_test, y_pred, average='weighted')
balanced_accuracy = balanced_accuracy_score(y_test, y_pred)
kappa = cohen_kappa_score(y_test, y_pred)
gini = 2 * roc_auc_score(y_test, y_pred) - 1
mcc = matthews_corrcoef(y_test, y_pred)
ks_stat, _ = ks_2samp(y_prob[y_test == 0], y_prob[y_test == 1])
auc_prc = average_precision_score(y_test, y_prob)
return accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc
# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y
)
# Initialize an empty list to store the DataFrames
results_dfs = []
# Original approach without class weight
accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc = evaluate_model(
X_train, y_train, X_test, y_test, LogisticRegression(solver='liblinear', max_iter=500, class_weight=None),
class_weight=None
)
# Create a DataFrame for the original approach
results_df = pd.DataFrame({
'Resampling Technique': ['Original'],
'Class Weight': ['None'],
'Accuracy': [accuracy],
'F1 Score': [f1],
'Precision': [precision],
'ROC AUC': [roc_auc],
'Balanced F1 Score': [balanced_f1],
'Balanced Accuracy': [balanced_accuracy],
'Kappa': [kappa],
'Gini Coefficient': [gini],
'MCC': [mcc],
'KS Statistic': [ks_stat],
'AUC PRC': [auc_prc],
'Sample shape X class0': [X_train[y_train == 0].shape[0]],
'Sample shape X class1': [X_train[y_train == 1].shape[0]],
})
# Append the DataFrame to the list
results_dfs.append(results_df)
# Original approach with class weight
accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc = evaluate_model(
X_train, y_train, X_test, y_test, LogisticRegression(solver='liblinear', max_iter=500, class_weight='balanced'),
class_weight='balanced'
)
# Create a DataFrame for the original approach
results_df = pd.DataFrame({
'Resampling Technique': ['Original'],
'Class Weight': ['balanced'],
'Accuracy': [accuracy],
'F1 Score': [f1],
'Precision': [precision],
'ROC AUC': [roc_auc],
'Balanced F1 Score': [balanced_f1],
'Balanced Accuracy': [balanced_accuracy],
'Kappa': [kappa],
'Gini Coefficient': [gini],
'MCC': [mcc],
'KS Statistic': [ks_stat],
'AUC PRC': [auc_prc],
'Sample shape X class0': [X_train[y_train == 0].shape[0]],
'Sample shape X class1': [X_train[y_train == 1].shape[0]],
})
# Append the DataFrame to the list
results_dfs.append(results_df)
# List of combined sampling methods
combined_methods = [
ADASYN(),
SMOTETomek(),
SMOTEENN(),
SMOTE(sampling_strategy=0.90),
EditedNearestNeighbours(),
TomekLinks(),
ClusterCentroids(),
NearMiss(),
RepeatedEditedNearestNeighbours(sampling_strategy='auto'),
RandomUnderSampler(sampling_strategy='majority'), # Undersampling majority class
]
# Loop through each combined method
for method in combined_methods:
# Evaluate with class_weight=None
print(f"Evaluating {method.__class__.__name__}... Class Weight: None")
X_resampled, y_resampled = method.fit_resample(X_train, y_train)
accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc = evaluate_model(
X_resampled, y_resampled, X_test, y_test, LogisticRegression(solver='liblinear', max_iter=500, class_weight=None),
class_weight=None
)
results_df = pd.DataFrame({
'Resampling Technique': [method.__class__.__name__],
'Class Weight': [None],
'Accuracy': [accuracy],
'F1 Score': [f1],
'Precision': [precision],
'ROC AUC': [roc_auc],
'Balanced F1 Score': [balanced_f1],
'Balanced Accuracy': [balanced_accuracy],
'Kappa': [kappa],
'Gini Coefficient': [gini],
'MCC': [mcc],
'KS Statistic': [ks_stat],
'AUC PRC': [auc_prc],
'Sample shape X class0': [X_resampled[y_resampled == 0].shape[0]],
'Sample shape X class1': [X_resampled[y_resampled == 1].shape[0]],
})
results_dfs.append(results_df)
# Evaluate with class_weight='balanced'
print(f"Evaluating {method.__class__.__name__}... Class Weight: balanced")
X_resampled, y_resampled = method.fit_resample(X_train, y_train)
accuracy, f1, precision, roc_auc, balanced_f1, balanced_accuracy, kappa, gini, mcc, ks_stat, auc_prc = evaluate_model(
X_resampled, y_resampled, X_test, y_test, LogisticRegression(solver='liblinear', max_iter=500, class_weight='balanced'),
class_weight='balanced'
)
results_df = pd.DataFrame({
'Resampling Technique': [method.__class__.__name__],
'Class Weight': ['balanced'],
'Accuracy': [accuracy],
'F1 Score': [f1],
'Precision': [precision],
'ROC AUC': [roc_auc],
'Balanced F1 Score': [balanced_f1],
'Balanced Accuracy': [balanced_accuracy],
'Kappa': [kappa],
'Gini Coefficient': [gini],
'MCC': [mcc],
'KS Statistic': [ks_stat],
'AUC PRC': [auc_prc],
'Sample shape X class0': [X_resampled[y_resampled == 0].shape[0]],
'Sample shape X class1': [X_resampled[y_resampled == 1].shape[0]],
})
results_dfs.append(results_df)
# Concatenate all DataFrames in the list
final_results_df = pd.concat(results_dfs, ignore_index=True)
# Display the final results DataFrame
display(round(final_results_df,2))
round(final_results_df,2).to_csv(f'{files_directory}/resampling_results.csv', index=False)
end_timer()
Evaluating ADASYN... Class Weight: None Evaluating ADASYN... Class Weight: balanced Evaluating SMOTETomek... Class Weight: None Evaluating SMOTETomek... Class Weight: balanced Evaluating SMOTEENN... Class Weight: None Evaluating SMOTEENN... Class Weight: balanced Evaluating SMOTE... Class Weight: None Evaluating SMOTE... Class Weight: balanced Evaluating EditedNearestNeighbours... Class Weight: None Evaluating EditedNearestNeighbours... Class Weight: balanced Evaluating TomekLinks... Class Weight: None Evaluating TomekLinks... Class Weight: balanced Evaluating ClusterCentroids... Class Weight: None Evaluating ClusterCentroids... Class Weight: balanced Evaluating NearMiss... Class Weight: None Evaluating NearMiss... Class Weight: balanced Evaluating RepeatedEditedNearestNeighbours... Class Weight: None Evaluating RepeatedEditedNearestNeighbours... Class Weight: balanced Evaluating RandomUnderSampler... Class Weight: None Evaluating RandomUnderSampler... Class Weight: balanced
| Resampling Technique | Class Weight | Accuracy | F1 Score | Precision | ROC AUC | Balanced F1 Score | Balanced Accuracy | Kappa | Gini Coefficient | MCC | KS Statistic | AUC PRC | Sample shape X class0 | Sample shape X class1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Original | None | 0.95 | 0.01 | 0.20 | 0.50 | 0.92 | 0.50 | 0.01 | 0.00 | 0.02 | 0.19 | 0.08 | 14386 | 800 |
| 1 | Original | balanced | 0.58 | 0.13 | 0.07 | 0.59 | 0.70 | 0.59 | 0.04 | 0.19 | 0.08 | 0.22 | 0.08 | 14386 | 800 |
| 2 | ADASYN | None | 0.59 | 0.14 | 0.08 | 0.61 | 0.70 | 0.61 | 0.05 | 0.23 | 0.10 | 0.24 | 0.08 | 14386 | 14184 |
| 3 | ADASYN | balanced | 0.59 | 0.14 | 0.08 | 0.60 | 0.70 | 0.60 | 0.05 | 0.20 | 0.09 | 0.22 | 0.08 | 14386 | 14184 |
| 4 | SMOTETomek | None | 0.60 | 0.14 | 0.08 | 0.61 | 0.71 | 0.61 | 0.05 | 0.21 | 0.10 | 0.24 | 0.08 | 14129 | 14129 |
| 5 | SMOTETomek | balanced | 0.60 | 0.14 | 0.08 | 0.60 | 0.71 | 0.60 | 0.05 | 0.20 | 0.09 | 0.22 | 0.08 | 14160 | 14160 |
| 6 | SMOTEENN | None | 0.48 | 0.13 | 0.07 | 0.59 | 0.61 | 0.59 | 0.03 | 0.19 | 0.08 | 0.22 | 0.08 | 9201 | 12942 |
| 7 | SMOTEENN | balanced | 0.58 | 0.14 | 0.08 | 0.60 | 0.69 | 0.60 | 0.05 | 0.21 | 0.09 | 0.24 | 0.08 | 9196 | 12995 |
| 8 | SMOTE | None | 0.64 | 0.14 | 0.08 | 0.60 | 0.74 | 0.60 | 0.05 | 0.21 | 0.10 | 0.23 | 0.08 | 14386 | 12947 |
| 9 | SMOTE | balanced | 0.59 | 0.14 | 0.08 | 0.60 | 0.70 | 0.60 | 0.05 | 0.21 | 0.09 | 0.23 | 0.08 | 14386 | 12947 |
| 10 | EditedNearestNeighbours | None | 0.95 | 0.01 | 0.10 | 0.50 | 0.92 | 0.50 | 0.00 | 0.00 | 0.01 | 0.20 | 0.08 | 12328 | 800 |
| 11 | EditedNearestNeighbours | balanced | 0.58 | 0.14 | 0.08 | 0.60 | 0.69 | 0.60 | 0.05 | 0.21 | 0.09 | 0.21 | 0.08 | 12328 | 800 |
| 12 | TomekLinks | None | 0.95 | 0.01 | 0.50 | 0.50 | 0.92 | 0.50 | 0.01 | 0.00 | 0.05 | 0.18 | 0.08 | 14081 | 800 |
| 13 | TomekLinks | balanced | 0.58 | 0.13 | 0.07 | 0.59 | 0.70 | 0.59 | 0.04 | 0.18 | 0.08 | 0.22 | 0.08 | 14081 | 800 |
| 14 | ClusterCentroids | None | 0.27 | 0.11 | 0.06 | 0.53 | 0.36 | 0.53 | 0.01 | 0.06 | 0.03 | 0.19 | 0.07 | 800 | 800 |
| 15 | ClusterCentroids | balanced | 0.27 | 0.11 | 0.06 | 0.54 | 0.36 | 0.54 | 0.01 | 0.09 | 0.05 | 0.20 | 0.07 | 800 | 800 |
| 16 | NearMiss | None | 0.14 | 0.08 | 0.04 | 0.42 | 0.18 | 0.42 | -0.02 | -0.16 | -0.11 | 0.22 | 0.04 | 800 | 800 |
| 17 | NearMiss | balanced | 0.14 | 0.08 | 0.04 | 0.42 | 0.18 | 0.42 | -0.02 | -0.16 | -0.11 | 0.22 | 0.04 | 800 | 800 |
| 18 | RepeatedEditedNearestNeighbours | None | 0.94 | 0.02 | 0.10 | 0.50 | 0.92 | 0.50 | 0.01 | 0.00 | 0.01 | 0.22 | 0.08 | 11309 | 800 |
| 19 | RepeatedEditedNearestNeighbours | balanced | 0.57 | 0.14 | 0.08 | 0.61 | 0.69 | 0.61 | 0.05 | 0.21 | 0.09 | 0.22 | 0.08 | 11309 | 800 |
| 20 | RandomUnderSampler | None | 0.51 | 0.13 | 0.07 | 0.59 | 0.63 | 0.59 | 0.04 | 0.18 | 0.08 | 0.21 | 0.07 | 800 | 800 |
| 21 | RandomUnderSampler | balanced | 0.52 | 0.12 | 0.07 | 0.58 | 0.64 | 0.58 | 0.03 | 0.15 | 0.07 | 0.18 | 0.07 | 800 | 800 |
round(final_results_df,2).to_csv(f'{files_directory}/resampling_results.csv', index=False)
# Segmentos das variaveis # Features segments
sector_columns = {
'Bank account': [f'K{i:05d}' for i in range(1, 27)],
'Credit Card Account': [f'K{i:05d}' for i in range(27, 84)],
'Checking account': [f'K{i:05d}' for i in range(84, 209)],
'Saving account': ['K00209'],
'Pre approved credit': [f'K{i:05d}' for i in range(210, 212)],
'Financial Instability': [f'K{i:05d}' for i in range(212, 269)],
'Assets': [f'K{i:05d}' for i in range(269, 281)],
'Income': [f'K{i:05d}' for i in range(281, 370)],
'Expenses': [f'K{i:05d}' for i in range(370, 466)],
'Insurance': [f'K{i:05d}' for i in range(466, 484)],
'Liabilities': [f'K{i:05d}' for i in range(484, 541)]
}
# Print the generated lists for each sector
for sector, columns in sector_columns.items():
print(f"{sector}: {columns}")
Bank account: ['K00001', 'K00002', 'K00003', 'K00004', 'K00005', 'K00006', 'K00007', 'K00008', 'K00009', 'K00010', 'K00011', 'K00012', 'K00013', 'K00014', 'K00015', 'K00016', 'K00017', 'K00018', 'K00019', 'K00020', 'K00021', 'K00022', 'K00023', 'K00024', 'K00025', 'K00026'] Credit Card Account: ['K00027', 'K00028', 'K00029', 'K00030', 'K00031', 'K00032', 'K00033', 'K00034', 'K00035', 'K00036', 'K00037', 'K00038', 'K00039', 'K00040', 'K00041', 'K00042', 'K00043', 'K00044', 'K00045', 'K00046', 'K00047', 'K00048', 'K00049', 'K00050', 'K00051', 'K00052', 'K00053', 'K00054', 'K00055', 'K00056', 'K00057', 'K00058', 'K00059', 'K00060', 'K00061', 'K00062', 'K00063', 'K00064', 'K00065', 'K00066', 'K00067', 'K00068', 'K00069', 'K00070', 'K00071', 'K00072', 'K00073', 'K00074', 'K00075', 'K00076', 'K00077', 'K00078', 'K00079', 'K00080', 'K00081', 'K00082', 'K00083'] Checking account: ['K00084', 'K00085', 'K00086', 'K00087', 'K00088', 'K00089', 'K00090', 'K00091', 'K00092', 'K00093', 'K00094', 'K00095', 'K00096', 'K00097', 'K00098', 'K00099', 'K00100', 'K00101', 'K00102', 'K00103', 'K00104', 'K00105', 'K00106', 'K00107', 'K00108', 'K00109', 'K00110', 'K00111', 'K00112', 'K00113', 'K00114', 'K00115', 'K00116', 'K00117', 'K00118', 'K00119', 'K00120', 'K00121', 'K00122', 'K00123', 'K00124', 'K00125', 'K00126', 'K00127', 'K00128', 'K00129', 'K00130', 'K00131', 'K00132', 'K00133', 'K00134', 'K00135', 'K00136', 'K00137', 'K00138', 'K00139', 'K00140', 'K00141', 'K00142', 'K00143', 'K00144', 'K00145', 'K00146', 'K00147', 'K00148', 'K00149', 'K00150', 'K00151', 'K00152', 'K00153', 'K00154', 'K00155', 'K00156', 'K00157', 'K00158', 'K00159', 'K00160', 'K00161', 'K00162', 'K00163', 'K00164', 'K00165', 'K00166', 'K00167', 'K00168', 'K00169', 'K00170', 'K00171', 'K00172', 'K00173', 'K00174', 'K00175', 'K00176', 'K00177', 'K00178', 'K00179', 'K00180', 'K00181', 'K00182', 'K00183', 'K00184', 'K00185', 'K00186', 'K00187', 'K00188', 'K00189', 'K00190', 'K00191', 'K00192', 'K00193', 'K00194', 'K00195', 'K00196', 'K00197', 'K00198', 'K00199', 'K00200', 'K00201', 'K00202', 'K00203', 'K00204', 'K00205', 'K00206', 'K00207', 'K00208'] Saving account: ['K00209'] Pre approved credit: ['K00210', 'K00211'] Financial Instability: ['K00212', 'K00213', 'K00214', 'K00215', 'K00216', 'K00217', 'K00218', 'K00219', 'K00220', 'K00221', 'K00222', 'K00223', 'K00224', 'K00225', 'K00226', 'K00227', 'K00228', 'K00229', 'K00230', 'K00231', 'K00232', 'K00233', 'K00234', 'K00235', 'K00236', 'K00237', 'K00238', 'K00239', 'K00240', 'K00241', 'K00242', 'K00243', 'K00244', 'K00245', 'K00246', 'K00247', 'K00248', 'K00249', 'K00250', 'K00251', 'K00252', 'K00253', 'K00254', 'K00255', 'K00256', 'K00257', 'K00258', 'K00259', 'K00260', 'K00261', 'K00262', 'K00263', 'K00264', 'K00265', 'K00266', 'K00267', 'K00268'] Assets: ['K00269', 'K00270', 'K00271', 'K00272', 'K00273', 'K00274', 'K00275', 'K00276', 'K00277', 'K00278', 'K00279', 'K00280'] Income: ['K00281', 'K00282', 'K00283', 'K00284', 'K00285', 'K00286', 'K00287', 'K00288', 'K00289', 'K00290', 'K00291', 'K00292', 'K00293', 'K00294', 'K00295', 'K00296', 'K00297', 'K00298', 'K00299', 'K00300', 'K00301', 'K00302', 'K00303', 'K00304', 'K00305', 'K00306', 'K00307', 'K00308', 'K00309', 'K00310', 'K00311', 'K00312', 'K00313', 'K00314', 'K00315', 'K00316', 'K00317', 'K00318', 'K00319', 'K00320', 'K00321', 'K00322', 'K00323', 'K00324', 'K00325', 'K00326', 'K00327', 'K00328', 'K00329', 'K00330', 'K00331', 'K00332', 'K00333', 'K00334', 'K00335', 'K00336', 'K00337', 'K00338', 'K00339', 'K00340', 'K00341', 'K00342', 'K00343', 'K00344', 'K00345', 'K00346', 'K00347', 'K00348', 'K00349', 'K00350', 'K00351', 'K00352', 'K00353', 'K00354', 'K00355', 'K00356', 'K00357', 'K00358', 'K00359', 'K00360', 'K00361', 'K00362', 'K00363', 'K00364', 'K00365', 'K00366', 'K00367', 'K00368', 'K00369'] Expenses: ['K00370', 'K00371', 'K00372', 'K00373', 'K00374', 'K00375', 'K00376', 'K00377', 'K00378', 'K00379', 'K00380', 'K00381', 'K00382', 'K00383', 'K00384', 'K00385', 'K00386', 'K00387', 'K00388', 'K00389', 'K00390', 'K00391', 'K00392', 'K00393', 'K00394', 'K00395', 'K00396', 'K00397', 'K00398', 'K00399', 'K00400', 'K00401', 'K00402', 'K00403', 'K00404', 'K00405', 'K00406', 'K00407', 'K00408', 'K00409', 'K00410', 'K00411', 'K00412', 'K00413', 'K00414', 'K00415', 'K00416', 'K00417', 'K00418', 'K00419', 'K00420', 'K00421', 'K00422', 'K00423', 'K00424', 'K00425', 'K00426', 'K00427', 'K00428', 'K00429', 'K00430', 'K00431', 'K00432', 'K00433', 'K00434', 'K00435', 'K00436', 'K00437', 'K00438', 'K00439', 'K00440', 'K00441', 'K00442', 'K00443', 'K00444', 'K00445', 'K00446', 'K00447', 'K00448', 'K00449', 'K00450', 'K00451', 'K00452', 'K00453', 'K00454', 'K00455', 'K00456', 'K00457', 'K00458', 'K00459', 'K00460', 'K00461', 'K00462', 'K00463', 'K00464', 'K00465'] Insurance: ['K00466', 'K00467', 'K00468', 'K00469', 'K00470', 'K00471', 'K00472', 'K00473', 'K00474', 'K00475', 'K00476', 'K00477', 'K00478', 'K00479', 'K00480', 'K00481', 'K00482', 'K00483'] Liabilities: ['K00484', 'K00485', 'K00486', 'K00487', 'K00488', 'K00489', 'K00490', 'K00491', 'K00492', 'K00493', 'K00494', 'K00495', 'K00496', 'K00497', 'K00498', 'K00499', 'K00500', 'K00501', 'K00502', 'K00503', 'K00504', 'K00505', 'K00506', 'K00507', 'K00508', 'K00509', 'K00510', 'K00511', 'K00512', 'K00513', 'K00514', 'K00515', 'K00516', 'K00517', 'K00518', 'K00519', 'K00520', 'K00521', 'K00522', 'K00523', 'K00524', 'K00525', 'K00526', 'K00527', 'K00528', 'K00529', 'K00530', 'K00531', 'K00532', 'K00533', 'K00534', 'K00535', 'K00536', 'K00537', 'K00538', 'K00539', 'K00540']
sector_columns_binned = {
'Bank account': [f'K{i:05d}_binned' for i in range(1, 27)],
'Credit Card Account': [f'K{i:05d}_binned' for i in range(27, 84)],
'Checking account': [f'K{i:05d}_binned' for i in range(84, 209)],
'Saving account': ['K00209_binned'],
'Pre approved credit': [f'K{i:05d}_binned' for i in range(210, 212)],
'Financial Instability': [f'K{i:05d}_binned' for i in range(212, 269)],
'Assets': [f'K{i:05d}_binned' for i in range(269, 281)],
'Income': [f'K{i:05d}_binned' for i in range(281, 370)],
'Expenses': [f'K{i:05d}_binned' for i in range(370, 466)],
'Insurance': [f'K{i:05d}_binned' for i in range(466, 484)],
'Liabilities': [f'K{i:05d}_binned' for i in range(484, 541)]
}
# Print the generated lists for each sector
for sector, columns in sector_columns_binned.items():
print(f"{sector}: {columns}")
Bank account: ['K00001_binned', 'K00002_binned', 'K00003_binned', 'K00004_binned', 'K00005_binned', 'K00006_binned', 'K00007_binned', 'K00008_binned', 'K00009_binned', 'K00010_binned', 'K00011_binned', 'K00012_binned', 'K00013_binned', 'K00014_binned', 'K00015_binned', 'K00016_binned', 'K00017_binned', 'K00018_binned', 'K00019_binned', 'K00020_binned', 'K00021_binned', 'K00022_binned', 'K00023_binned', 'K00024_binned', 'K00025_binned', 'K00026_binned'] Credit Card Account: ['K00027_binned', 'K00028_binned', 'K00029_binned', 'K00030_binned', 'K00031_binned', 'K00032_binned', 'K00033_binned', 'K00034_binned', 'K00035_binned', 'K00036_binned', 'K00037_binned', 'K00038_binned', 'K00039_binned', 'K00040_binned', 'K00041_binned', 'K00042_binned', 'K00043_binned', 'K00044_binned', 'K00045_binned', 'K00046_binned', 'K00047_binned', 'K00048_binned', 'K00049_binned', 'K00050_binned', 'K00051_binned', 'K00052_binned', 'K00053_binned', 'K00054_binned', 'K00055_binned', 'K00056_binned', 'K00057_binned', 'K00058_binned', 'K00059_binned', 'K00060_binned', 'K00061_binned', 'K00062_binned', 'K00063_binned', 'K00064_binned', 'K00065_binned', 'K00066_binned', 'K00067_binned', 'K00068_binned', 'K00069_binned', 'K00070_binned', 'K00071_binned', 'K00072_binned', 'K00073_binned', 'K00074_binned', 'K00075_binned', 'K00076_binned', 'K00077_binned', 'K00078_binned', 'K00079_binned', 'K00080_binned', 'K00081_binned', 'K00082_binned', 'K00083_binned'] Checking account: ['K00084_binned', 'K00085_binned', 'K00086_binned', 'K00087_binned', 'K00088_binned', 'K00089_binned', 'K00090_binned', 'K00091_binned', 'K00092_binned', 'K00093_binned', 'K00094_binned', 'K00095_binned', 'K00096_binned', 'K00097_binned', 'K00098_binned', 'K00099_binned', 'K00100_binned', 'K00101_binned', 'K00102_binned', 'K00103_binned', 'K00104_binned', 'K00105_binned', 'K00106_binned', 'K00107_binned', 'K00108_binned', 'K00109_binned', 'K00110_binned', 'K00111_binned', 'K00112_binned', 'K00113_binned', 'K00114_binned', 'K00115_binned', 'K00116_binned', 'K00117_binned', 'K00118_binned', 'K00119_binned', 'K00120_binned', 'K00121_binned', 'K00122_binned', 'K00123_binned', 'K00124_binned', 'K00125_binned', 'K00126_binned', 'K00127_binned', 'K00128_binned', 'K00129_binned', 'K00130_binned', 'K00131_binned', 'K00132_binned', 'K00133_binned', 'K00134_binned', 'K00135_binned', 'K00136_binned', 'K00137_binned', 'K00138_binned', 'K00139_binned', 'K00140_binned', 'K00141_binned', 'K00142_binned', 'K00143_binned', 'K00144_binned', 'K00145_binned', 'K00146_binned', 'K00147_binned', 'K00148_binned', 'K00149_binned', 'K00150_binned', 'K00151_binned', 'K00152_binned', 'K00153_binned', 'K00154_binned', 'K00155_binned', 'K00156_binned', 'K00157_binned', 'K00158_binned', 'K00159_binned', 'K00160_binned', 'K00161_binned', 'K00162_binned', 'K00163_binned', 'K00164_binned', 'K00165_binned', 'K00166_binned', 'K00167_binned', 'K00168_binned', 'K00169_binned', 'K00170_binned', 'K00171_binned', 'K00172_binned', 'K00173_binned', 'K00174_binned', 'K00175_binned', 'K00176_binned', 'K00177_binned', 'K00178_binned', 'K00179_binned', 'K00180_binned', 'K00181_binned', 'K00182_binned', 'K00183_binned', 'K00184_binned', 'K00185_binned', 'K00186_binned', 'K00187_binned', 'K00188_binned', 'K00189_binned', 'K00190_binned', 'K00191_binned', 'K00192_binned', 'K00193_binned', 'K00194_binned', 'K00195_binned', 'K00196_binned', 'K00197_binned', 'K00198_binned', 'K00199_binned', 'K00200_binned', 'K00201_binned', 'K00202_binned', 'K00203_binned', 'K00204_binned', 'K00205_binned', 'K00206_binned', 'K00207_binned', 'K00208_binned'] Saving account: ['K00209_binned'] Pre approved credit: ['K00210_binned', 'K00211_binned'] Financial Instability: ['K00212_binned', 'K00213_binned', 'K00214_binned', 'K00215_binned', 'K00216_binned', 'K00217_binned', 'K00218_binned', 'K00219_binned', 'K00220_binned', 'K00221_binned', 'K00222_binned', 'K00223_binned', 'K00224_binned', 'K00225_binned', 'K00226_binned', 'K00227_binned', 'K00228_binned', 'K00229_binned', 'K00230_binned', 'K00231_binned', 'K00232_binned', 'K00233_binned', 'K00234_binned', 'K00235_binned', 'K00236_binned', 'K00237_binned', 'K00238_binned', 'K00239_binned', 'K00240_binned', 'K00241_binned', 'K00242_binned', 'K00243_binned', 'K00244_binned', 'K00245_binned', 'K00246_binned', 'K00247_binned', 'K00248_binned', 'K00249_binned', 'K00250_binned', 'K00251_binned', 'K00252_binned', 'K00253_binned', 'K00254_binned', 'K00255_binned', 'K00256_binned', 'K00257_binned', 'K00258_binned', 'K00259_binned', 'K00260_binned', 'K00261_binned', 'K00262_binned', 'K00263_binned', 'K00264_binned', 'K00265_binned', 'K00266_binned', 'K00267_binned', 'K00268_binned'] Assets: ['K00269_binned', 'K00270_binned', 'K00271_binned', 'K00272_binned', 'K00273_binned', 'K00274_binned', 'K00275_binned', 'K00276_binned', 'K00277_binned', 'K00278_binned', 'K00279_binned', 'K00280_binned'] Income: ['K00281_binned', 'K00282_binned', 'K00283_binned', 'K00284_binned', 'K00285_binned', 'K00286_binned', 'K00287_binned', 'K00288_binned', 'K00289_binned', 'K00290_binned', 'K00291_binned', 'K00292_binned', 'K00293_binned', 'K00294_binned', 'K00295_binned', 'K00296_binned', 'K00297_binned', 'K00298_binned', 'K00299_binned', 'K00300_binned', 'K00301_binned', 'K00302_binned', 'K00303_binned', 'K00304_binned', 'K00305_binned', 'K00306_binned', 'K00307_binned', 'K00308_binned', 'K00309_binned', 'K00310_binned', 'K00311_binned', 'K00312_binned', 'K00313_binned', 'K00314_binned', 'K00315_binned', 'K00316_binned', 'K00317_binned', 'K00318_binned', 'K00319_binned', 'K00320_binned', 'K00321_binned', 'K00322_binned', 'K00323_binned', 'K00324_binned', 'K00325_binned', 'K00326_binned', 'K00327_binned', 'K00328_binned', 'K00329_binned', 'K00330_binned', 'K00331_binned', 'K00332_binned', 'K00333_binned', 'K00334_binned', 'K00335_binned', 'K00336_binned', 'K00337_binned', 'K00338_binned', 'K00339_binned', 'K00340_binned', 'K00341_binned', 'K00342_binned', 'K00343_binned', 'K00344_binned', 'K00345_binned', 'K00346_binned', 'K00347_binned', 'K00348_binned', 'K00349_binned', 'K00350_binned', 'K00351_binned', 'K00352_binned', 'K00353_binned', 'K00354_binned', 'K00355_binned', 'K00356_binned', 'K00357_binned', 'K00358_binned', 'K00359_binned', 'K00360_binned', 'K00361_binned', 'K00362_binned', 'K00363_binned', 'K00364_binned', 'K00365_binned', 'K00366_binned', 'K00367_binned', 'K00368_binned', 'K00369_binned'] Expenses: ['K00370_binned', 'K00371_binned', 'K00372_binned', 'K00373_binned', 'K00374_binned', 'K00375_binned', 'K00376_binned', 'K00377_binned', 'K00378_binned', 'K00379_binned', 'K00380_binned', 'K00381_binned', 'K00382_binned', 'K00383_binned', 'K00384_binned', 'K00385_binned', 'K00386_binned', 'K00387_binned', 'K00388_binned', 'K00389_binned', 'K00390_binned', 'K00391_binned', 'K00392_binned', 'K00393_binned', 'K00394_binned', 'K00395_binned', 'K00396_binned', 'K00397_binned', 'K00398_binned', 'K00399_binned', 'K00400_binned', 'K00401_binned', 'K00402_binned', 'K00403_binned', 'K00404_binned', 'K00405_binned', 'K00406_binned', 'K00407_binned', 'K00408_binned', 'K00409_binned', 'K00410_binned', 'K00411_binned', 'K00412_binned', 'K00413_binned', 'K00414_binned', 'K00415_binned', 'K00416_binned', 'K00417_binned', 'K00418_binned', 'K00419_binned', 'K00420_binned', 'K00421_binned', 'K00422_binned', 'K00423_binned', 'K00424_binned', 'K00425_binned', 'K00426_binned', 'K00427_binned', 'K00428_binned', 'K00429_binned', 'K00430_binned', 'K00431_binned', 'K00432_binned', 'K00433_binned', 'K00434_binned', 'K00435_binned', 'K00436_binned', 'K00437_binned', 'K00438_binned', 'K00439_binned', 'K00440_binned', 'K00441_binned', 'K00442_binned', 'K00443_binned', 'K00444_binned', 'K00445_binned', 'K00446_binned', 'K00447_binned', 'K00448_binned', 'K00449_binned', 'K00450_binned', 'K00451_binned', 'K00452_binned', 'K00453_binned', 'K00454_binned', 'K00455_binned', 'K00456_binned', 'K00457_binned', 'K00458_binned', 'K00459_binned', 'K00460_binned', 'K00461_binned', 'K00462_binned', 'K00463_binned', 'K00464_binned', 'K00465_binned'] Insurance: ['K00466_binned', 'K00467_binned', 'K00468_binned', 'K00469_binned', 'K00470_binned', 'K00471_binned', 'K00472_binned', 'K00473_binned', 'K00474_binned', 'K00475_binned', 'K00476_binned', 'K00477_binned', 'K00478_binned', 'K00479_binned', 'K00480_binned', 'K00481_binned', 'K00482_binned', 'K00483_binned'] Liabilities: ['K00484_binned', 'K00485_binned', 'K00486_binned', 'K00487_binned', 'K00488_binned', 'K00489_binned', 'K00490_binned', 'K00491_binned', 'K00492_binned', 'K00493_binned', 'K00494_binned', 'K00495_binned', 'K00496_binned', 'K00497_binned', 'K00498_binned', 'K00499_binned', 'K00500_binned', 'K00501_binned', 'K00502_binned', 'K00503_binned', 'K00504_binned', 'K00505_binned', 'K00506_binned', 'K00507_binned', 'K00508_binned', 'K00509_binned', 'K00510_binned', 'K00511_binned', 'K00512_binned', 'K00513_binned', 'K00514_binned', 'K00515_binned', 'K00516_binned', 'K00517_binned', 'K00518_binned', 'K00519_binned', 'K00520_binned', 'K00521_binned', 'K00522_binned', 'K00523_binned', 'K00524_binned', 'K00525_binned', 'K00526_binned', 'K00527_binned', 'K00528_binned', 'K00529_binned', 'K00530_binned', 'K00531_binned', 'K00532_binned', 'K00533_binned', 'K00534_binned', 'K00535_binned', 'K00536_binned', 'K00537_binned', 'K00538_binned', 'K00539_binned', 'K00540_binned']
# Carrega o dataframe
df = pd.read_csv(f'{files_directory}/df_droped_001.csv')
df
| user_id | bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00013 | ... | K00534 | K00535 | K00536 | K00537 | K00538 | K00540 | count_of_nan | count_of_nan_zero | count_of_zero | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 14.0 | 14.0 | 14.0 | 14.0 | 0.0 | 0.0 | NaN | 0.0 | ... | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 129 | 453 | 324 | 0 |
| 1 | 1 | 1 | 19.0 | 19.0 | 19.0 | 19.0 | 0.0 | 0.0 | 41.0 | 14.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 25 | 375 | 350 | 0 |
| 2 | 2 | 0 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | NaN | 5.0 | ... | 0.0 | 20.0 | 0.0 | 0.0 | 1.0 | 20.0 | 91 | 426 | 335 | 0 |
| 3 | 3 | 2 | 21.0 | 21.0 | 21.0 | 21.0 | 0.0 | 0.0 | 310.0 | NaN | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 180 | 472 | 292 | 0 |
| 4 | 4 | 3 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 257.0 | NaN | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 95 | 382 | 287 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 18978 | 2 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | 95.0 | 14.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 106 | 368 | 262 | 0 |
| 18979 | 18979 | 0 | 8.0 | 8.0 | 8.0 | 8.0 | 0.0 | 0.0 | NaN | 5.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 526 | 26 | 0 |
| 18980 | 18980 | 2 | 16.0 | 16.0 | 16.0 | 16.0 | 0.0 | 0.0 | 217.0 | 7.0 | ... | 1090.0 | 1155.0 | 1.0 | 6.0 | 10.0 | 385.0 | 21 | 291 | 270 | 0 |
| 18981 | 18981 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 2.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 530 | 30 | 0 |
| 18982 | 18982 | 0 | 12.0 | 12.0 | 12.0 | 12.0 | 0.0 | 0.0 | NaN | 11.0 | ... | 32.0 | 42.0 | 1.0 | 2.0 | 3.0 | 14.0 | 30 | 351 | 321 | 0 |
18983 rows × 481 columns
# Normalizar as variaveis # Normalize the variables columns[2:481]
from sklearn.preprocessing import MinMaxScaler
df_norm = df.copy()
df_norm.iloc[:, 2:481] = MinMaxScaler().fit_transform(df_norm.iloc[:, 2:481])
#df_norm = df_norm.fillna(-0.05)
df_norm
| user_id | bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00013 | ... | K00534 | K00535 | K00536 | K00537 | K00538 | K00540 | count_of_nan | count_of_nan_zero | count_of_zero | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0.583333 | 0.636364 | 0.636364 | 0.636364 | 0.0 | 0.0 | NaN | 0.000000 | ... | 0.000000 | 0.000000 | NaN | 0.000000 | 0.000000 | 0.000000 | 0.225743 | 0.733945 | 0.842541 | 0 |
| 1 | 1 | 1 | 0.791667 | 0.863636 | 0.863636 | 0.863636 | 0.0 | 0.0 | 0.028025 | 0.358974 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.019802 | 0.495413 | 0.914365 | 0 |
| 2 | 2 | 0 | 0.541667 | 0.590909 | 0.590909 | 0.590909 | 0.0 | 0.0 | NaN | 0.128205 | ... | 0.000000 | 0.000142 | 0.000000 | 0.000000 | 0.008403 | 0.000426 | 0.150495 | 0.651376 | 0.872928 | 0 |
| 3 | 3 | 2 | 0.875000 | 0.954545 | 0.954545 | 0.954545 | 0.0 | 0.0 | 0.211893 | NaN | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.326733 | 0.792049 | 0.754144 | 0 |
| 4 | 4 | 3 | 0.041667 | 0.045455 | 0.045455 | 0.045455 | 0.0 | 0.0 | 0.175666 | NaN | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.158416 | 0.516820 | 0.740331 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 18978 | 2 | 0.541667 | 0.590909 | 0.590909 | 0.590909 | 0.0 | 0.0 | 0.064935 | 0.358974 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.180198 | 0.474006 | 0.671271 | 0 |
| 18979 | 18979 | 0 | 0.333333 | 0.363636 | 0.363636 | 0.363636 | 0.0 | 0.0 | NaN | 0.128205 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 0.960396 | 0.957187 | 0.019337 | 0 |
| 18980 | 18980 | 2 | 0.666667 | 0.727273 | 0.727273 | 0.727273 | 0.0 | 0.0 | 0.148325 | 0.179487 | ... | 0.031160 | 0.008202 | 0.030303 | 0.085714 | 0.084034 | 0.008202 | 0.011881 | 0.238532 | 0.693370 | 0 |
| 18981 | 18981 | 0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | NaN | 0.051282 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 0.960396 | 0.969419 | 0.030387 | 0 |
| 18982 | 18982 | 0 | 0.500000 | 0.545455 | 0.545455 | 0.545455 | 0.0 | 0.0 | NaN | 0.282051 | ... | 0.000915 | 0.000298 | 0.030303 | 0.028571 | 0.025210 | 0.000298 | 0.029703 | 0.422018 | 0.834254 | 0 |
18983 rows × 481 columns
# Cria um novo DataFrame para armazenar os dados agrupados
# Create a new DataFrame to store binned data
df_bins = df_norm.copy()
start_timer()
# Divide cada coluna em 20 bins e atribui rótulos
# Split features into 20 bins manually and assign labels
num_bins = 20
bin_labels = range(1, num_bins + 1) # Use 20 labels for 20 bins
for sector, columns in sector_columns.items():
valid_columns = [col for col in columns if col in df_bins.columns]
if valid_columns:
for col in valid_columns:
col_values = df_bins[col].to_numpy()
bins = pd.cut(col_values, bins=num_bins, labels=bin_labels, duplicates='drop')
df_bins[f"{col}_binned"] = bins
columns_to_drop = df_bins.columns[2:481]
df_bins = df_bins.drop(columns=columns_to_drop)
# Save the resulting DataFrame with binned columns
df_bins.to_csv("binned_data.csv", index=False)
end_timer()
Tempo de execução: 4.99585223197937 segundos
df_bins['label'] = df['label'].copy()
# For categorical columns, convert NaN to a new category ( NaN as a separate category)
categorical_columns = df_bins.select_dtypes(include='category').columns
# Setitem on a Categorical with a new category (0), set the categories first
for col in categorical_columns:
df_bins[col] = df_bins[col].cat.add_categories([0])
df_bins[categorical_columns] = df_bins[categorical_columns].fillna(0)
df_bins
| user_id | bank | K00001_binned | K00002_binned | K00003_binned | K00004_binned | K00005_binned | K00006_binned | K00007_binned | K00013_binned | ... | K00531_binned | K00532_binned | K00533_binned | K00534_binned | K00535_binned | K00536_binned | K00537_binned | K00538_binned | K00540_binned | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 12 | 13 | 13 | 13 | 1 | 1 | 0 | 1 | ... | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 |
| 1 | 1 | 1 | 16 | 18 | 18 | 18 | 1 | 1 | 1 | 8 | ... | 20 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
| 2 | 2 | 0 | 11 | 12 | 12 | 12 | 1 | 1 | 0 | 3 | ... | 20 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
| 3 | 3 | 2 | 18 | 20 | 20 | 20 | 1 | 1 | 5 | 0 | ... | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
| 4 | 4 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 0 | ... | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 18978 | 2 | 11 | 12 | 12 | 12 | 1 | 1 | 2 | 8 | ... | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
| 18979 | 18979 | 0 | 7 | 8 | 8 | 8 | 1 | 1 | 0 | 3 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 18980 | 18980 | 2 | 14 | 15 | 15 | 15 | 1 | 1 | 3 | 4 | ... | 20 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 0 |
| 18981 | 18981 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 2 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 18982 | 18982 | 0 | 10 | 11 | 11 | 11 | 1 | 1 | 0 | 6 | ... | 20 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
18983 rows × 478 columns
# Plot binned data for each sector
# Create a list of sector names
sectors_binned = list(sector_columns_binned.keys())
start_timer()
# Create a boxplot and stripplot for each sector's columns
plt.figure(figsize=(12, 8))
for sector, columns in sector_columns_binned.items():
valid_columns_binned = [col for col in columns if col in df_bins.columns]
if valid_columns_binned:
plt.figure(figsize=(30, 5))
data_subset = df_bins[valid_columns_binned].copy() # Include 'label' column and valid columns
data_subset['label'] = df['label'].copy()
melted_data = data_subset.melt(id_vars=['label'], value_vars=valid_columns_binned, var_name='Feature')
# plt.subplot(1, 2, 1)
# sns.boxplot(data=melted_data, x='Feature', y='value', hue='label')
plt.subplot(1, 2, 2)
sns.stripplot(data=melted_data, x='Feature', y='value', hue='label', dodge=True, marker='.', alpha=0.5)
plt.xticks(rotation=45)
plt.xlabel('Feature')
plt.ylabel('Value')
plt.suptitle(f'Boxplots and Stripplots of {sector} Sector by Label - Binned')
# Set legend location explicitly and make legend box transparent
legend = plt.legend(loc='upper left', bbox_to_anchor=(1, 1))
legend.get_frame().set_facecolor('none')
plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.show()
end_timer()
<Figure size 1200x800 with 0 Axes>
Tempo de execução: 67.21745252609253 segundos
# StandardScaler
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler, StandardScaler
# Create a list of sector names
sectors = list(sector_columns.keys())
start_timer()
# Create a boxplot and stripplot for each sector's columns
plt.figure(figsize=(12, 8))
for sector, columns in sector_columns.items():
valid_columns = [col for col in columns if col in df.columns]
if valid_columns:
plt.figure(figsize=(20, 4))
data_subset = df[['label'] + valid_columns] # Include 'label' column and valid columns
# Apply StandardScaler scaling
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data_subset[valid_columns])
data_scaled_df = pd.DataFrame(data_scaled, columns=valid_columns)
data_scaled_df['label'] = data_subset['label']
melted_data = data_scaled_df.melt(id_vars=['label'], value_vars=valid_columns, var_name='Feature')
plt.subplot(1, 2, 1)
sns.boxplot(data=melted_data, x='Feature', y='value', hue='label')
plt.subplot(1, 2, 2)
sns.stripplot(data=melted_data, x='Feature', y='value', hue='label', dodge=True, marker='.', alpha=0.5)
plt.xticks(rotation=45)
plt.xlabel('Feature')
plt.ylabel('Value')
plt.suptitle(f'Boxplots and Stripplots of {sector} Sector by Label (Standard Scaler)')
plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.show()
end_timer()
<Figure size 1200x800 with 0 Axes>
Tempo de execução: 149.65817260742188 segundos
# StandardScaler with RandomUnderSampler
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from imblearn.under_sampling import RandomUnderSampler
# Create a list of sector names
sectors = list(sector_columns.keys())
# Function to perform random undersampling
def perform_random_undersampling(X, y):
sampler = RandomUnderSampler(sampling_strategy='majority', random_state=42)
X_resampled, y_resampled = sampler.fit_resample(X, y)
return X_resampled, y_resampled
start_timer()
# Create a boxplot and stripplot for each sector's columns
plt.figure(figsize=(12, 8))
for sector, columns in sector_columns.items():
valid_columns = [col for col in columns if col in df.columns]
if valid_columns:
plt.figure(figsize=(20, 4))
data_subset = df[['label'] + valid_columns] # Include 'label' column and valid columns
# Apply StandardScaler scaling
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data_subset[valid_columns])
data_scaled_df = pd.DataFrame(data_scaled, columns=valid_columns)
data_scaled_df['label'] = data_subset['label']
# Perform random undersampling
X_resampled, y_resampled = perform_random_undersampling(data_scaled_df.drop('label', axis=1), data_scaled_df['label'])
# Combine the resampled features and labels
data_subset_resampled = pd.DataFrame(X_resampled, columns=data_scaled_df.drop('label', axis=1).columns)
data_subset_resampled['label'] = y_resampled
melted_data = data_subset_resampled.melt(id_vars=['label'], value_vars=valid_columns, var_name='Feature')
plt.subplot(1, 2, 1)
sns.boxplot(data=melted_data, x='Feature', y='value', hue='label')
plt.subplot(1, 2, 2)
sns.stripplot(data=melted_data, x='Feature', y='value', hue='label', dodge=True, marker='.', alpha=0.5)
plt.xticks(rotation=45)
plt.xlabel('Feature')
plt.ylabel('Value')
plt.suptitle(f'Boxplots and Stripplots of {sector} Sector by Label (Standard Scaler with Random Undersampling)')
plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.show()
end_timer()
<Figure size 1200x800 with 0 Axes>
Tempo de execução: 25.509060621261597 segundos
# MinMaxScaler
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
# Create a list of sector names
sectors = list(sector_columns.keys())
start_timer()
# Create a boxplot and stripplot for each sector's columns
plt.figure(figsize=(12, 8))
for sector, columns in sector_columns.items():
valid_columns = [col for col in columns if col in df.columns]
if valid_columns:
plt.figure(figsize=(20, 4))
data_subset = df[['label'] + valid_columns] # Include 'label' column and valid columns
# Apply StandardScaler scaling
scaler = MinMaxScaler()
data_scaled = scaler.fit_transform(data_subset[valid_columns])
data_scaled_df = pd.DataFrame(data_scaled, columns=valid_columns)
data_scaled_df['label'] = data_subset['label']
melted_data = data_scaled_df.melt(id_vars=['label'], value_vars=valid_columns, var_name='Feature')
plt.subplot(1, 2, 1)
sns.boxplot(data=melted_data, x='Feature', y='value', hue='label')
plt.subplot(1, 2, 2)
sns.stripplot(data=melted_data, x='Feature', y='value', hue='label', dodge=True, marker='.', alpha=0.5)
plt.xticks(rotation=45)
plt.xlabel('Feature')
plt.ylabel('Value')
plt.suptitle(f'Boxplots and Stripplots of {sector} Sector by Label (MinMax Scaler)')
plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.show()
end_timer()
<Figure size 1200x800 with 0 Axes>
Tempo de execução: 259.9793314933777 segundos
# MinMaxScaler with RandomUnderSampler
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from imblearn.under_sampling import RandomUnderSampler
# Create a list of sector names
sectors = list(sector_columns.keys())
# Function to perform random undersampling
def perform_random_undersampling(X, y):
sampler = RandomUnderSampler(sampling_strategy='majority', random_state=42)
X_resampled, y_resampled = sampler.fit_resample(X, y)
return X_resampled, y_resampled
start_timer()
# Create a boxplot and stripplot for each sector's columns
plt.figure(figsize=(12, 8))
for sector, columns in sector_columns.items():
valid_columns = [col for col in columns if col in df.columns]
if valid_columns:
plt.figure(figsize=(20, 4))
data_subset = df[['label'] + valid_columns] # Include 'label' column and valid columns
# Perform random undersampling
X_resampled, y_resampled = perform_random_undersampling(data_subset.drop('label', axis=1), data_subset['label'])
# Combine the resampled features and labels
data_subset_resampled = pd.DataFrame(X_resampled, columns=data_subset.drop('label', axis=1).columns)
data_subset_resampled['label'] = y_resampled
# Apply MinMaxScaler scaling
scaler = MinMaxScaler()
data_scaled = scaler.fit_transform(data_subset_resampled[valid_columns])
data_scaled_df = pd.DataFrame(data_scaled, columns=valid_columns)
data_scaled_df['label'] = data_subset_resampled['label']
melted_data = data_scaled_df.melt(id_vars=['label'], value_vars=valid_columns, var_name='Feature')
plt.subplot(1, 2, 1)
sns.boxplot(data=melted_data, x='Feature', y='value', hue='label')
plt.subplot(1, 2, 2)
sns.stripplot(data=melted_data, x='Feature', y='value', hue='label', dodge=True, marker='.', alpha=0.5)
plt.xticks(rotation=45)
plt.xlabel('Feature')
plt.ylabel('Value')
plt.suptitle(f'Boxplots and Stripplots of {sector} Sector by Label (MinMax Scaler with Random Undersampling)')
plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.show()
end_timer()
<Figure size 1200x800 with 0 Axes>
Tempo de execução: 26.05815315246582 segundos
# Data binner with RandomUnderSample of classes
from imblearn.under_sampling import RandomUnderSampler
# Function to perform random undersampling
def perform_random_undersampling(X, y):
sampler = RandomUnderSampler(sampling_strategy='majority', random_state=42)
X_resampled, y_resampled = sampler.fit_resample(X, y)
return X_resampled, y_resampled
start_timer()
# Create a list of sector names
sectors_binned = list(sector_columns_binned.keys())
# Create a boxplot and stripplot for each sector's columns
plt.figure(figsize=(12, 8))
for sector, columns in sector_columns_binned.items():
valid_columns_binned = [col for col in columns if col in df_bins.columns]
if valid_columns_binned:
plt.figure(figsize=(30, 5))
data_subset = df_bins[valid_columns_binned].copy() # Include 'label' column and valid columns
data_subset['label'] = df['label'].copy()
# Perform random undersampling
X_resampled, y_resampled = perform_random_undersampling(data_subset.drop('label', axis=1), data_subset['label'])
# Combine the resampled features and labels
data_subset_resampled = pd.DataFrame(X_resampled, columns=data_subset.drop('label', axis=1).columns)
data_subset_resampled['label'] = y_resampled
melted_data = data_subset_resampled.melt(id_vars=['label'], value_vars=valid_columns_binned, var_name='Feature')
plt.subplot(1, 2, 2)
sns.stripplot(data=melted_data, x='Feature', y='value', hue='label', dodge=True, marker='.', alpha=0.5)
plt.xticks(rotation=45)
plt.xlabel('Feature')
plt.ylabel('Value')
plt.suptitle(f'Boxplots and Stripplots of {sector} Sector by Label - Binned (with Random Undersampling)')
# Set legend location explicitly and make legend box transparent
legend = plt.legend(loc='upper left', bbox_to_anchor=(1, 1))
legend.get_frame().set_facecolor('none')
plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.show()
print("Original Dataset:", df_bins['label'].value_counts(), "\n", "Resampled Dataset:", y_resampled.value_counts())
end_timer()
<Figure size 1200x800 with 0 Axes>
Original Dataset: label 0 17983 1 1000 Name: count, dtype: int64 Resampled Dataset: label 0 1000 1 1000 Name: count, dtype: int64 Tempo de execução: 13.493772506713867 segundos
df_droped_001 = pd.read_csv(f'{files_directory}/df_droped_001.csv')
df_droped_001
| user_id | bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00013 | ... | K00534 | K00535 | K00536 | K00537 | K00538 | K00540 | count_of_nan | count_of_nan_zero | count_of_zero | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 14.0 | 14.0 | 14.0 | 14.0 | 0.0 | 0.0 | NaN | 0.0 | ... | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 129 | 453 | 324 | 0 |
| 1 | 1 | 1 | 19.0 | 19.0 | 19.0 | 19.0 | 0.0 | 0.0 | 41.0 | 14.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 25 | 375 | 350 | 0 |
| 2 | 2 | 0 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | NaN | 5.0 | ... | 0.0 | 20.0 | 0.0 | 0.0 | 1.0 | 20.0 | 91 | 426 | 335 | 0 |
| 3 | 3 | 2 | 21.0 | 21.0 | 21.0 | 21.0 | 0.0 | 0.0 | 310.0 | NaN | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 180 | 472 | 292 | 0 |
| 4 | 4 | 3 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 257.0 | NaN | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 95 | 382 | 287 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 18978 | 2 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | 95.0 | 14.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 106 | 368 | 262 | 0 |
| 18979 | 18979 | 0 | 8.0 | 8.0 | 8.0 | 8.0 | 0.0 | 0.0 | NaN | 5.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 526 | 26 | 0 |
| 18980 | 18980 | 2 | 16.0 | 16.0 | 16.0 | 16.0 | 0.0 | 0.0 | 217.0 | 7.0 | ... | 1090.0 | 1155.0 | 1.0 | 6.0 | 10.0 | 385.0 | 21 | 291 | 270 | 0 |
| 18981 | 18981 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 2.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 530 | 30 | 0 |
| 18982 | 18982 | 0 | 12.0 | 12.0 | 12.0 | 12.0 | 0.0 | 0.0 | NaN | 11.0 | ... | 32.0 | 42.0 | 1.0 | 2.0 | 3.0 | 14.0 | 30 | 351 | 321 | 0 |
18983 rows × 481 columns
# Correlation Methods Comparison
start_timer()
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# Adjust the threshold value as needed
threshold = 0.9
# Define a function to drop highly correlated features based on threshold and NaN values
def drop_highly_correlated_columns(df, threshold=threshold):
dropped_features_by_method = {method: [] for method in ['pearson', 'kendall', 'spearman']}
selected_columns_by_method = {method: [] for method in ['pearson', 'kendall', 'spearman']}
for method in ['pearson', 'kendall', 'spearman']:
corr_matrix = df.corr(method=method)
columns = np.full((corr_matrix.shape[0],), True, dtype=bool)
features_to_drop = []
for i in range(corr_matrix.shape[0]):
for j in range(i + 1, corr_matrix.shape[0]):
if abs(corr_matrix.iloc[i, j]) >= threshold or np.isnan(corr_matrix.iloc[i, j]):
if columns[j]:
columns[j] = False
features_to_drop.append(df.columns[j])
selected_columns = df.columns[columns]
selected_columns_by_method[method] = selected_columns
dropped_features_by_method[method] = features_to_drop
print(f'{method.capitalize()}:', 'Initially was {} features, if drop features by correlation, remanin {} features'.format(df.shape[1], len(selected_columns)))
print(f'{method.capitalize()}:', 'Columns to drop by : {}'.format(set(df.columns) - set(selected_columns)), '\n')
return selected_columns_by_method, dropped_features_by_method
# Drop highly correlated columns
selected_columns_by_method, dropped_features_by_method = drop_highly_correlated_columns(df_droped_001.iloc[:, :])
#print(f'{method}:', 'Initially was {} features, now is {} features'.format(df_droped_001.shape[1], len(selected_columns_by_method['pearson'])))
#print(f'{method}:'Columns to drop by : {}'.format(set(df_droped_001.columns) - set(selected_columns_by_method['pearson'])))
# Print dropped features for each method
for method, dropped_features in dropped_features_by_method.items():
print(f'\n {len(dropped_features)} Features to drop by {method.capitalize()} correlation method, considering a threshold value of {threshold}: {dropped_features}.')
# Calculate intersection and union of dropped features
intersection = set(dropped_features_by_method['pearson'])
union = set(dropped_features_by_method['pearson'])
for method in ['kendall', 'spearman']:
intersection &= set(dropped_features_by_method[method])
union |= set(dropped_features_by_method[method])
print(f'\nIntersection features to drop: {len(intersection)}: {intersection}')
print(f'Union features to drop: {len(union)}: {union}')
# Calculate correlation matrices for different methods
corr_matrix = df.corr(method=method)
plt.figure(figsize=(20, 6))
for idx, method in enumerate(['pearson', 'kendall', 'spearman']):
np.fill_diagonal(corr_matrix.values, 0)
plt.subplot(1, 3, idx + 1) # 1 row, 3 columns, position index
sns.heatmap(df_droped_001[selected_columns_by_method[method]].corr(method=method),
annot=False, cmap='coolwarm', fmt=".2f")
plt.title(f'Correlation Heatmap - {method.capitalize()}')
plt.tight_layout()
plt.show()
end_timer()
Pearson: Initially was 481 features, if drop features by correlation, remanin 200 features
Pearson: Columns to drop by : {'K00184', 'K00321', 'K00328', 'K00456', 'K00452', 'K00154', 'K00300', 'K00276', 'K00075', 'K00472', 'K00339', 'K00247', 'K00283', 'K00526', 'K00159', 'K00127', 'K00338', 'K00540', 'K00495', 'K00106', 'K00306', 'K00221', 'K00490', 'K00342', 'K00384', 'K00483', 'K00447', 'K00363', 'K00310', 'K00479', 'K00451', 'K00432', 'K00441', 'K00051', 'K00068', 'K00329', 'K00044', 'K00105', 'K00371', 'K00224', 'K00516', 'K00414', 'K00175', 'K00115', 'K00534', 'K00307', 'K00160', 'K00222', 'K00177', 'K00303', 'K00166', 'K00484', 'K00038', 'K00517', 'K00512', 'K00477', 'K00359', 'K00250', 'K00060', 'K00248', 'K00289', 'K00324', 'K00458', 'K00165', 'K00514', 'K00311', 'K00413', 'K00004', 'K00326', 'K00078', 'K00529', 'K00100', 'K00080', 'K00119', 'K00494', 'K00062', 'K00435', 'K00505', 'K00538', 'K00510', 'K00345', 'K00509', 'K00511', 'K00327', 'K00032', 'K00428', 'K00475', 'K00528', 'K00521', 'K00139', 'K00063', 'K00039', 'K00265', 'K00256', 'K00151', 'K00453', 'K00422', 'K00387', 'K00136', 'K00172', 'K00533', 'K00116', 'K00122', 'K00263', 'K00045', 'K00271', 'K00488', 'K00532', 'K00340', 'K00107', 'K00297', 'K00308', 'K00171', 'K00411', 'K00244', 'K00043', 'K00474', 'K00214', 'K00313', 'K00285', 'K00410', 'K00530', 'K00003', 'K00102', 'K00148', 'K00492', 'K00079', 'K00046', 'K00169', 'K00537', 'K00489', 'K00438', 'K00113', 'K00155', 'K00163', 'K00487', 'K00108', 'K00325', 'K00262', 'K00341', 'K00069', 'K00513', 'K00096', 'K00343', 'K00226', 'K00196', 'K00252', 'K00302', 'K00213', 'K00378', 'K00178', 'K00448', 'K00120', 'K00103', 'K00424', 'K00264', 'K00468', 'K00295', 'K00081', 'K00238', 'K00425', 'K00524', 'K00404', 'K00497', 'K00042', 'K00183', 'K00405', 'K00408', 'K00033', 'K00225', 'K00358', 'K00193', 'K00519', 'K00057', 'K00132', 'K00531', 'K00427', 'K00375', 'K00459', 'K00117', 'K00322', 'K00099', 'K00056', 'K00074', 'K00110', 'K00098', 'K00372', 'K00440', 'K00124', 'K00126', 'K00168', 'K00261', 'K00104', 'K00130', 'K00461', 'K00493', 'K00138', 'K00402', 'K00536', 'K00237', 'K00522', 'K00050', 'K00121', 'K00361', 'K00268', 'K00502', 'K00241', 'K00087', 'K00305', 'K00180', 'K00366', 'K00417', 'K00508', 'K00109', 'K00217', 'K00450', 'K00374', 'K00094', 'K00429', 'K00319', 'K00157', 'K00396', 'K00015', 'K00527', 'K00255', 'K00240', 'K00114', 'K00133', 'K00399', 'K00118', 'K00292', 'count_of_zero', 'K00123', 'K00111', 'K00223', 'K00498', 'K00125', 'K00503', 'K00367', 'K00232', 'K00437', 'K00500', 'K00381', 'K00291', 'K00019', 'K00520', 'K00369', 'K00501', 'K00002', 'K00434', 'K00016', 'K00181', 'K00478', 'K00156', 'K00253', 'K00473', 'K00449', 'K00231', 'K00093', 'K00037', 'K00426', 'K00506', 'K00294', 'K00465', 'K00286', 'K00097', 'K00444', 'K00525', 'K00496', 'K00162', 'K00486', 'K00323', 'K00504', 'K00280', 'K00480', 'K00229', 'K00284', 'K00086', 'K00245', 'K00235', 'K00112'}
Kendall: Initially was 481 features, if drop features by correlation, remanin 225 features
Kendall: Columns to drop by : {'K00321', 'K00328', 'K00137', 'K00452', 'K00154', 'K00300', 'K00276', 'K00075', 'K00472', 'K00339', 'K00379', 'K00247', 'K00398', 'K00127', 'K00338', 'K00540', 'K00495', 'K00106', 'K00380', 'K00221', 'K00482', 'K00342', 'K00483', 'K00490', 'K00447', 'K00479', 'K00423', 'K00451', 'K00476', 'K00051', 'K00068', 'K00329', 'K00441', 'K00105', 'K00272', 'K00224', 'K00464', 'K00243', 'K00516', 'K00415', 'K00301', 'K00115', 'K00484', 'K00222', 'K00303', 'K00273', 'K00215', 'K00018', 'K00517', 'K00512', 'K00477', 'K00359', 'K00250', 'K00228', 'K00445', 'K00248', 'K00071', 'K00324', 'K00458', 'K00514', 'K00153', 'K00446', 'K00392', 'K00004', 'K00216', 'K00357', 'K00326', 'K00119', 'K00494', 'K00435', 'K00505', 'K00260', 'K00518', 'K00510', 'K00345', 'K00509', 'K00511', 'K00327', 'K00032', 'K00428', 'K00463', 'K00475', 'K00521', 'K00528', 'K00139', 'K00421', 'K00265', 'K00039', 'K00416', 'K00256', 'K00453', 'K00422', 'K00387', 'K00136', 'K00172', 'K00533', 'K00116', 'K00122', 'K00532', 'K00274', 'K00317', 'K00340', 'K00152', 'K00391', 'K00107', 'K00053', 'K00297', 'K00409', 'K00411', 'K00244', 'K00278', 'K00474', 'K00214', 'K00469', 'K00313', 'K00410', 'K00003', 'K00148', 'K00169', 'K00113', 'K00325', 'K00487', 'K00108', 'K00262', 'K00341', 'K00069', 'K00513', 'K00242', 'K00267', 'K00343', 'K00226', 'K00373', 'K00196', 'K00302', 'K00178', 'K00120', 'K00448', 'K00470', 'K00424', 'K00468', 'K00295', 'K00238', 'K00425', 'K00524', 'K00404', 'K00146', 'K00497', 'K00405', 'K00225', 'K00033', 'K00358', 'K00356', 'K00193', 'K00519', 'K00057', 'K00132', 'K00531', 'K00375', 'K00293', 'K00459', 'K00117', 'K00322', 'K00110', 'K00440', 'K00124', 'K00126', 'K00318', 'K00261', 'K00104', 'K00493', 'K00138', 'K00364', 'K00536', 'K00237', 'K00147', 'K00522', 'K00121', 'K00361', 'K00268', 'K00050', 'K00502', 'K00241', 'K00305', 'K00366', 'K00417', 'K00508', 'K00194', 'K00471', 'K00279', 'K00109', 'K00450', 'K00217', 'K00481', 'K00397', 'K00374', 'K00429', 'K00249', 'K00319', 'K00015', 'K00433', 'K00255', 'K00114', 'K00133', 'K00393', 'K00399', 'K00118', 'K00292', 'K00365', 'K00111', 'K00223', 'K00123', 'K00498', 'K00125', 'K00503', 'K00367', 'K00232', 'K00131', 'K00403', 'K00500', 'K00381', 'K00291', 'K00019', 'K00266', 'K00520', 'K00006', 'K00369', 'K00457', 'K00501', 'K00002', 'K00434', 'K00016', 'K00195', 'K00112', 'K00253', 'K00449', 'K00231', 'K00426', 'K00506', 'K00145', 'K00294', 'K00465', 'K00254', 'K00525', 'K00496', 'K00386', 'K00236', 'K00323', 'K00504', 'K00280', 'K00480', 'K00229', 'K00245', 'K00427', 'K00230', 'K00439', 'K00385'}
Spearman: Initially was 481 features, if drop features by correlation, remanin 155 features
Spearman: Columns to drop by : {'K00328', 'K00456', 'K00137', 'K00075', 'K00472', 'K00283', 'K00398', 'K00127', 'K00540', 'K00342', 'K00310', 'K00479', 'K00476', 'K00051', 'K00068', 'K00329', 'K00464', 'K00177', 'K00166', 'K00359', 'K00250', 'K00289', 'K00324', 'K00514', 'K00446', 'K00004', 'K00216', 'K00357', 'K00080', 'K00062', 'K00435', 'K00260', 'K00538', 'K00509', 'K00463', 'K00475', 'K00521', 'K00256', 'K00422', 'K00136', 'K00532', 'K00274', 'K00317', 'K00297', 'K00308', 'K00278', 'K00474', 'K00170', 'K00108', 'K00487', 'K00069', 'K00267', 'K00302', 'K00448', 'K00468', 'K00404', 'K00405', 'K00408', 'K00293', 'K00124', 'K00261', 'K00237', 'K00361', 'K00268', 'K00502', 'K00180', 'K00279', 'K00450', 'K00118', 'K00292', 'K00365', 'K00535', 'K00367', 'K00131', 'K00061', 'K00381', 'K00019', 'K00006', 'K00369', 'K00181', 'K00449', 'K00037', 'K00035', 'K00236', 'K00480', 'K00229', 'K00245', 'K00427', 'K00321', 'K00452', 'K00300', 'K00379', 'K00338', 'K00495', 'K00482', 'K00441', 'K00105', 'K00224', 'K00243', 'K00516', 'K00175', 'K00301', 'K00222', 'K00303', 'K00018', 'K00512', 'K00060', 'K00153', 'K00392', 'K00078', 'K00529', 'K00494', 'K00518', 'K00510', 'K00345', 'K00511', 'K00327', 'K00528', 'K00139', 'K00063', 'K00265', 'K00053', 'K00171', 'K00214', 'K00410', 'K00003', 'K00102', 'K00492', 'K00537', 'K00096', 'K00196', 'K00470', 'K00295', 'K00042', 'K00033', 'K00193', 'K00519', 'K00057', 'K00132', 'K00459', 'K00056', 'K00110', 'K00493', 'K00138', 'K00364', 'K00536', 'K00147', 'K00522', 'K00241', 'K00087', 'K00305', 'K00094', 'K00249', 'K00396', 'K00527', 'K00433', 'K00393', 'K00399', 'K00111', 'K00223', 'K00125', 'K00232', 'K00291', 'K00457', 'K00501', 'K00002', 'K00434', 'K00195', 'K00253', 'K00231', 'K00286', 'K00097', 'K00496', 'K00386', 'K00504', 'K00385', 'K00184', 'K00154', 'K00276', 'K00339', 'K00380', 'K00221', 'K00483', 'K00363', 'K00415', 'K00160', 'K00484', 'K00038', 'K00477', 'K00228', 'K00445', 'K00071', 'K00458', 'K00311', 'K00299', 'K00100', 'K00119', 'K00505', 'K00428', 'count_of_nan_zero', 'K00421', 'K00387', 'K00172', 'K00340', 'K00391', 'K00107', 'K00043', 'K00469', 'K00285', 'K00530', 'K00148', 'K00489', 'K00113', 'K00163', 'K00341', 'K00343', 'K00226', 'K00373', 'K00081', 'K00238', 'K00524', 'K00146', 'K00497', 'K00225', 'K00531', 'K00117', 'K00099', 'K00074', 'K00098', 'K00440', 'K00121', 'K00417', 'K00194', 'K00109', 'K00217', 'K00429', 'K00319', 'K00255', 'K00114', 'K00176', 'K00403', 'K00520', 'K00016', 'K00077', 'K00145', 'K00254', 'K00486', 'K00439', 'K00247', 'K00159', 'K00106', 'K00490', 'K00447', 'K00423', 'K00451', 'K00174', 'K00044', 'K00272', 'K00115', 'K00273', 'K00215', 'K00517', 'K00248', 'K00165', 'K00326', 'K00032', 'K00039', 'K00416', 'K00453', 'K00533', 'K00116', 'K00122', 'K00135', 'K00488', 'K00045', 'K00152', 'K00409', 'K00411', 'K00244', 'K00313', 'K00079', 'K00169', 'K00325', 'K00262', 'K00513', 'K00242', 'K00178', 'K00120', 'K00103', 'K00424', 'K00425', 'K00183', 'K00358', 'K00356', 'K00375', 'K00322', 'K00126', 'K00318', 'K00104', 'K00050', 'K00366', 'K00508', 'K00471', 'K00481', 'K00397', 'K00374', 'K00157', 'K00015', 'K00133', 'K00123', 'K00498', 'K00503', 'K00500', 'K00266', 'K00156', 'K00093', 'K00426', 'K00506', 'K00294', 'K00465', 'K00525', 'K00162', 'K00323', 'K00280', 'K00230', 'K00112'}
281 Features to drop by Pearson correlation method, considering a threshold value of 0.9: ['K00002', 'K00003', 'K00004', 'K00019', 'K00015', 'K00016', 'K00245', 'K00248', 'K00221', 'K00222', 'K00223', 'K00224', 'K00225', 'K00226', 'K00338', 'K00339', 'K00340', 'K00341', 'K00342', 'K00343', 'K00345', 'K00032', 'K00033', 'K00037', 'K00043', 'K00038', 'K00044', 'K00045', 'K00039', 'K00042', 'K00046', 'K00050', 'K00051', 'K00056', 'K00057', 'K00060', 'K00062', 'K00063', 'K00068', 'K00069', 'K00074', 'K00075', 'K00078', 'K00079', 'K00080', 'K00081', 'K00086', 'K00087', 'K00093', 'K00098', 'K00104', 'K00105', 'K00110', 'K00116', 'K00122', 'K00155', 'K00094', 'K00099', 'K00100', 'K00106', 'K00111', 'K00112', 'K00117', 'K00118', 'K00123', 'K00124', 'K00156', 'K00283', 'K00096', 'K00097', 'K00107', 'K00108', 'K00109', 'K00119', 'K00120', 'K00121', 'K00284', 'K00285', 'K00286', 'K00102', 'K00113', 'K00114', 'K00125', 'K00126', 'K00103', 'K00115', 'K00127', 'K00157', 'K00130', 'K00132', 'K00133', 'K00517', 'K00136', 'K00138', 'K00520', 'K00139', 'K00521', 'K00522', 'K00148', 'K00151', 'K00154', 'K00307', 'K00308', 'K00313', 'K00159', 'K00160', 'K00310', 'K00311', 'K00162', 'K00163', 'K00526', 'K00527', 'K00532', 'K00165', 'K00166', 'K00488', 'K00489', 'K00529', 'K00530', 'K00168', 'K00169', 'K00493', 'K00494', 'K00495', 'K00500', 'K00171', 'K00496', 'K00497', 'K00172', 'K00498', 'K00175', 'K00177', 'K00178', 'K00180', 'K00181', 'K00183', 'K00184', 'K00478', 'K00193', 'K00479', 'K00480', 'K00196', 'K00213', 'K00263', 'K00214', 'K00264', 'K00217', 'K00328', 'K00322', 'K00229', 'K00323', 'K00324', 'K00329', 'K00231', 'K00325', 'K00326', 'K00232', 'K00327', 'K00235', 'K00237', 'K00238', 'K00240', 'K00241', 'K00244', 'K00247', 'K00250', 'K00252', 'K00253', 'K00255', 'K00256', 'K00261', 'K00262', 'K00265', 'K00268', 'K00271', 'K00276', 'K00508', 'K00280', 'K00306', 'K00289', 'K00291', 'K00292', 'K00297', 'K00294', 'K00295', 'K00300', 'K00305', 'K00302', 'K00303', 'K00321', 'K00319', 'K00424', 'K00425', 'K00426', 'K00427', 'K00428', 'K00429', 'K00448', 'K00449', 'K00450', 'K00451', 'K00452', 'K00453', 'K00472', 'K00475', 'K00509', 'K00510', 'K00511', 'K00512', 'K00513', 'K00514', 'K00516', 'K00484', 'K00487', 'K00490', 'K00501', 'K00504', 'K00525', 'K00528', 'K00531', 'K00533', 'K00536', 'K00361', 'K00358', 'K00359', 'K00363', 'K00369', 'K00366', 'K00367', 'K00371', 'K00372', 'K00374', 'K00375', 'K00378', 'K00381', 'K00384', 'K00387', 'K00396', 'K00534', 'K00399', 'K00537', 'K00538', 'K00402', 'K00404', 'K00405', 'K00408', 'K00410', 'K00411', 'K00413', 'K00414', 'K00417', 'K00422', 'K00432', 'K00434', 'K00435', 'K00437', 'K00438', 'K00440', 'K00441', 'K00444', 'K00447', 'K00456', 'K00458', 'K00459', 'K00461', 'K00502', 'K00503', 'K00465', 'K00505', 'K00506', 'K00468', 'K00473', 'K00474', 'K00477', 'K00483', 'K00486', 'K00492', 'K00519', 'K00524', 'K00540', 'count_of_zero'].
256 Features to drop by Kendall correlation method, considering a threshold value of 0.9: ['K00002', 'K00003', 'K00004', 'K00006', 'K00019', 'K00015', 'K00016', 'K00018', 'K00245', 'K00248', 'K00221', 'K00222', 'K00223', 'K00224', 'K00225', 'K00226', 'K00338', 'K00339', 'K00340', 'K00341', 'K00342', 'K00343', 'K00345', 'K00032', 'K00033', 'K00039', 'K00050', 'K00051', 'K00053', 'K00057', 'K00068', 'K00069', 'K00071', 'K00075', 'K00104', 'K00116', 'K00105', 'K00117', 'K00106', 'K00118', 'K00107', 'K00108', 'K00109', 'K00110', 'K00122', 'K00111', 'K00123', 'K00112', 'K00124', 'K00113', 'K00125', 'K00114', 'K00126', 'K00115', 'K00127', 'K00119', 'K00120', 'K00121', 'K00131', 'K00132', 'K00133', 'K00137', 'K00517', 'K00136', 'K00138', 'K00518', 'K00139', 'K00519', 'K00524', 'K00520', 'K00521', 'K00522', 'K00146', 'K00145', 'K00147', 'K00148', 'K00152', 'K00153', 'K00154', 'K00493', 'K00169', 'K00494', 'K00495', 'K00500', 'K00496', 'K00172', 'K00497', 'K00498', 'K00178', 'K00194', 'K00481', 'K00193', 'K00195', 'K00196', 'K00479', 'K00480', 'K00482', 'K00483', 'K00215', 'K00214', 'K00216', 'K00217', 'K00328', 'K00228', 'K00230', 'K00231', 'K00322', 'K00323', 'K00325', 'K00326', 'K00229', 'K00232', 'K00324', 'K00327', 'K00329', 'K00236', 'K00237', 'K00238', 'K00242', 'K00241', 'K00243', 'K00244', 'K00247', 'K00249', 'K00250', 'K00254', 'K00253', 'K00255', 'K00256', 'K00260', 'K00261', 'K00262', 'K00266', 'K00265', 'K00267', 'K00268', 'K00272', 'K00273', 'K00274', 'K00276', 'K00278', 'K00279', 'K00280', 'K00291', 'K00293', 'K00292', 'K00294', 'K00295', 'K00297', 'K00301', 'K00300', 'K00302', 'K00305', 'K00303', 'K00313', 'K00317', 'K00318', 'K00319', 'K00321', 'K00424', 'K00425', 'K00426', 'K00427', 'K00428', 'K00429', 'K00448', 'K00449', 'K00450', 'K00451', 'K00452', 'K00453', 'K00472', 'K00475', 'K00509', 'K00510', 'K00511', 'K00512', 'K00513', 'K00514', 'K00516', 'K00484', 'K00487', 'K00490', 'K00501', 'K00504', 'K00525', 'K00528', 'K00531', 'K00533', 'K00536', 'K00357', 'K00356', 'K00358', 'K00359', 'K00361', 'K00365', 'K00364', 'K00366', 'K00369', 'K00367', 'K00373', 'K00374', 'K00375', 'K00379', 'K00380', 'K00381', 'K00385', 'K00386', 'K00387', 'K00391', 'K00392', 'K00393', 'K00397', 'K00398', 'K00399', 'K00403', 'K00404', 'K00405', 'K00409', 'K00410', 'K00411', 'K00415', 'K00416', 'K00417', 'K00421', 'K00422', 'K00423', 'K00433', 'K00434', 'K00435', 'K00439', 'K00440', 'K00441', 'K00445', 'K00446', 'K00447', 'K00457', 'K00458', 'K00459', 'K00463', 'K00464', 'K00502', 'K00505', 'K00465', 'K00503', 'K00506', 'K00508', 'K00469', 'K00468', 'K00470', 'K00471', 'K00474', 'K00476', 'K00477', 'K00532', 'K00540'].
326 Features to drop by Spearman correlation method, considering a threshold value of 0.9: ['K00002', 'K00003', 'K00004', 'K00006', 'K00019', 'K00015', 'K00016', 'K00018', 'K00245', 'K00248', 'K00221', 'K00222', 'K00223', 'K00224', 'K00225', 'K00226', 'K00338', 'K00339', 'K00340', 'K00341', 'K00342', 'K00343', 'K00345', 'K00032', 'K00033', 'K00038', 'K00037', 'K00039', 'K00035', 'K00042', 'K00044', 'K00043', 'K00045', 'K00050', 'K00051', 'K00053', 'K00056', 'K00057', 'K00060', 'K00062', 'K00061', 'K00063', 'K00068', 'K00069', 'K00071', 'K00074', 'K00075', 'K00078', 'K00080', 'K00079', 'K00081', 'K00077', 'K00087', 'K00093', 'K00098', 'K00104', 'K00105', 'K00110', 'K00116', 'K00122', 'K00094', 'K00099', 'K00100', 'K00106', 'K00111', 'K00112', 'K00117', 'K00118', 'K00123', 'K00124', 'K00096', 'K00107', 'K00108', 'K00119', 'K00097', 'K00109', 'K00120', 'K00121', 'K00285', 'K00286', 'K00102', 'K00113', 'K00114', 'K00125', 'K00126', 'K00103', 'K00115', 'K00127', 'K00283', 'K00184', 'K00160', 'K00131', 'K00132', 'K00133', 'K00135', 'K00137', 'K00138', 'K00517', 'K00520', 'K00136', 'K00139', 'K00518', 'K00521', 'K00519', 'K00522', 'K00524', 'K00146', 'K00145', 'K00147', 'K00148', 'K00152', 'K00153', 'K00154', 'K00156', 'K00157', 'K00308', 'K00313', 'K00159', 'K00310', 'K00311', 'K00162', 'K00163', 'K00165', 'K00166', 'K00488', 'K00489', 'K00530', 'K00170', 'K00493', 'K00496', 'K00169', 'K00171', 'K00172', 'K00494', 'K00495', 'K00497', 'K00498', 'K00500', 'K00174', 'K00176', 'K00175', 'K00177', 'K00178', 'K00180', 'K00181', 'K00183', 'K00194', 'K00481', 'K00193', 'K00195', 'K00196', 'K00479', 'K00482', 'K00480', 'K00483', 'K00215', 'K00214', 'K00216', 'K00217', 'K00328', 'K00228', 'K00230', 'K00231', 'K00322', 'K00323', 'K00325', 'K00326', 'K00229', 'K00232', 'K00324', 'K00327', 'K00329', 'K00236', 'K00237', 'K00238', 'K00242', 'K00241', 'K00243', 'K00244', 'K00247', 'K00249', 'K00250', 'K00254', 'K00253', 'K00255', 'K00256', 'K00260', 'K00261', 'K00262', 'K00266', 'K00265', 'K00267', 'K00268', 'K00272', 'K00273', 'K00274', 'K00276', 'K00278', 'K00279', 'K00280', 'K00289', 'K00291', 'K00292', 'K00293', 'K00294', 'K00295', 'K00297', 'K00299', 'K00301', 'K00302', 'K00300', 'K00303', 'K00305', 'K00317', 'K00318', 'K00319', 'K00321', 'K00424', 'K00425', 'K00426', 'K00427', 'K00428', 'K00429', 'K00448', 'K00449', 'K00450', 'K00451', 'K00452', 'K00453', 'K00472', 'K00475', 'K00509', 'K00510', 'K00511', 'K00512', 'K00513', 'K00514', 'K00516', 'K00484', 'K00487', 'K00490', 'K00501', 'K00504', 'K00525', 'K00528', 'K00531', 'K00533', 'K00536', 'K00357', 'K00356', 'K00358', 'K00359', 'K00361', 'K00363', 'K00365', 'K00366', 'K00364', 'K00367', 'K00369', 'K00373', 'K00374', 'K00375', 'K00379', 'K00380', 'K00381', 'K00385', 'K00386', 'K00387', 'K00391', 'K00392', 'K00393', 'K00397', 'K00396', 'K00398', 'K00399', 'K00537', 'K00538', 'K00403', 'K00404', 'K00405', 'K00409', 'K00408', 'K00410', 'K00411', 'K00415', 'K00416', 'K00417', 'K00421', 'K00422', 'K00423', 'K00433', 'K00434', 'K00435', 'K00439', 'K00440', 'K00441', 'K00445', 'K00446', 'K00447', 'K00457', 'K00456', 'K00458', 'K00459', 'K00463', 'K00464', 'K00502', 'K00505', 'K00465', 'K00503', 'K00506', 'K00508', 'K00469', 'K00468', 'K00470', 'K00471', 'K00474', 'K00476', 'K00477', 'K00486', 'K00529', 'K00492', 'K00527', 'K00532', 'K00535', 'K00540', 'count_of_nan_zero'].
Intersection features to drop: 188: {'K00321', 'K00328', 'K00154', 'K00452', 'K00300', 'K00276', 'K00075', 'K00247', 'K00339', 'K00472', 'K00127', 'K00338', 'K00540', 'K00106', 'K00495', 'K00221', 'K00490', 'K00342', 'K00483', 'K00447', 'K00479', 'K00451', 'K00441', 'K00051', 'K00068', 'K00329', 'K00105', 'K00224', 'K00516', 'K00115', 'K00484', 'K00222', 'K00303', 'K00517', 'K00512', 'K00477', 'K00248', 'K00250', 'K00359', 'K00324', 'K00458', 'K00514', 'K00004', 'K00326', 'K00119', 'K00494', 'K00435', 'K00505', 'K00510', 'K00345', 'K00032', 'K00509', 'K00511', 'K00327', 'K00428', 'K00475', 'K00528', 'K00139', 'K00521', 'K00039', 'K00265', 'K00256', 'K00453', 'K00533', 'K00387', 'K00116', 'K00122', 'K00136', 'K00172', 'K00422', 'K00532', 'K00340', 'K00107', 'K00297', 'K00411', 'K00244', 'K00474', 'K00214', 'K00313', 'K00410', 'K00003', 'K00148', 'K00169', 'K00113', 'K00325', 'K00487', 'K00108', 'K00262', 'K00341', 'K00069', 'K00513', 'K00343', 'K00226', 'K00196', 'K00302', 'K00178', 'K00120', 'K00448', 'K00424', 'K00238', 'K00295', 'K00425', 'K00468', 'K00524', 'K00497', 'K00404', 'K00225', 'K00405', 'K00033', 'K00358', 'K00519', 'K00193', 'K00057', 'K00132', 'K00531', 'K00375', 'K00459', 'K00117', 'K00322', 'K00110', 'K00126', 'K00124', 'K00440', 'K00104', 'K00261', 'K00493', 'K00138', 'K00536', 'K00237', 'K00050', 'K00522', 'K00121', 'K00361', 'K00268', 'K00502', 'K00241', 'K00305', 'K00366', 'K00417', 'K00508', 'K00109', 'K00217', 'K00450', 'K00374', 'K00429', 'K00319', 'K00015', 'K00255', 'K00114', 'K00133', 'K00399', 'K00118', 'K00292', 'K00123', 'K00223', 'K00111', 'K00498', 'K00125', 'K00503', 'K00367', 'K00232', 'K00500', 'K00381', 'K00291', 'K00019', 'K00520', 'K00369', 'K00501', 'K00002', 'K00016', 'K00434', 'K00253', 'K00449', 'K00231', 'K00426', 'K00506', 'K00294', 'K00465', 'K00525', 'K00496', 'K00323', 'K00280', 'K00504', 'K00480', 'K00229', 'K00245', 'K00427', 'K00112'}
Union features to drop: 359: {'K00328', 'K00456', 'K00137', 'K00075', 'K00472', 'K00283', 'K00398', 'K00127', 'K00540', 'K00306', 'K00342', 'K00384', 'K00310', 'K00479', 'K00476', 'K00051', 'K00068', 'K00329', 'K00464', 'K00177', 'K00166', 'K00359', 'K00250', 'K00324', 'K00289', 'K00514', 'K00446', 'K00004', 'K00216', 'K00357', 'K00080', 'K00062', 'K00435', 'K00260', 'K00538', 'K00509', 'K00475', 'K00463', 'K00521', 'K00256', 'K00422', 'K00136', 'K00263', 'K00532', 'K00274', 'K00317', 'K00297', 'K00308', 'K00278', 'K00474', 'K00438', 'K00155', 'K00487', 'K00108', 'K00170', 'K00069', 'K00267', 'K00302', 'K00448', 'K00468', 'K00404', 'K00405', 'K00408', 'K00293', 'K00124', 'K00168', 'K00261', 'K00237', 'K00361', 'K00268', 'K00502', 'K00180', 'K00279', 'K00450', 'K00118', 'K00292', 'K00365', 'K00535', 'K00367', 'K00131', 'K00381', 'K00061', 'K00019', 'K00006', 'K00369', 'K00181', 'K00449', 'K00037', 'K00035', 'K00236', 'K00480', 'K00229', 'K00245', 'K00427', 'K00235', 'K00321', 'K00452', 'K00300', 'K00379', 'K00338', 'K00495', 'K00482', 'K00441', 'K00105', 'K00224', 'K00243', 'K00516', 'K00175', 'K00301', 'K00222', 'K00303', 'K00018', 'K00512', 'K00060', 'K00153', 'K00392', 'K00078', 'K00529', 'K00494', 'K00518', 'K00510', 'K00345', 'K00511', 'K00327', 'K00528', 'K00139', 'K00063', 'K00265', 'K00151', 'K00053', 'K00171', 'K00214', 'K00410', 'K00003', 'K00102', 'K00492', 'K00046', 'K00537', 'K00096', 'K00196', 'K00252', 'K00213', 'K00378', 'K00470', 'K00295', 'K00042', 'K00033', 'K00193', 'K00519', 'K00057', 'K00132', 'K00459', 'K00056', 'K00110', 'K00493', 'K00138', 'K00364', 'K00402', 'K00536', 'K00522', 'K00147', 'K00241', 'K00087', 'K00305', 'K00094', 'K00249', 'K00396', 'K00527', 'K00433', 'K00393', 'K00399', 'K00223', 'K00111', 'K00125', 'K00232', 'K00437', 'K00291', 'K00457', 'K00501', 'K00002', 'K00434', 'K00478', 'K00195', 'K00253', 'K00231', 'K00286', 'K00097', 'K00444', 'K00496', 'K00386', 'K00504', 'K00086', 'K00385', 'K00184', 'K00154', 'K00276', 'K00339', 'K00526', 'K00380', 'K00221', 'K00483', 'K00363', 'K00432', 'K00371', 'K00415', 'K00414', 'K00160', 'K00484', 'K00038', 'K00477', 'K00228', 'K00445', 'K00071', 'K00458', 'K00311', 'K00413', 'K00299', 'K00100', 'K00119', 'K00505', 'K00428', 'count_of_nan_zero', 'K00421', 'K00387', 'K00172', 'K00340', 'K00391', 'K00107', 'K00043', 'K00469', 'K00285', 'K00530', 'K00148', 'K00489', 'K00113', 'K00163', 'K00341', 'K00343', 'K00226', 'K00373', 'K00264', 'K00081', 'K00238', 'K00524', 'K00497', 'K00146', 'K00225', 'K00531', 'K00117', 'K00099', 'K00074', 'K00098', 'K00440', 'K00461', 'K00121', 'K00417', 'K00194', 'K00109', 'K00217', 'K00429', 'K00319', 'K00255', 'K00114', 'count_of_zero', 'K00176', 'K00403', 'K00520', 'K00016', 'K00077', 'K00473', 'K00145', 'K00254', 'K00486', 'K00284', 'K00439', 'K00247', 'K00159', 'K00106', 'K00490', 'K00447', 'K00423', 'K00451', 'K00174', 'K00044', 'K00272', 'K00115', 'K00534', 'K00307', 'K00273', 'K00517', 'K00215', 'K00248', 'K00165', 'K00326', 'K00032', 'K00039', 'K00416', 'K00453', 'K00533', 'K00116', 'K00122', 'K00488', 'K00135', 'K00045', 'K00271', 'K00152', 'K00409', 'K00411', 'K00244', 'K00313', 'K00079', 'K00169', 'K00325', 'K00262', 'K00513', 'K00242', 'K00178', 'K00120', 'K00103', 'K00424', 'K00425', 'K00183', 'K00358', 'K00356', 'K00375', 'K00322', 'K00372', 'K00126', 'K00318', 'K00104', 'K00130', 'K00050', 'K00366', 'K00508', 'K00471', 'K00481', 'K00397', 'K00374', 'K00157', 'K00015', 'K00240', 'K00133', 'K00123', 'K00498', 'K00503', 'K00500', 'K00266', 'K00156', 'K00093', 'K00426', 'K00506', 'K00294', 'K00465', 'K00525', 'K00162', 'K00323', 'K00280', 'K00230', 'K00112'}
Tempo de execução: 511.1961245536804 segundos
# Calculate correlation matrices for different methods with absolute values
plt.figure(figsize=(20, 6))
for idx, method in enumerate(['pearson', 'kendall', 'spearman']):
np.fill_diagonal(corr_matrix.values, 0)
plt.subplot(1, 3, idx + 1) # 1 row, 3 columns, position index
sns.heatmap(np.abs(df_droped_001[selected_columns_by_method[method]].corr(method=method)),
annot=False, cmap='coolwarm', fmt=".2f")
plt.title(f'Correlation Heatmap - {method.capitalize()}')
plt.tight_layout()
plt.show()
# Create a new DataFrame removing the intersection of features by correlation
df_drop = df_droped_001.loc[:, ~df_droped_001.columns.isin(intersection)]
# Display the updated DataFrame
df_drop
| user_id | bank | K00001 | K00005 | K00006 | K00007 | K00013 | K00014 | K00017 | K00018 | ... | K00529 | K00530 | K00534 | K00535 | K00537 | K00538 | count_of_nan | count_of_nan_zero | count_of_zero | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 14.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | ... | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 129 | 453 | 324 | 0 |
| 1 | 1 | 1 | 19.0 | 0.0 | 0.0 | 41.0 | 14.0 | 14.0 | 0.0 | 0.0 | ... | 50.0 | 123.0 | 0.0 | 0.0 | 0.0 | 0.0 | 25 | 375 | 350 | 0 |
| 2 | 2 | 0 | 13.0 | 0.0 | 0.0 | NaN | 5.0 | 5.0 | 0.0 | 0.0 | ... | 13.0 | 23.0 | 0.0 | 20.0 | 0.0 | 1.0 | 91 | 426 | 335 | 0 |
| 3 | 3 | 2 | 21.0 | 0.0 | 0.0 | 310.0 | NaN | NaN | NaN | NaN | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 180 | 472 | 292 | 0 |
| 4 | 4 | 3 | 1.0 | 0.0 | 0.0 | 257.0 | NaN | NaN | NaN | NaN | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 95 | 382 | 287 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 18978 | 2 | 13.0 | 0.0 | 0.0 | 95.0 | 14.0 | 14.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 106 | 368 | 262 | 0 |
| 18979 | 18979 | 0 | 8.0 | 0.0 | 0.0 | NaN | 5.0 | 5.0 | 0.0 | 0.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 526 | 26 | 0 |
| 18980 | 18980 | 2 | 16.0 | 0.0 | 0.0 | 217.0 | 7.0 | 7.0 | 0.0 | 0.0 | ... | 17.0 | 27.0 | 1090.0 | 1155.0 | 6.0 | 10.0 | 21 | 291 | 270 | 0 |
| 18981 | 18981 | 0 | 0.0 | 0.0 | 0.0 | NaN | 2.0 | 2.0 | 0.0 | 0.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 530 | 30 | 0 |
| 18982 | 18982 | 0 | 12.0 | 0.0 | 0.0 | NaN | 11.0 | 11.0 | 0.0 | 0.0 | ... | 73.0 | 114.0 | 32.0 | 42.0 | 2.0 | 3.0 | 30 | 351 | 321 | 0 |
18983 rows × 293 columns
### ANOVA F-Test Feature Selection ###
start_timer()
from pandas import read_csv
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif
from matplotlib import pyplot
import pandas as pd
import numpy as np
X = df_drop.drop(['user_id', 'bank','label'], axis=1).fillna(0)
y = df_drop['label']
# Feature Selection
def select_features(X_train, y_train, X_test):
# configure to select all features
fs = SelectKBest(score_func=f_classif, k='all')
# learn relationship from training data
fs.fit(X_train, y_train)
# transform train input data
X_train_fs = fs.transform(X_train)
# transform test input data
X_test_fs = fs.transform(X_test)
return X_train_fs, X_test_fs, fs
# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
# List of feature indices to drop
#features_to_drop = [28, 32, 34, 55]
# Calculate the variance of each feature
variances = X_train.var()
# Find the indices of constant features (variance = 0)
constant_feature_indices = [idx for idx, var in enumerate(variances) if var == 0]
# Convert indices to column labels
constant_feature_labels = X_train.columns[constant_feature_indices]
# Drop the constant features from X_train and X_test
X_train = X_train.drop(columns=constant_feature_labels)
X_test = X_test.drop(columns=constant_feature_labels)
# Print the dropped feature labels
print("Dropped constant feature labels:", constant_feature_labels.tolist())
# feature selection
X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)
# what are scores for the features
sorted_fscore = fs.scores_.argsort()[::-1] # Sort feature indices by scores
# Create a dictionary to store F-score and p-value information
feature_info = {'Feature': [], 'F-Score': [], 'p-Value': []}
for i in range(len(fs.scores_)):
feature_info['Feature'].append(X_test.columns[i])
feature_info['F-Score'].append(fs.scores_[i]) # Keep numeric values for calculations
feature_info['p-Value'].append(fs.pvalues_[i]) # Keep numeric values for calculations
# Create a DataFrame from the dictionary
feature_df = pd.DataFrame(feature_info)
# Sort the DataFrame by F-Score and p-value
feature_df.sort_values(by=['F-Score'], ascending=False, inplace=True)
# Filter the DataFrame to select rows where p-Value is less than 0.05
significant_features = feature_df[feature_df['p-Value'] < 0.05]
# Store the significant feature names in a list
significant_feature_names = significant_features['Feature'].tolist()
# Print the list of significant feature names
print(f"Total of {len(significant_feature_names)} significant features with p-Value < 0.05:", significant_feature_names)
# Maximum number of features to select
max = 40
# Highlight top features and significant p-values
def highlight_features(val):
color = 'background-color: blue' if val in feature_df.head(max)['F-Score'].tolist() else ''
return color
def highlight_p_values(val):
color = 'background-color: blue' if val < 0.01 else ''
return color
styled_feature_df = (
feature_df[0:max].style
.applymap(highlight_features, subset=['F-Score'])
.applymap(highlight_p_values, subset=['p-Value'])
)
# Display the styled DataFrame
display(styled_feature_df)
plt.figure(figsize=(24, 6))
# Plot: Choose colors based on the highest 10 F-Score and lowest p-values
colorsf = ['blue' if i in sorted_fscore[:max] else 'white' for i in range(len(fs.scores_))]
pyplot.bar([i for i in range(len(fs.scores_))], fs.scores_/fs.scores_.max() , color=colorsf, alpha=0.8)
p_threshold = 0.05 # Set the p-value threshold
colorsp = ['red' if p < p_threshold else 'white' for p in fs.pvalues_]
pyplot.bar([i for i in range(len(fs.pvalues_))], p_threshold/(fs.pvalues_+0.25),color=colorsp, alpha=0.4)
pyplot.xlabel('Feature Index')
pyplot.ylabel('Normalized F-Score and p-Value')
pyplot.title('Feature Scores and p-Value')
pyplot.show()
print('Variaveis em vermelho são significativas com p-value < 0.05 e azul são as variaveis com maior F-Score','\nVariaveis com ambas as cores devem ser selecionadas')
print('Features with color red are significant with p-value < 0.05 and blue are the top features with highest F-Score','\nFeatures with both colors should be selected')
end_timer()
Dropped constant feature labels: [] Total of 130 significant features with p-Value < 0.05: ['count_of_nan', 'count_of_nan_zero', 'K00312', 'count_of_zero', 'K00287', 'K00491', 'K00288', 'K00492', 'K00289', 'K00102', 'K00103', 'K00101', 'K00488', 'K00489', 'K00160', 'K00159', 'K00165', 'K00166', 'K00183', 'K00311', 'K00310', 'K00282', 'K00164', 'K00158', 'K00530', 'K00184', 'K00529', 'K00182', 'K00283', 'K00027', 'K00309', 'K00013', 'K00097', 'K00096', 'K00001', 'K00014', 'K00281', 'K00307', 'K00095', 'K00157', 'K00156', 'K00308', 'K00526', 'K00286', 'K00059', 'K00180', 'K00099', 'K00083', 'K00285', 'K00093', 'K00094', 'K00537', 'K00527', 'K00100', 'K00398', 'K00162', 'K00181', 'K00538', 'K00306', 'K00065', 'K00163', 'K00284', 'K00179', 'K00161', 'K00098', 'K00155', 'K00485', 'K00077', 'K00397', 'K00486', 'K00092', 'K00074', 'K00072', 'K00080', 'K00053', 'K00028', 'K00081', 'K00385', 'K00067', 'K00078', 'K00066', 'K00048', 'K00049', 'K00061', 'K00062', 'K00131', 'K00386', 'K00380', 'K00073', 'K00171', 'K00299', 'K00063', 'K00054', 'K00060', 'K00170', 'K00457', 'K00384', 'K00071', 'K00298', 'K00137', 'K00082', 'K00535', 'K00365', 'K00290', 'K00534', 'K00058', 'K00383', 'K00382', 'K00056', 'K00379', 'K00293', 'K00260', 'K00079', 'K00377', 'K00396', 'K00076', 'K00035', 'K00395', 'K00378', 'K00041', 'K00047', 'K00007', 'K00086', 'K00376', 'K00176', 'K00085', 'K00017', 'K00147', 'K00393', 'K00087']
| Feature | F-Score | p-Value | |
|---|---|---|---|
| 287 | count_of_nan | 164.506769 | 0.000000 |
| 288 | count_of_nan_zero | 158.560306 | 0.000000 |
| 183 | K00312 | 153.797590 | 0.000000 |
| 289 | count_of_zero | 135.795018 | 0.000000 |
| 169 | K00287 | 94.272894 | 0.000000 |
| 276 | K00491 | 92.833814 | 0.000000 |
| 170 | K00288 | 89.596179 | 0.000000 |
| 277 | K00492 | 85.854319 | 0.000000 |
| 171 | K00289 | 74.787517 | 0.000000 |
| 71 | K00102 | 53.172934 | 0.000000 |
| 72 | K00103 | 51.252739 | 0.000000 |
| 70 | K00101 | 45.230959 | 0.000000 |
| 274 | K00488 | 43.870335 | 0.000000 |
| 275 | K00489 | 43.828140 | 0.000000 |
| 98 | K00160 | 43.019259 | 0.000000 |
| 97 | K00159 | 42.885576 | 0.000000 |
| 103 | K00165 | 41.921435 | 0.000000 |
| 104 | K00166 | 41.718600 | 0.000000 |
| 118 | K00183 | 41.606834 | 0.000000 |
| 182 | K00311 | 41.459174 | 0.000000 |
| 181 | K00310 | 39.901792 | 0.000000 |
| 164 | K00282 | 39.381416 | 0.000000 |
| 102 | K00164 | 38.776881 | 0.000000 |
| 96 | K00158 | 38.758512 | 0.000000 |
| 282 | K00530 | 38.209025 | 0.000000 |
| 119 | K00184 | 37.940539 | 0.000000 |
| 281 | K00529 | 37.858352 | 0.000000 |
| 117 | K00182 | 36.152356 | 0.000000 |
| 165 | K00283 | 35.804047 | 0.000000 |
| 9 | K00027 | 34.840540 | 0.000000 |
| 180 | K00309 | 33.989225 | 0.000000 |
| 4 | K00013 | 33.874638 | 0.000000 |
| 66 | K00097 | 32.893723 | 0.000000 |
| 65 | K00096 | 32.872810 | 0.000000 |
| 0 | K00001 | 32.361230 | 0.000000 |
| 5 | K00014 | 32.184726 | 0.000000 |
| 163 | K00281 | 31.767651 | 0.000000 |
| 178 | K00307 | 30.813072 | 0.000000 |
| 64 | K00095 | 30.375648 | 0.000000 |
| 95 | K00157 | 29.961412 | 0.000000 |
Variaveis em vermelho são significativas com p-value < 0.05 e azul são as variaveis com maior F-Score Variaveis com ambas as cores devem ser selecionadas Features with color red are significant with p-value < 0.05 and blue are the top features with highest F-Score Features with both colors should be selected Tempo de execução: 1.1061046123504639 segundos
# Cria o dataframe com as variaveis selecionadas por ANOVA
df_drop_anova = df_drop.loc[:, df_drop.columns.isin(significant_feature_names)]
df_drop_anova
| K00001 | K00007 | K00013 | K00014 | K00017 | K00027 | K00028 | K00035 | K00041 | K00047 | ... | K00527 | K00529 | K00530 | K00534 | K00535 | K00537 | K00538 | count_of_nan | count_of_nan_zero | count_of_zero | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 14.0 | NaN | 0.0 | 0.0 | 0.0 | 2 | 0.0 | NaN | NaN | NaN | ... | 3000.000000 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 129 | 453 | 324 |
| 1 | 19.0 | 41.0 | 14.0 | 14.0 | 0.0 | 1 | 7000.0 | 0.0 | 0.00 | 0.24 | ... | 8892.599609 | 50.0 | 123.0 | 0.0 | 0.0 | 0.0 | 0.0 | 25 | 375 | 350 |
| 2 | 13.0 | NaN | 5.0 | 5.0 | 0.0 | 2 | 0.0 | NaN | NaN | NaN | ... | 3842.199951 | 13.0 | 23.0 | 0.0 | 20.0 | 0.0 | 1.0 | 91 | 426 | 335 |
| 3 | 21.0 | 310.0 | NaN | NaN | NaN | 0 | NaN | NaN | NaN | NaN | ... | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 180 | 472 | 292 |
| 4 | 1.0 | 257.0 | NaN | NaN | NaN | 0 | NaN | NaN | NaN | NaN | ... | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 95 | 382 | 287 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 13.0 | 95.0 | 14.0 | 14.0 | 0.0 | 1 | 7000.0 | 0.0 | 0.09 | 0.18 | ... | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 106 | 368 | 262 |
| 18979 | 8.0 | NaN | 5.0 | 5.0 | 0.0 | 2 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 526 | 26 |
| 18980 | 16.0 | 217.0 | 7.0 | 7.0 | 0.0 | 1 | NaN | 0.0 | 0.40 | 0.45 | ... | 9598.500000 | 17.0 | 27.0 | 1090.0 | 1155.0 | 6.0 | 10.0 | 21 | 291 | 270 |
| 18981 | 0.0 | NaN | 2.0 | 2.0 | 0.0 | 2 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 530 | 30 |
| 18982 | 12.0 | NaN | 11.0 | 11.0 | 0.0 | 1 | 0.0 | 0.0 | 0.01 | 0.47 | ... | 9252.429688 | 73.0 | 114.0 | 32.0 | 42.0 | 2.0 | 3.0 | 30 | 351 | 321 |
18983 rows × 130 columns
### Mutual Information feature selection for numerical input data ###
start_timer()
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import mutual_info_classif
from matplotlib import pyplot
# Feature selection
def select_features(X_train, y_train, X_test):
# Configure to select all features
fs = SelectKBest(score_func=mutual_info_classif, k='all')
# Learn relationship from training data
fs.fit(X_train, y_train)
# Transform train input data
X_train_fs = fs.transform(X_train)
# Transform test input data
X_test_fs = fs.transform(X_test)
return X_train_fs, X_test_fs, fs
# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
# Feature Selection
X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)
'''
for i in range(len(fs.scores_)):
print(X_test.columns[i], '- Feature Mutual Information - index %d: %f' % (i, fs.scores_[i]))
# plot the scores
pyplot.bar([i for i in range(len(fs.scores_))], fs.scores_)
pyplot.show()'''
# Create a dictionary to store feature names and their scores
feature_scores_dict_mi = {'Feature': [], 'Mutual_Information_Score': []}
for i in range(len(fs.scores_)):
feature_scores_dict_mi['Feature'].append(X_test.columns[i])
feature_scores_dict_mi['Mutual_Information_Score'].append(round(fs.scores_[i], 3))
# Create a DataFrame from the dictionary
feature_scores_df_mi = pd.DataFrame(feature_scores_dict_mi)
# Convert Mutual Information scores column to numeric
feature_scores_df_mi['Mutual_Information_Score'] = feature_scores_df_mi['Mutual_Information_Score'].astype(float)/feature_scores_df_mi['Mutual_Information_Score'].max()
# Format Mutual Information scores column to 3 decimal places
feature_scores_df_mi['Mutual_Information_Score'] = feature_scores_df_mi['Mutual_Information_Score'].apply(lambda x: '{:.3f}'.format(x))
# Print the DataFrame
print("DataFrame of Feature Mutual Information Scores:")
display(feature_scores_df_mi.T)
# Sort the DataFrame by Mutual Information scores in descending order
sorted_feature_scores_df_mi = feature_scores_df_mi.sort_values(by='Mutual_Information_Score', ascending=False)
# Maximum number of features to select
max = 40
# Print the list of selected feature names
selected_features_mi = sorted_feature_scores_df_mi.head(max)['Feature'].tolist()
print(f"List of {len(selected_features_mi)} Selected Feature Names:")
print(selected_features_mi)
# Plot the scores
pyplot.bar([i for i in range(len(fs.scores_))], fs.scores_/fs.scores_.max())
pyplot.xlabel('Feature Index')
pyplot.ylabel('Normalized Mutual Information Score')
pyplot.title('Feature Mutual Information Scores')
pyplot.show()
end_timer()
DataFrame of Feature Mutual Information Scores:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 280 | 281 | 282 | 283 | 284 | 285 | 286 | 287 | 288 | 289 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Feature | K00001 | K00005 | K00006 | K00007 | K00013 | K00014 | K00017 | K00018 | K00026 | K00027 | ... | K00527 | K00529 | K00530 | K00534 | K00535 | K00537 | K00538 | count_of_nan | count_of_nan_zero | count_of_zero |
| Mutual_Information_Score | 0.300 | 0.000 | 0.000 | 0.000 | 0.400 | 0.000 | 0.100 | 0.100 | 0.100 | 0.000 | ... | 0.500 | 0.300 | 0.700 | 0.100 | 0.000 | 0.200 | 0.100 | 0.700 | 0.900 | 0.500 |
2 rows × 290 columns
List of 40 Selected Feature Names: ['K00157', 'K00312', 'count_of_nan_zero', 'K00100', 'K00099', 'K00160', 'K00093', 'K00156', 'K00180', 'K00530', 'K00306', 'K00309', 'K00094', 'count_of_nan', 'K00092', 'K00526', 'K00308', 'K00158', 'K00307', 'K00183', 'K00310', 'K00283', 'K00486', 'K00098', 'K00165', 'K00164', 'K00159', 'K00077', 'K00284', 'K00095', 'K00166', 'K00167', 'K00155', 'count_of_zero', 'K00282', 'K00037', 'K00485', 'K00311', 'K00287', 'K00527']
Tempo de execução: 19.445337057113647 segundos
# Gerar o dataframe com as variaveis selecionadas por MI
df_drop_anova_mi = df_drop_anova.loc[:, df_drop_anova.columns.isin(selected_features_mi)]
df_drop_anova_mi
| K00092 | K00093 | K00094 | K00099 | K00100 | K00155 | K00156 | K00157 | K00158 | K00159 | ... | K00312 | K00395 | K00485 | K00486 | K00526 | K00527 | K00530 | count_of_nan | count_of_nan_zero | count_of_zero | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 74.199997 | 3074.199951 | 3074.199951 | 3083.070068 | 3083.070068 | 0.000000 | 3000.000000 | 3000.000000 | 0.0 | 1.0 | ... | 1.0 | 0.000000 | 3000.000000 | 3000.000000 | 3000.000000 | 3000.000000 | 1.0 | 129 | 453 | 324 |
| 1 | 3825.590088 | 10242.940430 | 20437.449219 | 10324.440430 | 20714.269531 | 3825.340088 | 10201.349609 | 20365.519531 | 20.0 | 103.0 | ... | 3.0 | 0.000000 | 4040.330078 | 8892.599609 | 4040.330078 | 8892.599609 | 123.0 | 25 | 375 | 350 |
| 2 | 1271.839966 | 2003.540039 | 4292.620117 | 2780.510010 | 5755.069824 | 1271.839966 | 2003.540039 | 4292.620117 | 11.0 | 18.0 | ... | 3.0 | 0.000000 | 1777.000000 | 3862.199951 | 1777.000000 | 3842.199951 | 23.0 | 91 | 426 | 335 |
| 3 | 0.000000 | 0.000000 | 0.000000 | 21770.460938 | 21770.460938 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | ... | NaN | 0.000000 | 20959.980469 | 20959.980469 | 0.000000 | 0.000000 | 0.0 | 180 | 472 | 292 |
| 4 | 8721.959961 | 10261.959961 | 10261.959961 | 8723.849609 | 8723.849609 | 0.010000 | 40.009998 | 40.009998 | 1.0 | 2.0 | ... | NaN | 0.000000 | 378.500000 | 378.500000 | 0.000000 | 0.000000 | 0.0 | 95 | 382 | 287 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 26819 | 1565.000000 | 1565.000000 | 1565.000000 | 4876.439941 | 4876.439941 | 1565.000000 | 1565.000000 | 1565.000000 | 2.0 | 2.0 | ... | NaN | 115.059998 | 1204.420044 | 1204.420044 | 0.000000 | 0.000000 | 0.0 | 106 | 368 | 262 |
| 26820 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 526 | 26 |
| 26821 | 16270.559570 | 76942.039062 | 100723.296875 | 76431.796875 | 103675.773438 | 4197.600098 | 26182.599609 | 33165.699219 | 13.0 | 26.0 | ... | 3.0 | 90.000000 | 8654.040039 | 13834.540039 | 4483.000000 | 9598.500000 | 27.0 | 21 | 291 | 270 |
| 26822 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 530 | 30 |
| 26823 | 6671.049805 | 10642.000000 | 16146.040039 | 10849.290039 | 16147.509766 | 5113.000000 | 8078.399902 | 10763.500000 | 22.0 | 57.0 | ... | 3.0 | 32.000000 | 6500.120117 | 9294.429688 | 6468.120117 | 9252.429688 | 114.0 | 30 | 351 | 321 |
18983 rows × 36 columns
# Cria a list com as variaveis selecionadas por ANOVA e MI
# Convert the lists to sets for intersection
significant_features_set = set(significant_feature_names)
selected_features_set = set(selected_features_mi)
# Find the intersection of the two sets
intersection_features = significant_features_set.intersection(selected_features_set)
# Convert the intersection set back to a list
intersection_features_list = list(intersection_features)
# Print the list of intersection features
print("Intersection of Significant Features and Selected Features:")
print(intersection_features_list,'\n Total of Features Selected:', len(intersection_features_list))
Intersection of Significant Features and Selected Features: ['K00527', 'count_of_nan_zero', 'K00485', 'K00283', 'count_of_zero', 'K00526', 'K00159', 'K00183', 'K00306', 'K00287', 'K00099', 'count_of_nan', 'K00310', 'K00282', 'K00098', 'K00094', 'K00308', 'K00077', 'K00156', 'K00158', 'K00093', 'K00095', 'K00530', 'K00160', 'K00307', 'K00166', 'K00164', 'K00180', 'K00092', 'K00155', 'K00165', 'K00311', 'K00486', 'K00312', 'K00100', 'K00284', 'K00157', 'K00309'] Total of Features Selected: 38
# Gerar o dataframe com as variaveis selecionadas por ANOVA e MI
df_drop_anova_mi = df_drop.loc[:, df_drop.columns.isin(intersection_features_list)]
df_drop_anova_mi
| K00077 | K00092 | K00093 | K00094 | K00095 | K00098 | K00099 | K00100 | K00155 | K00156 | ... | K00311 | K00312 | K00485 | K00486 | K00526 | K00527 | K00530 | count_of_nan | count_of_nan_zero | count_of_zero | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | 74.199997 | 3074.199951 | 3074.199951 | 2.0 | 83.070000 | 3083.070068 | 3083.070068 | 0.000000 | 3000.000000 | ... | 1.0 | 1.0 | 3000.000000 | 3000.000000 | 3000.000000 | 3000.000000 | 1.0 | 129 | 453 | 324 |
| 1 | 0.00 | 3825.590088 | 10242.940430 | 20437.449219 | 31.0 | 3807.000000 | 10324.440430 | 20714.269531 | 3825.340088 | 10201.349609 | ... | 261.0 | 3.0 | 4040.330078 | 8892.599609 | 4040.330078 | 8892.599609 | 123.0 | 25 | 375 | 350 |
| 2 | NaN | 1271.839966 | 2003.540039 | 4292.620117 | 11.0 | 1297.760010 | 2780.510010 | 5755.069824 | 1271.839966 | 2003.540039 | ... | 31.0 | 3.0 | 1777.000000 | 3862.199951 | 1777.000000 | 3842.199951 | 23.0 | 91 | 426 | 335 |
| 3 | NaN | 0.000000 | 0.000000 | 0.000000 | 0.0 | 21770.460938 | 21770.460938 | 21770.460938 | 0.000000 | 0.000000 | ... | NaN | NaN | 20959.980469 | 20959.980469 | 0.000000 | 0.000000 | 0.0 | 180 | 472 | 292 |
| 4 | NaN | 8721.959961 | 10261.959961 | 10261.959961 | 6.0 | 7180.850098 | 8723.849609 | 8723.849609 | 0.010000 | 40.009998 | ... | 0.0 | NaN | 378.500000 | 378.500000 | 0.000000 | 0.000000 | 0.0 | 95 | 382 | 287 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 0.56 | 1565.000000 | 1565.000000 | 1565.000000 | 2.0 | 4876.439941 | 4876.439941 | 4876.439941 | 1565.000000 | 1565.000000 | ... | NaN | NaN | 1204.420044 | 1204.420044 | 0.000000 | 0.000000 | 0.0 | 106 | 368 | 262 |
| 18979 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 526 | 26 |
| 18980 | 0.00 | 16270.559570 | 76942.039062 | 100723.296875 | 26.0 | 20111.189453 | 76431.796875 | 103675.773438 | 4197.600098 | 26182.599609 | ... | 17.0 | 3.0 | 8654.040039 | 13834.540039 | 4483.000000 | 9598.500000 | 27.0 | 21 | 291 | 270 |
| 18981 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 530 | 30 |
| 18982 | 0.00 | 6671.049805 | 10642.000000 | 16146.040039 | 32.0 | 6514.939941 | 10849.290039 | 16147.509766 | 5113.000000 | 8078.399902 | ... | 85.0 | 3.0 | 6500.120117 | 9294.429688 | 6468.120117 | 9252.429688 | 114.0 | 30 | 351 | 321 |
18983 rows × 38 columns
# Adiciona as coluna user_id, bank, label ao dataframe
df_drop_anova_mi_complete = df_drop_anova_mi.copy()
df_drop_anova_mi_complete['user_id'] = df_drop['user_id']
df_drop_anova_mi_complete['bank'] = df_drop['bank']
df_drop_anova_mi_complete['label'] = df_drop['label']
# Crie uma lista com a ordem desejada das colunas
new_order = ['user_id', 'bank'] + [col for col in df_drop_anova_mi_complete.columns if col not in ['user_id', 'bank']]
# Reorganize as colunas de acordo com a nova ordem
df_drop_anova_mi_complete = df_drop_anova_mi_complete[new_order]
df_drop_anova_mi_complete
| user_id | bank | K00077 | K00092 | K00093 | K00094 | K00095 | K00098 | K00099 | K00100 | ... | K00312 | K00485 | K00486 | K00526 | K00527 | K00530 | count_of_nan | count_of_nan_zero | count_of_zero | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | NaN | 74.199997 | 3074.199951 | 3074.199951 | 2.0 | 83.070000 | 3083.070068 | 3083.070068 | ... | 1.0 | 3000.000000 | 3000.000000 | 3000.000000 | 3000.000000 | 1.0 | 129 | 453 | 324 | 0 |
| 1 | 1 | 1 | 0.00 | 3825.590088 | 10242.940430 | 20437.449219 | 31.0 | 3807.000000 | 10324.440430 | 20714.269531 | ... | 3.0 | 4040.330078 | 8892.599609 | 4040.330078 | 8892.599609 | 123.0 | 25 | 375 | 350 | 0 |
| 2 | 2 | 0 | NaN | 1271.839966 | 2003.540039 | 4292.620117 | 11.0 | 1297.760010 | 2780.510010 | 5755.069824 | ... | 3.0 | 1777.000000 | 3862.199951 | 1777.000000 | 3842.199951 | 23.0 | 91 | 426 | 335 | 0 |
| 3 | 3 | 2 | NaN | 0.000000 | 0.000000 | 0.000000 | 0.0 | 21770.460938 | 21770.460938 | 21770.460938 | ... | NaN | 20959.980469 | 20959.980469 | 0.000000 | 0.000000 | 0.0 | 180 | 472 | 292 | 0 |
| 4 | 4 | 3 | NaN | 8721.959961 | 10261.959961 | 10261.959961 | 6.0 | 7180.850098 | 8723.849609 | 8723.849609 | ... | NaN | 378.500000 | 378.500000 | 0.000000 | 0.000000 | 0.0 | 95 | 382 | 287 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 18978 | 2 | 0.56 | 1565.000000 | 1565.000000 | 1565.000000 | 2.0 | 4876.439941 | 4876.439941 | 4876.439941 | ... | NaN | 1204.420044 | 1204.420044 | 0.000000 | 0.000000 | 0.0 | 106 | 368 | 262 | 0 |
| 18979 | 18979 | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 526 | 26 | 0 |
| 18980 | 18980 | 2 | 0.00 | 16270.559570 | 76942.039062 | 100723.296875 | 26.0 | 20111.189453 | 76431.796875 | 103675.773438 | ... | 3.0 | 8654.040039 | 13834.540039 | 4483.000000 | 9598.500000 | 27.0 | 21 | 291 | 270 | 0 |
| 18981 | 18981 | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 530 | 30 | 0 |
| 18982 | 18982 | 0 | 0.00 | 6671.049805 | 10642.000000 | 16146.040039 | 32.0 | 6514.939941 | 10849.290039 | 16147.509766 | ... | 3.0 | 6500.120117 | 9294.429688 | 6468.120117 | 9252.429688 | 114.0 | 30 | 351 | 321 | 0 |
18983 rows × 41 columns
# Atribuindo valores X e y (df_drop_anova_mi_complete)
X = df_drop_anova_mi_complete.fillna(0)
X = pd.get_dummies(X, columns=['bank'])
X = X.drop(['user_id'], axis=1)
X = X.drop(['label'], axis=1)
# Display the resulting dataframe X
display(X, y)
y = df_drop_anova_mi_complete['label']
| K00077 | K00092 | K00093 | K00094 | K00095 | K00098 | K00099 | K00100 | K00155 | K00156 | ... | bank_14 | bank_15 | bank_16 | bank_17 | bank_18 | bank_19 | bank_20 | bank_21 | bank_22 | bank_23 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.00 | 74.199997 | 3074.199951 | 3074.199951 | 2.0 | 83.070000 | 3083.070068 | 3083.070068 | 0.000000 | 3000.000000 | ... | False | False | False | False | False | False | False | False | False | False |
| 1 | 0.00 | 3825.590088 | 10242.940430 | 20437.449219 | 31.0 | 3807.000000 | 10324.440430 | 20714.269531 | 3825.340088 | 10201.349609 | ... | False | False | False | False | False | False | False | False | False | False |
| 2 | 0.00 | 1271.839966 | 2003.540039 | 4292.620117 | 11.0 | 1297.760010 | 2780.510010 | 5755.069824 | 1271.839966 | 2003.540039 | ... | False | False | False | False | False | False | False | False | False | False |
| 3 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 21770.460938 | 21770.460938 | 21770.460938 | 0.000000 | 0.000000 | ... | False | False | False | False | False | False | False | False | False | False |
| 4 | 0.00 | 8721.959961 | 10261.959961 | 10261.959961 | 6.0 | 7180.850098 | 8723.849609 | 8723.849609 | 0.010000 | 40.009998 | ... | False | False | False | False | False | False | False | False | False | False |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 0.56 | 1565.000000 | 1565.000000 | 1565.000000 | 2.0 | 4876.439941 | 4876.439941 | 4876.439941 | 1565.000000 | 1565.000000 | ... | False | False | False | False | False | False | False | False | False | False |
| 18979 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | False | False | False | False | False | False | False | False | False | False |
| 18980 | 0.00 | 16270.559570 | 76942.039062 | 100723.296875 | 26.0 | 20111.189453 | 76431.796875 | 103675.773438 | 4197.600098 | 26182.599609 | ... | False | False | False | False | False | False | False | False | False | False |
| 18981 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | False | False | False | False | False | False | False | False | False | False |
| 18982 | 0.00 | 6671.049805 | 10642.000000 | 16146.040039 | 32.0 | 6514.939941 | 10849.290039 | 16147.509766 | 5113.000000 | 8078.399902 | ... | False | False | False | False | False | False | False | False | False | False |
18983 rows × 62 columns
0 0
1 0
2 0
3 0
4 0
..
18978 0
18979 0
18980 0
18981 0
18982 0
Name: label, Length: 18983, dtype: int64
#X = np.nan_to_num(X, nan=0)
### Baseline Model com as variaveis selecionadas ###
start_timer()
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.metrics import accuracy_score, f1_score, precision_score, roc_auc_score, balanced_accuracy_score
from xgboost import XGBClassifier
from sklearn.tree import DecisionTreeClassifier
# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
# Fit the model
#model = DecisionTreeClassifier(random_state=42, class_weight='balanced')
#model = SGDClassifier(n_estimators=1000, learning_rate=0.05, n_jobs=16, random_state=42)
#model = XGBClassifier(n_estimators=1000, learning_rate=0.05, n_jobs=16, random_state=42, class_weight='balanced')
#model = RandomForestClassifier(n_estimators=1000 , random_state=42, n_jobs=16, max_depth=None, class_weight='balanced')
model = LogisticRegression(class_weight = 'balanced', max_iter = 20000, n_jobs = 16)
model.fit(X_train, y_train)
# Evaluate the model
yhat = model.predict(X_test)
# Performance Metrics
accuracy = accuracy_score(y_test, yhat)
f1 = f1_score(y_test, yhat)
precision = precision_score(y_test, yhat)
roc_auc = roc_auc_score(y_test, yhat)
balanced_f1 = f1_score(y_test, yhat, average='weighted')
balanced_accuracy = balanced_accuracy_score(y_test, yhat)
print('Accuracy: %.2f%%' % (accuracy * 100))
print('F1 Score: %.3f' % f1)
print('Precision: %.3f' % precision)
print('ROC AUC: %.3f' % roc_auc)
print('Balanced F1 Score: %.3f' % balanced_f1)
print('Balanced Accuracy: %.3f' % balanced_accuracy)
end_timer()
Accuracy: 56.49% F1 Score: 0.125 Precision: 0.070 ROC AUC: 0.577 Balanced F1 Score: 0.680 Balanced Accuracy: 0.577 Tempo de execução: 13.036937713623047 segundos
df_cleandata = pd.read_excel(f'{files_directory}/MBA001_cleandata.xlsx', index_col=False)
df_cleandata = df_cleandata.drop(['Unnamed: 0'], axis=1)
df_cleandata
| user_id | bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00008 | ... | K00532 | K00533 | K00534 | K00535 | K00536 | K00537 | K00538 | K00539 | K00540 | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 14.0 | 14.0 | 14.0 | 14.0 | 0.0 | 0.0 | NaN | NaN | ... | 3000.000000 | NaN | 0.0 | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | 0 |
| 1 | 1 | 1 | 19.0 | 19.0 | 19.0 | 19.0 | 0.0 | 0.0 | 41.0 | 41.0 | ... | 2964.199951 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 |
| 2 | 2 | 0 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | NaN | NaN | ... | 1280.729980 | 0.0 | 0.0 | 20.0 | 0.0 | 0.0 | 1.0 | 1.0 | 20.0 | 0 |
| 3 | 3 | 2 | 21.0 | 21.0 | 21.0 | 21.0 | 0.0 | 0.0 | 310.0 | 310.0 | ... | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 |
| 4 | 4 | 3 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 257.0 | 257.0 | ... | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 18978 | 2 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | 95.0 | 95.0 | ... | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 |
| 18979 | 18979 | 0 | 8.0 | 8.0 | 8.0 | 8.0 | 0.0 | 0.0 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0 |
| 18980 | 18980 | 2 | 16.0 | 16.0 | 16.0 | 16.0 | 0.0 | 0.0 | 217.0 | 217.0 | ... | 3199.500000 | 20.0 | 1090.0 | 1155.0 | 1.0 | 6.0 | 10.0 | 3.0 | 385.0 | 0 |
| 18981 | 18981 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0 |
| 18982 | 18982 | 0 | 12.0 | 12.0 | 12.0 | 12.0 | 0.0 | 0.0 | NaN | NaN | ... | 3084.139893 | 12.0 | 32.0 | 42.0 | 1.0 | 2.0 | 3.0 | 3.0 | 14.0 | 0 |
18983 rows × 543 columns
### Baseline Model com dataframe df_cleandata ###
start_timer()
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.metrics import accuracy_score, f1_score, precision_score, roc_auc_score, balanced_accuracy_score
from xgboost import XGBClassifier
from sklearn.tree import DecisionTreeClassifier
# Atribuindo valores X e y
X = df_cleandata.fillna(0)
X = pd.get_dummies(X, columns=['bank'])
X = X.drop(['user_id'], axis=1)
X = X.drop(['label'], axis=1)
# Display the resulting dataframe X
display(X, y)
y = df_cleandata['label']
# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
# Fit the model
#model = DecisionTreeClassifier(random_state=42, class_weight='balanced')
#model = SGDClassifier(n_estimators=1000, learning_rate=0.05, n_jobs=16, random_state=42)
#model = XGBClassifier(n_estimators=1000, learning_rate=0.05, n_jobs=16, random_state=42, class_weight='balanced')
#model = RandomForestClassifier(n_estimators=1000 , random_state=42, n_jobs=16, max_depth=None, class_weight='balanced')
model = LogisticRegression(class_weight = 'balanced', max_iter = 20000, n_jobs = 16)
model.fit(X_train, y_train)
# Evaluate the model
yhat = model.predict(X_test)
# Performance Metrics
accuracy = accuracy_score(y_test, yhat)
f1 = f1_score(y_test, yhat)
precision = precision_score(y_test, yhat)
roc_auc = roc_auc_score(y_test, yhat)
balanced_f1 = f1_score(y_test, yhat, average='weighted')
balanced_accuracy = balanced_accuracy_score(y_test, yhat)
print('Accuracy: %.2f%%' % (accuracy * 100))
print('F1 Score: %.3f' % f1)
print('Precision: %.3f' % precision)
print('ROC AUC: %.3f' % roc_auc)
print('Balanced F1 Score: %.3f' % balanced_f1)
print('Balanced Accuracy: %.3f' % balanced_accuracy)
end_timer()
| K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00008 | K00009 | K00010 | ... | bank_14 | bank_15 | bank_16 | bank_17 | bank_18 | bank_19 | bank_20 | bank_21 | bank_22 | bank_23 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 14.0 | 14.0 | 14.0 | 14.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | False | False | False | False | False | False | False | False | False | False |
| 1 | 19.0 | 19.0 | 19.0 | 19.0 | 0.0 | 0.0 | 41.0 | 41.0 | 41.0 | 41.0 | ... | False | False | False | False | False | False | False | False | False | False |
| 2 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | False | False | False | False | False | False | False | False | False | False |
| 3 | 21.0 | 21.0 | 21.0 | 21.0 | 0.0 | 0.0 | 310.0 | 310.0 | 310.0 | 310.0 | ... | False | False | False | False | False | False | False | False | False | False |
| 4 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 257.0 | 257.0 | 257.0 | 257.0 | ... | False | False | False | False | False | False | False | False | False | False |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | 95.0 | 95.0 | 95.0 | 95.0 | ... | False | False | False | False | False | False | False | False | False | False |
| 18979 | 8.0 | 8.0 | 8.0 | 8.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | False | False | False | False | False | False | False | False | False | False |
| 18980 | 16.0 | 16.0 | 16.0 | 16.0 | 0.0 | 0.0 | 217.0 | 217.0 | 217.0 | 217.0 | ... | False | False | False | False | False | False | False | False | False | False |
| 18981 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | False | False | False | False | False | False | False | False | False | False |
| 18982 | 12.0 | 12.0 | 12.0 | 12.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | False | False | False | False | False | False | False | False | False | False |
18983 rows × 564 columns
0 0
1 0
2 0
3 0
4 0
..
18978 0
18979 0
18980 0
18981 0
18982 0
Name: label, Length: 18983, dtype: int64
Accuracy: 57.99% F1 Score: 0.141 Precision: 0.079 ROC AUC: 0.615 Balanced F1 Score: 0.691 Balanced Accuracy: 0.615 Tempo de execução: 124.26049423217773 segundos
# Cria função para calcular as metricas de classificação
from sklearn.metrics import confusion_matrix, precision_recall_curve, roc_curve, auc, matthews_corrcoef, cohen_kappa_score, accuracy_score, f1_score, precision_score, recall_score, balanced_accuracy_score
def calculate_classification_metrics(y_true, y_pred_prob):
# Calculate binary predictions
y_pred = (y_pred_prob >= 0.5).astype(int)
# Confusion Matrix
cm = confusion_matrix(y_true, y_pred)
# True Positives, True Negatives, False Positives, False Negatives
TP, TN, FP, FN = cm.ravel()
# Sensitivity (True Positive Rate)
sensitivity = TP / (TP + FN)
# Specificity (True Negative Rate)
specificity = TN / (TN + FP)
# Area Under the ROC Curve (AUC-ROC)
fpr, tpr, _ = roc_curve(y_true, y_pred_prob)
auc_roc = auc(fpr, tpr)
# Area Under the Precision-Recall Curve (AUC-PRC)
precision, recall, _ = precision_recall_curve(y_true, y_pred_prob)
auc_prc = auc(recall, precision)
# Gini Coefficient
gini = 2 * auc_roc - 1
# Kolmogorov-Smirnov (KS) statistic
ks = np.max(tpr - fpr)
# Matthews Correlation Coefficient (MCC)
mcc = matthews_corrcoef(y_true, y_pred)
# Cohen's Kappa
kappa = cohen_kappa_score(y_true, y_pred)
# Accuracy
accuracy = accuracy_score(y_true, y_pred)
# Balanced Accuracy
balanced_accuracy = balanced_accuracy_score(y_true, y_pred)
# F1 Score
f1 = f1_score(y_true, y_pred)
# Weighted F1 Score
f1_weighted = f1_score(y_true, y_pred, average='weighted')
# Precision
precision = precision_score(y_true, y_pred)
# Recall
recall = recall_score(y_true, y_pred)
# Create DataFrame
metrics_df = pd.DataFrame({
'Class': ['Combined', 'Class 0', 'Class 1'],
'TP': [TP, cm[0, 0], cm[1, 1]],
'TN': [TN, cm[1, 1], cm[0, 0]],
'FP': [FP, cm[0, 1], cm[1, 0]],
'FN': [FN, cm[1, 0], cm[0, 1]],
'Sensitivity_TPR': [sensitivity] * 3,
'Specificity': [specificity] * 3,
'AUC_ROC': [auc_roc] * 3,
'AUC_PRC': [auc_prc] * 3,
'Gini': [gini] * 3,
'KS': [ks] * 3,
'MCC': [mcc] * 3,
'Kappa': [kappa] * 3,
'Accuracy': [accuracy] * 3,
'Balanced_Accuracy': [balanced_accuracy] * 3,
'F1': [f1] * 3,
'F1_Weighted': [f1_weighted] * 3,
'Precision': [precision] * 3,
'Recall': [recall] * 3
})
return metrics_df
metrics_df_example = calculate_classification_metrics(y_test, yhat)
display(metrics_df_example)
| Class | TP | TN | FP | FN | Sensitivity_TPR | Specificity | AUC_ROC | AUC_PRC | Gini | KS | MCC | Kappa | Accuracy | Balanced_Accuracy | F1 | F1_Weighted | Precision | Recall | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Combined | 2071 | 1526 | 69 | 131 | 0.940509 | 0.95674 | 0.615379 | 0.376115 | 0.230758 | 0.230758 | 0.103938 | 0.051973 | 0.579932 | 0.615379 | 0.141088 | 0.691383 | 0.079059 | 0.655 |
| 1 | Class 0 | 2071 | 131 | 1526 | 69 | 0.940509 | 0.95674 | 0.615379 | 0.376115 | 0.230758 | 0.230758 | 0.103938 | 0.051973 | 0.579932 | 0.615379 | 0.141088 | 0.691383 | 0.079059 | 0.655 |
| 2 | Class 1 | 131 | 2071 | 69 | 1526 | 0.940509 | 0.95674 | 0.615379 | 0.376115 | 0.230758 | 0.230758 | 0.103938 | 0.051973 | 0.579932 | 0.615379 | 0.141088 | 0.691383 | 0.079059 | 0.655 |
# Cria função para calcular as metricas de classificação # Only Class 1 metrics
import pandas as pd
import numpy as np
from sklearn.metrics import confusion_matrix, precision_recall_curve, roc_curve, auc, matthews_corrcoef, cohen_kappa_score, accuracy_score, f1_score, precision_score, recall_score, balanced_accuracy_score
def calculate_classification_metrics_one_class(y_true, y_pred_prob, target_class=1):
# Calculate binary predictions
y_pred = (y_pred_prob >= 0.5).astype(int)
# Confusion Matrix
cm = confusion_matrix(y_true, y_pred)
# True Positives, True Negatives, False Positives, False Negatives
TP, TN, FP, FN = cm.ravel()
# Sensitivity (True Positive Rate)
sensitivity = TP / (TP + FN)
# Specificity (True Negative Rate)
specificity = TN / (TN + FP)
# Area Under the ROC Curve (AUC-ROC)
fpr, tpr, _ = roc_curve(y_true, y_pred_prob)
auc_roc = auc(fpr, tpr)
# Area Under the Precision-Recall Curve (AUC-PRC)
precision, recall, _ = precision_recall_curve(y_true, y_pred_prob)
auc_prc = auc(recall, precision)
# Gini Coefficient
gini = 2 * auc_roc - 1
# Kolmogorov-Smirnov (KS) statistic
ks = np.max(tpr - fpr)
# Matthews Correlation Coefficient (MCC)
mcc = matthews_corrcoef(y_true, y_pred)
# Cohen's Kappa
kappa = cohen_kappa_score(y_true, y_pred)
# Accuracy
accuracy = accuracy_score(y_true, y_pred)
# Balanced Accuracy
balanced_accuracy = balanced_accuracy_score(y_true, y_pred)
# F1 Score
f1 = f1_score(y_true, y_pred)
# Weighted F1 Score
f1_weighted = f1_score(y_true, y_pred, average='weighted')
# Precision
precision = precision_score(y_true, y_pred)
# Recall
recall = recall_score(y_true, y_pred)
# Create DataFrame
metrics_df = pd.DataFrame({
'Class': [f'Class {target_class}'],
'TP': [TP],
'TN': [TN],
'FP': [FP],
'FN': [FN],
'Sensitivity_TPR': [sensitivity],
'Specificity': [specificity],
'AUC_ROC': [auc_roc],
'AUC_PRC': [auc_prc],
'Gini': [gini],
'KS': [ks],
'MCC': [mcc],
'Kappa': [kappa],
'Accuracy': [accuracy],
'Balanced_Accuracy': [balanced_accuracy],
'F1': [f1],
'F1_Weighted': [f1_weighted],
'Precision': [precision],
'Recall': [recall]
})
return metrics_df
# Exemplo de uso:
metrics_df_example = calculate_classification_metrics_one_class(y_test, yhat, target_class=1)
display(metrics_df_example)
--------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[4], line 87 84 return metrics_df 86 # Exemplo de uso: ---> 87 metrics_df_example = calculate_classification_metrics_one_class(y_test, yhat, target_class=1) 88 display(metrics_df_example) NameError: name 'y_test' is not defined
A seleção de variaveis inicial foi realizada com base nos valores de correlação, ANOVA e MI. Apesar das métricas serem similares para um modelo de referencia, houve um ganho no tempo de execução de quase 10 vezes, de 124 segundos para 13 segundos. O que é relevante quando se trata de um modelo de produção, com mais dados disponiveis.
Dados Iniciais: Accuracy: 57.99% F1 Score: 0.141 Precision: 0.079 ROC AUC: 0.615 Balanced F1 Score: 0.691 Balanced Accuracy: 0.615
Accuracy: 87.25% F1 Score: 0.171 Precision: 0.130 ROC AUC: 0.579 Balanced F1 Score: 0.891 Balanced Accuracy: 0.579
DT - All variable - CW Accuracy: 86.67% F1 Score: 0.157 Precision: 0.117 ROC AUC: 0.568 Balanced F1 Score: 0.887 Balanced Accuracy: 0.568
DT - All variable - No CW Accuracy: 89.70% F1 Score: 0.097 Precision: 0.090 ROC AUC: 0.523 Balanced F1 Score: 0.901 Balanced Accuracy: 0.523
DT - 36 - CW Accuracy: 83.20% F1 Score: 0.163 Precision: 0.110 ROC AUC: 0.585 Balanced F1 Score: 0.867 Balanced Accuracy: 0.585
DT - 36 - No CW Accuracy: 90.44% F1 Score: 0.076 Precision: 0.078 ROC AUC: 0.513 Balanced F1 Score: 0.904 Balanced Accuracy: 0.513
XG - 36 - CW Accuracy: 94.63% F1 Score: 0.019 Precision: 0.250 ROC AUC: 0.504 Balanced F1 Score: 0.922 Balanced Accuracy: 0.504
XG - 36 - No CW Accuracy: 94.63% F1 Score: 0.019 Precision: 0.250 ROC AUC: 0.504 Balanced F1 Score: 0.922 Balanced Accuracy: 0.504
RF - All variable - CW Accuracy: 90.81% F1 Score: 0.134 Precision: 0.133 ROC AUC: 0.543 Balanced F1 Score: 0.908 Balanced Accuracy: 0.543
RF - All variable - No CW Accuracy: 94.50% F1 Score: 0.019 Precision: 0.154 ROC AUC: 0.503 Balanced F1 Score: 0.921 Balanced Accuracy: 0.503
XG - All variable - CW Accuracy: 94.68% F1 Score: 0.019 Precision: 0.333 ROC AUC: 0.504 Balanced F1 Score: 0.922 Balanced Accuracy: 0.504
XG - All variable - No CW Accuracy: 94.68% F1 Score: 0.019 Precision: 0.333 ROC AUC: 0.504 Balanced F1 Score: 0.922 Balanced Accuracy: 0.504
df_cleandata[['count_of_nan', 'count_of_nan_zero', 'count_of_zero']] = df_droped_001[['count_of_nan', 'count_of_nan_zero', 'count_of_zero']]
df_cleandata
| user_id | bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00008 | ... | K00535 | K00536 | K00537 | K00538 | K00539 | K00540 | label | count_of_nan | count_of_nan_zero | count_of_zero | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 14.0 | 14.0 | 14.0 | 14.0 | 0.0 | 0.0 | NaN | NaN | ... | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | 0 | 129 | 453 | 324 |
| 1 | 1 | 1 | 19.0 | 19.0 | 19.0 | 19.0 | 0.0 | 0.0 | 41.0 | 41.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 | 25 | 375 | 350 |
| 2 | 2 | 0 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | NaN | NaN | ... | 20.0 | 0.0 | 0.0 | 1.0 | 1.0 | 20.0 | 0 | 91 | 426 | 335 |
| 3 | 3 | 2 | 21.0 | 21.0 | 21.0 | 21.0 | 0.0 | 0.0 | 310.0 | 310.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 | 180 | 472 | 292 |
| 4 | 4 | 3 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 257.0 | 257.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 | 95 | 382 | 287 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 18978 | 2 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | 95.0 | 95.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0 | 106 | 368 | 262 |
| 18979 | 18979 | 0 | 8.0 | 8.0 | 8.0 | 8.0 | 0.0 | 0.0 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 0 | 500 | 526 | 26 |
| 18980 | 18980 | 2 | 16.0 | 16.0 | 16.0 | 16.0 | 0.0 | 0.0 | 217.0 | 217.0 | ... | 1155.0 | 1.0 | 6.0 | 10.0 | 3.0 | 385.0 | 0 | 21 | 291 | 270 |
| 18981 | 18981 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 0 | 500 | 530 | 30 |
| 18982 | 18982 | 0 | 12.0 | 12.0 | 12.0 | 12.0 | 0.0 | 0.0 | NaN | NaN | ... | 42.0 | 1.0 | 2.0 | 3.0 | 3.0 | 14.0 | 0 | 30 | 351 | 321 |
18983 rows × 546 columns
print(df_IV_001.loc[df_IV_001['Feature'] != 'label'])
Feature IV 102 K00117 0.293708 267 K00307 0.285362 101 K00116 0.265698 476 count_of_nan 0.264868 141 K00156 0.262254 244 K00282 0.259555 103 K00118 0.257652 140 K00155 0.245358 142 K00157 0.244759 90 K00105 0.239790 78 K00093 0.237117 273 K00313 0.236045 243 K00281 0.235114 165 K00180 0.233894 245 K00283 0.233654 268 K00308 0.231528 89 K00104 0.230779 79 K00094 0.230759 77 K00092 0.230129 107 K00122 0.228140 108 K00123 0.226998 85 K00100 0.226909 91 K00106 0.224047 477 count_of_nan_zero 0.223807 166 K00181 0.221063 266 K00306 0.219670 109 K00124 0.218431 84 K00099 0.217680 95 K00110 0.217470 83 K00098 0.216294 96 K00111 0.213073 147 K00162 0.211513 148 K00163 0.210402 97 K00112 0.209657 164 K00179 0.208522 146 K00161 0.205365 272 K00312 0.202736 425 K00485 0.200730 462 K00526 0.195744 247 K00285 0.195715 105 K00120 0.194380 144 K00159 0.193100 145 K00160 0.191877 106 K00121 0.189049 463 K00527 0.187860 248 K00286 0.186899 271 K00311 0.186745 424 K00484 0.183749 94 K00109 0.183326 426 K00486 0.181226 81 K00096 0.180354 93 K00108 0.179036 270 K00310 0.177023 82 K00097 0.176811 104 K00119 0.176604 478 count_of_zero 0.174835 80 K00095 0.171099 143 K00158 0.170801 168 K00183 0.170483 468 K00532 0.165676 92 K00107 0.165097 461 K00525 0.159694 150 K00165 0.158824 151 K00166 0.157135 269 K00309 0.156597 169 K00184 0.155907 428 K00488 0.154718 246 K00284 0.154692 465 K00529 0.152939 466 K00530 0.145067 149 K00164 0.143525 111 K00126 0.142363 167 K00182 0.141067 87 K00102 0.140002 125 K00140 0.139547 126 K00141 0.139200 99 K00114 0.138751 467 K00531 0.138057 429 K00489 0.137803 110 K00125 0.136065 31 K00042 0.135852 88 K00103 0.135174 75 K00086 0.133494 74 K00085 0.133490 34 K00045 0.133440 127 K00142 0.132486 427 K00487 0.131255 112 K00127 0.131074 76 K00087 0.130009 100 K00115 0.129169 22 K00033 0.129081 33 K00044 0.128731 25 K00036 0.128550 28 K00039 0.128030 86 K00101 0.127636 21 K00032 0.125969 27 K00038 0.125732 98 K00113 0.122427 435 K00495 0.121836 19 K00030 0.120599 464 K00528 0.119034 35 K00046 0.118222 20 K00031 0.117248 434 K00494 0.115885 26 K00037 0.115540 154 K00169 0.114813 36 K00047 0.114272 32 K00043 0.110997 439 K00500 0.110731 250 K00288 0.109018 438 K00498 0.105900 30 K00041 0.105304 29 K00040 0.105282 431 K00491 0.105218 153 K00168 0.103279 432 K00492 0.101988 157 K00172 0.101503 430 K00490 0.100991 249 K00287 0.094677 1 K00001 0.091833 4 K00004 0.091174 3 K00003 0.091075 2 K00002 0.090353 251 K00289 0.090107 437 K00497 0.089054 156 K00171 0.084430 433 K00493 0.083080 152 K00167 0.082346 470 K00534 0.076538 61 K00072 0.076059 469 K00533 0.074703 339 K00399 0.074456 335 K00395 0.072951 63 K00074 0.072305 471 K00535 0.069820 474 K00538 0.069492 64 K00075 0.068626 336 K00396 0.066822 338 K00398 0.066536 43 K00054 0.065690 475 K00540 0.065302 55 K00066 0.065106 47 K00058 0.065017 473 K00537 0.064541 62 K00073 0.062009 69 K00080 0.061969 70 K00081 0.061371 58 K00069 0.061064 57 K00068 0.061027 67 K00078 0.060682 9 K00014 0.059134 45 K00056 0.057856 8 K00013 0.057652 129 K00144 0.057567 56 K00067 0.057558 334 K00394 0.056760 10 K00015 0.054569 54 K00065 0.054384 65 K00076 0.053683 472 K00536 0.053365 53 K00064 0.053086 130 K00145 0.052723 48 K00059 0.052508 46 K00057 0.052124 11 K00016 0.051786 399 K00459 0.050857 68 K00079 0.050290 49 K00060 0.049522 73 K00084 0.048754 396 K00456 0.048118 133 K00148 0.047565 51 K00062 0.047493 118 K00133 0.046961 14 K00019 0.046770 72 K00083 0.046491 18 K00029 0.046416 395 K00455 0.046293 304 K00363 0.045233 71 K00082 0.044993 37 K00048 0.044286 40 K00051 0.043353 337 K00397 0.042201 220 K00258 0.041617 398 K00458 0.041490 265 K00305 0.041010 52 K00063 0.040768 39 K00050 0.040749 114 K00129 0.040023 309 K00369 0.039873 308 K00367 0.039758 307 K00366 0.039465 261 K00300 0.039400 221 K00259 0.038843 132 K00147 0.037724 66 K00077 0.035929 260 K00299 0.035221 24 K00035 0.035197 305 K00364 0.035195 128 K00143 0.034890 303 K00362 0.033631 115 K00130 0.033256 394 K00454 0.031972 117 K00132 0.031464 38 K00049 0.030771 324 K00384 0.029932 44 K00055 0.029135 264 K00303 0.028800 321 K00381 0.028566 7 K00007 0.028465 224 K00262 0.027956 50 K00061 0.027951 254 K00292 0.027754 23 K00034 0.027625 397 K00457 0.027574 436 K00496 0.026020 223 K00261 0.025448 155 K00170 0.024664 263 K00302 0.024552 113 K00128 0.024510 306 K00365 0.024386 222 K00260 0.024260 252 K00290 0.023469 258 K00297 0.022798 16 K00027 0.022579 257 K00295 0.021833 253 K00291 0.021600 323 K00383 0.021600 0 user_id 0.020526 116 K00131 0.018936 160 K00175 0.018745 227 K00265 0.017516 259 K00298 0.017480 135 K00150 0.016956 136 K00151 0.016593 131 K00146 0.016109 262 K00301 0.015967 318 K00378 0.015314 159 K00174 0.015258 219 K00257 0.015204 255 K00293 0.014335 226 K00264 0.014252 297 K00355 0.014006 302 K00361 0.013954 420 K00480 0.013954 327 K00387 0.013938 170 K00191 0.013190 172 K00193 0.012793 419 K00479 0.012793 162 K00177 0.012545 423 K00483 0.012501 326 K00386 0.012377 161 K00176 0.012090 121 K00136 0.011919 158 K00173 0.011784 298 K00356 0.011669 422 K00482 0.011662 300 K00358 0.011370 229 K00267 0.011013 163 K00178 0.010709 139 K00154 0.010536 256 K00294 0.010337 456 K00519 0.009700 120 K00135 0.009672 225 K00263 0.009633 175 K00196 0.009227 12 K00017 0.008913 171 K00192 0.008790 457 K00520 0.008050 460 K00524 0.007995 119 K00134 0.007983 174 K00195 0.007767 13 K00018 0.007743 230 K00268 0.007666 455 K00518 0.007359 228 K00266 0.007117 17 K00028 0.006833 176 K00209 0.006772 134 K00149 0.006490 421 K00481 0.006411 138 K00153 0.006152 301 K00359 0.006031 173 K00194 0.005873 122 K00137 0.005617 137 K00152 0.005365 454 K00517 0.004952 201 K00239 0.004630 204 K00242 0.004558 350 K00410 0.004489 205 K00243 0.004254 348 K00408 0.004162 182 K00217 0.004152 458 K00521 0.003745 206 K00244 0.003626 124 K00139 0.003591 347 K00407 0.003588 351 K00411 0.003552 203 K00241 0.003529 177 K00212 0.003517 178 K00213 0.003517 181 K00216 0.003272 123 K00138 0.003249 459 K00522 0.003155 202 K00240 0.003122 180 K00215 0.003091 179 K00214 0.003090 15 K00026 0.003090 389 K00449 0.003009 384 K00444 0.003009 442 K00503 0.003009 388 K00448 0.003009 441 K00502 0.003009 387 K00447 0.003009 386 K00446 0.003009 440 K00501 0.003009 385 K00445 0.003009 372 K00432 0.003009 383 K00443 0.003009 382 K00442 0.003009 373 K00433 0.003009 369 K00429 0.003009 381 K00441 0.003009 390 K00450 0.003009 370 K00430 0.003009 380 K00440 0.003009 371 K00431 0.003009 379 K00439 0.003009 378 K00438 0.003009 377 K00437 0.003009 376 K00436 0.003009 375 K00435 0.003009 374 K00434 0.003009 443 K00504 0.003009 449 K00511 0.003009 391 K00451 0.003009 407 K00467 0.003009 367 K00427 0.003009 418 K00478 0.003009 417 K00477 0.003009 416 K00476 0.003009 415 K00475 0.003009 414 K00474 0.003009 413 K00473 0.003009 412 K00472 0.003009 411 K00471 0.003009 410 K00470 0.003009 409 K00469 0.003009 408 K00468 0.003009 453 K00516 0.003009 452 K00514 0.003009 406 K00466 0.003009 392 K00452 0.003009 405 K00465 0.003009 404 K00464 0.003009 403 K00463 0.003009 402 K00462 0.003009 401 K00461 0.003009 400 K00460 0.003009 451 K00513 0.003009 450 K00512 0.003009 448 K00510 0.003009 447 K00509 0.003009 446 K00508 0.003009 445 K00506 0.003009 444 K00505 0.003009 393 K00453 0.003009 368 K00428 0.003009 240 K00278 0.003009 366 K00426 0.003009 281 K00322 0.003009 211 K00249 0.003009 212 K00250 0.003009 213 K00251 0.003009 214 K00252 0.003009 215 K00253 0.003009 216 K00254 0.003009 217 K00255 0.003009 218 K00256 0.003009 231 K00269 0.003009 232 K00270 0.003009 233 K00271 0.003009 234 K00272 0.003009 235 K00273 0.003009 236 K00274 0.003009 237 K00275 0.003009 238 K00276 0.003009 239 K00277 0.003009 241 K00279 0.003009 242 K00280 0.003009 274 K00314 0.003009 275 K00315 0.003009 276 K00316 0.003009 277 K00317 0.003009 278 K00318 0.003009 279 K00319 0.003009 210 K00248 0.003009 209 K00247 0.003009 208 K00246 0.003009 188 K00226 0.003009 5 K00005 0.003009 6 K00006 0.003009 41 K00052 0.003009 42 K00053 0.003009 59 K00070 0.003009 60 K00071 0.003009 183 K00221 0.003009 184 K00222 0.003009 185 K00223 0.003009 186 K00224 0.003009 187 K00225 0.003009 189 K00227 0.003009 207 K00245 0.003009 190 K00228 0.003009 191 K00229 0.003009 192 K00230 0.003009 193 K00231 0.003009 194 K00232 0.003009 195 K00233 0.003009 196 K00234 0.003009 197 K00235 0.003009 198 K00236 0.003009 199 K00237 0.003009 200 K00238 0.003009 280 K00321 0.003009 282 K00323 0.003009 365 K00425 0.003009 283 K00324 0.003009 330 K00390 0.003009 331 K00391 0.003009 332 K00392 0.003009 333 K00393 0.003009 340 K00400 0.003009 341 K00401 0.003009 342 K00402 0.003009 343 K00403 0.003009 344 K00404 0.003009 345 K00405 0.003009 346 K00406 0.003009 349 K00409 0.003009 352 K00412 0.003009 353 K00413 0.003009 354 K00414 0.003009 355 K00415 0.003009 356 K00416 0.003009 357 K00417 0.003009 358 K00418 0.003009 359 K00419 0.003009 360 K00420 0.003009 361 K00421 0.003009 362 K00422 0.003009 363 K00423 0.003009 364 K00424 0.003009 329 K00389 0.003009 328 K00388 0.003009 325 K00385 0.003009 295 K00345 0.003009 284 K00325 0.003009 285 K00326 0.003009 286 K00327 0.003009 287 K00328 0.003009 288 K00329 0.003009 289 K00338 0.003009 290 K00339 0.003009 291 K00340 0.003009 292 K00341 0.003009 293 K00342 0.003009 294 K00343 0.003009 296 K00354 0.003009 322 K00382 0.003009 299 K00357 0.003009 310 K00370 0.003009 311 K00371 0.003009 312 K00372 0.003009 313 K00373 0.003009 314 K00374 0.003009 315 K00375 0.003009 316 K00376 0.003009 317 K00377 0.003009 319 K00379 0.003009 320 K00380 0.003009
### Bi-directional Elimination
# Add features in order of df_IV_001 values, drop features according to p-values and confidence intervals
import statsmodels.api as sm
import pandas as pd
from sklearn.preprocessing import StandardScaler
import numpy as np
import logging
start_timer()
# Copy the dataset and fill NaN values with 0
data = df_cleandata.copy()
data = data.fillna(0)
# Separate features (X) and target (y)
X = data.drop("label", axis=1)
y = data["label"]
# Create an empty list to store selected features
selected_features = []
# Define o nível de log para ERROR para o módulo statsmodels
logging.getLogger('statsmodels').setLevel(logging.ERROR)
# Create a function to fit the model and check p-values
def fit_and_check_model(X_selected, y):
scaler = StandardScaler()
X_selected_scaled = scaler.fit_transform(X_selected)
X_selected_scaled = sm.add_constant(X_selected_scaled)
model = sm.Logit(y, X_selected_scaled)
result = model.fit_regularized(alpha=0.6, trim_mode='size', method='l1', max_iter=100000000, disp=False, qc_tol=1e-0)
return result
# Create a function to check if zero is in [0.025, 0.975] interval
def check_zero_interval(arr):
quantiles = np.quantile(arr, [0.025, 0.975])
if quantiles[0] <= 0 <= quantiles[1]:
if quantiles[0] >= 0 >= quantiles[1]:
return True
return False
features = df_IV_001.loc[df_IV_001['Feature'] != 'label']
# Iterate through features in the order of df_IV_001
for feature in features['Feature']:
# Add the current feature to the selected features list
selected_features.append(feature)
# Subset X with selected features
X_selected = X[selected_features]
# Fit and check the model
result = fit_and_check_model(X_selected, y)
# Check if the optimization converged
if result.mle_retvals['converged']:
# Check p-values of existing features and drop if p-value > 0.1
features_dropped = True
while features_dropped:
features_dropped = False
for idx, pvalue in enumerate(result.pvalues[1:]): # Exclude the constant term
if pvalue > 0.1:
print(f"Dropping feature due to high p-value: {selected_features[idx]} - p-value: {pvalue}")
selected_features.pop(idx)
X_selected = X[selected_features]
result = fit_and_check_model(X_selected, y)
features_dropped = True
break
# Check if the new feature's p-value is above 0.05
if result.pvalues[-1] > 0.05:
print(f"Dropping newly added feature due to high p-value: {feature} - p-value: {result.pvalues[-1]}")
selected_features.pop() # Remove the last added feature
else:
# Check if zero is in [0.025, 0.975] interval
if check_zero_interval(X_selected.iloc[:, -1]):
print(f"Dropping newly added feature due to zero in interval: {feature}")
selected_features.pop() # Remove the last added feature
else:
print(result.aic, f"Features added: {selected_features}")
#print(result.summary())
else:
print(f"Features added: {selected_features}")
print("Model did not converge\n")
# Print the final selected features
print(result.summary())
print("Final selected features:", selected_features)
end_timer()
7677.989321143859 Features added: ['K00117']
7675.682248594794 Features added: ['K00117', 'K00307']
Dropping feature due to high p-value: K00307 - p-value: 0.11323884060670035
7672.796914255221 Features added: ['K00117', 'K00116']
7606.494237142275 Features added: ['K00117', 'K00116', 'count_of_nan']
Dropping feature due to high p-value: K00156 - p-value: 0.7491528054115237
7606.494237142275 Features added: ['K00117', 'K00116', 'count_of_nan']
Dropping feature due to high p-value: K00116 - p-value: 0.11537002762492007
Dropping feature due to high p-value: K00117 - p-value: 0.16084321382181288
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00118 - p-value: 0.21474921412838321
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00155 - p-value: 0.8625678099116458
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00157 - p-value: 0.57891894126101
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00105 - p-value: 0.27545764597370925
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00093 - p-value: 0.2009176673805344
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
7606.808480155749 Features added: ['count_of_nan', 'K00282', 'K00313']
Dropping feature due to high p-value: K00313 - p-value: 0.914869181430013
Dropping newly added feature due to high p-value: K00281 - p-value: 0.0855860067372266
Dropping feature due to high p-value: K00180 - p-value: 0.45851488773064186
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00283 - p-value: 0.7580443978239031
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00308 - p-value: 0.5766969666574042
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping newly added feature due to high p-value: K00104 - p-value: 0.07237275602672022
Dropping feature due to high p-value: K00094 - p-value: 0.4269659131549074
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
7606.0498357956985 Features added: ['count_of_nan', 'K00282', 'K00092']
Dropping feature due to high p-value: K00092 - p-value: 0.7346363702899492
7604.6886331561345 Features added: ['count_of_nan', 'K00282', 'K00122']
Dropping feature due to high p-value: K00122 - p-value: 0.13937062896296662
Dropping feature due to high p-value: K00123 - p-value: 0.1123649399063858
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00100 - p-value: 0.11343142944373355
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: K00106 - p-value: 0.3889876998184615
7606.8083448375355 Features added: ['count_of_nan', 'K00282']
Dropping feature due to high p-value: count_of_nan - p-value: 0.18651669679183314
7601.814806000744 Features added: ['K00282', 'count_of_nan_zero']
Dropping feature due to high p-value: K00181 - p-value: 0.344891910841738
7601.814806000744 Features added: ['K00282', 'count_of_nan_zero']
Dropping feature due to high p-value: K00306 - p-value: 0.30204258273080564
7601.814806000744 Features added: ['K00282', 'count_of_nan_zero']
Dropping newly added feature due to high p-value: K00124 - p-value: 0.05995914363457379
7598.096561502643 Features added: ['K00282', 'count_of_nan_zero', 'K00099']
Dropping feature due to high p-value: K00099 - p-value: 0.8338138903424643
7597.665823533905 Features added: ['K00282', 'count_of_nan_zero', 'K00110']
Dropping feature due to high p-value: K00110 - p-value: 0.92495431644053
7596.894761798827 Features added: ['K00282', 'count_of_nan_zero', 'K00098']
Dropping feature due to high p-value: K00098 - p-value: 0.11642252928395672
7599.191172337483 Features added: ['K00282', 'count_of_nan_zero', 'K00111']
Dropping feature due to high p-value: K00162 - p-value: 0.5352140060786326
7599.191172337483 Features added: ['K00282', 'count_of_nan_zero', 'K00111']
Dropping feature due to high p-value: K00163 - p-value: 0.5473461283570313
7599.191172337483 Features added: ['K00282', 'count_of_nan_zero', 'K00111']
7599.1911801258975 Features added: ['K00282', 'count_of_nan_zero', 'K00111', 'K00112']
Dropping feature due to high p-value: K00111 - p-value: 0.7131648746810901
Dropping feature due to high p-value: K00112 - p-value: 0.18015078593045164
Dropping newly added feature due to high p-value: K00179 - p-value: 0.0529186176529721
Dropping feature due to high p-value: K00161 - p-value: 0.3165411038586735
7601.814806000744 Features added: ['K00282', 'count_of_nan_zero']
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00485 - p-value: 0.17275074029394388
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00526 - p-value: 0.46101459593689154
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
7590.968075225388 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00285']
Dropping feature due to high p-value: K00285 - p-value: 0.44605938292380365
Dropping feature due to high p-value: K00120 - p-value: 0.7425856242420678
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00159 - p-value: 0.673285434304763
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00160 - p-value: 0.7907985337401056
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00121 - p-value: 0.7726255988406424
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00527 - p-value: 0.4444378978926208
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00286 - p-value: 0.8546656307882725
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00311 - p-value: 0.8249855020406681
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00484 - p-value: 0.14509453522452473
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00109 - p-value: 0.8798515997895365
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00486 - p-value: 0.19244480347299675
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00096 - p-value: 0.9163783838612488
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00108 - p-value: 0.8289536295007104
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00310 - p-value: 0.905740207463549
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00097 - p-value: 0.9742002753896195
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00119 - p-value: 0.7613383206720492
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: count_of_zero - p-value: 0.5927738612722486
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00095 - p-value: 0.9658795067966314
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
Dropping feature due to high p-value: K00158 - p-value: 0.6018322596834211
7590.968028567717 Features added: ['K00282', 'count_of_nan_zero', 'K00312']
7583.461146136555 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00183']
Dropping feature due to high p-value: K00532 - p-value: 0.9647529584019049
7583.461146136555 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00183']
Dropping feature due to high p-value: K00107 - p-value: 0.45607641394042076
7583.461146136555 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00183']
Dropping feature due to high p-value: K00525 - p-value: 0.9349148379159548
7583.461146136555 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00183']
Dropping feature due to high p-value: K00165 - p-value: 0.10946626253428235
7583.461146136555 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00183']
Dropping newly added feature due to high p-value: K00166 - p-value: 0.05991386539260798
Dropping feature due to high p-value: K00309 - p-value: 0.7062083382016027
7583.461146136555 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00183']
Dropping feature due to high p-value: K00183 - p-value: 0.25398979375185626
7579.339416306051 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00184']
7575.679484837738 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00184', 'K00488']
Dropping feature due to high p-value: K00284 - p-value: 0.35184017698476344
7575.679484837738 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00184', 'K00488']
Dropping feature due to high p-value: K00529 - p-value: 0.2330580364605246
7575.679484837738 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00184', 'K00488']
Dropping feature due to high p-value: K00488 - p-value: 0.1369335098917621
7577.740645004133 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00184', 'K00530']
Dropping feature due to high p-value: K00530 - p-value: 0.1841907478323298
Dropping feature due to high p-value: K00164 - p-value: 0.18489271866386892
7579.339416306051 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00184']
Dropping feature due to high p-value: K00184 - p-value: 0.46820369323189415
7575.627601367731 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126']
Dropping feature due to high p-value: K00182 - p-value: 0.6400426395007348
7575.627601367731 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126']
7574.80494377063 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00102']
Dropping feature due to high p-value: K00140 - p-value: 0.12087221567603797
7574.80494377063 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00102']
Dropping newly added feature due to high p-value: K00141 - p-value: 0.07514665395357001
Dropping feature due to high p-value: K00102 - p-value: 0.6521433116519542
7559.062692364424 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114']
7556.627233868789 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531']
Dropping feature due to high p-value: K00489 - p-value: 0.8259665184899095
7556.627233868789 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531']
7556.62815191475 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00125']
Dropping newly added feature due to high p-value: K00042 - p-value: 0.08866590900183584
Dropping feature due to high p-value: K00125 - p-value: 0.5877548799091821
Dropping newly added feature due to high p-value: K00103 - p-value: 0.08683849080838922
Dropping feature due to high p-value: K00086 - p-value: 0.519097097304089
7556.627233868789 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531']
Dropping feature due to high p-value: K00085 - p-value: 0.24082863403210653
7556.627233868789 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531']
7554.639635995036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00045']
Dropping feature due to high p-value: K00142 - p-value: 0.7179962293224719
7554.639635995036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00045']
Dropping feature due to high p-value: K00487 - p-value: 0.49259707808095654
7554.639635995036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00045']
7551.593234564308 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00045', 'K00127']
Dropping feature due to high p-value: K00087 - p-value: 0.3766139416680305
7551.593234564308 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00045', 'K00127']
7551.613099824102 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00045', 'K00127', 'K00115']
Dropping feature due to high p-value: K00033 - p-value: 0.25056938876096935
7551.613099824102 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00045', 'K00127', 'K00115']
Dropping feature due to high p-value: K00045 - p-value: 0.33111409769075206
7552.533160892499 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115', 'K00044']
Dropping feature due to high p-value: K00044 - p-value: 0.14576989630490816
Dropping feature due to high p-value: K00036 - p-value: 0.13365392906409188
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00039 - p-value: 0.17044850370632403
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00101 - p-value: 0.2896473534259769
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00032 - p-value: 0.11492250514871455
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00038 - p-value: 0.14002287117779208
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00113 - p-value: 0.2708622492831999
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00495 - p-value: 0.12360101450614636
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping newly added feature due to high p-value: K00030 - p-value: 0.0936538111331149
Dropping feature due to high p-value: K00528 - p-value: 0.6776975221276043
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00046 - p-value: 0.25622283165889737
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00031 - p-value: 0.16274221150680535
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00494 - p-value: 0.18655054700872475
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping feature due to high p-value: K00037 - p-value: 0.11740725444710791
7553.581714835036 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00531', 'K00127', 'K00115']
Dropping newly added feature due to high p-value: K00169 - p-value: 0.09740178853680051
Dropping feature due to high p-value: K00531 - p-value: 0.10955910342672533
7552.947380950207 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047']
Dropping newly added feature due to high p-value: K00043 - p-value: 0.07613809708307055
Dropping feature due to high p-value: K00500 - p-value: 0.15337607462342456
7552.947380950207 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047']
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping newly added feature due to high p-value: K00498 - p-value: 0.09462114312557979
Dropping feature due to high p-value: K00041 - p-value: 0.9714176071417053
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping feature due to high p-value: K00040 - p-value: 0.1692827690635328
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping feature due to high p-value: K00491 - p-value: 0.6347386615785753
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping feature due to high p-value: K00168 - p-value: 0.15447083066100104
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping feature due to high p-value: K00492 - p-value: 0.21220113439435084
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping newly added feature due to high p-value: K00172 - p-value: 0.06619800818135914
Dropping feature due to high p-value: K00490 - p-value: 0.699704047487211
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping feature due to high p-value: K00287 - p-value: 0.7981000888363039
7549.442297302143 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00047', 'K00288']
Dropping feature due to high p-value: K00047 - p-value: 0.12640361283356671
7534.317067802609 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001']
7534.318435464542 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004']
7534.318014084953 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003']
7534.318445623482 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003', 'K00002']
7530.658497995007 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00497 - p-value: 0.1935129342422216
7530.658497995007 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00171 - p-value: 0.13728365128780487
7530.658497995007 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00493 - p-value: 0.3265541836860618
7530.658497995007 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00167 - p-value: 0.30405647140419745
7530.658497995007 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00534 - p-value: 0.4478796861837988
7530.658497995007 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00288', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00288 - p-value: 0.9108051076399917
Dropping newly added feature due to high p-value: K00072 - p-value: 0.09452981963714274
Dropping feature due to high p-value: K00533 - p-value: 0.848511045014456
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00399 - p-value: 0.2066876425683779
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00395 - p-value: 0.4188721270224389
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping newly added feature due to high p-value: K00074 - p-value: 0.051243163895292085
Dropping feature due to high p-value: K00535 - p-value: 0.3522700163556125
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00538 - p-value: 0.12538900044114903
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping newly added feature due to high p-value: K00075 - p-value: 0.06075993420095678
Dropping feature due to high p-value: K00396 - p-value: 0.3393320446111262
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00398 - p-value: 0.21941105183459708
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00054 - p-value: 0.5998918006719778
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00540 - p-value: 0.26250105897624976
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00066 - p-value: 0.20648362945775245
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00058 - p-value: 0.5342818397740434
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
Dropping feature due to high p-value: K00537 - p-value: 0.11735252340909139
7530.659029598257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289']
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00080 - p-value: 0.6188078562572181
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
7524.3419354548805 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00081']
Dropping feature due to high p-value: K00081 - p-value: 0.8299464940035459
Dropping feature due to high p-value: K00069 - p-value: 0.5523066752315777
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00068 - p-value: 0.6949789109905691
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00078 - p-value: 0.7766593296958357
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00014 - p-value: 0.22121304270603193
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00056 - p-value: 0.47928457656372303
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00013 - p-value: 0.335416319659378
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00144 - p-value: 0.15391677352213964
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00067 - p-value: 0.6560855571088609
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00394 - p-value: 0.7071237594661308
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00015 - p-value: 0.27450225683668816
7524.341186641328 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00001', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping feature due to high p-value: K00001 - p-value: 0.5701062748373682
Dropping newly added feature due to high p-value: K00065 - p-value: 0.08967589061006827
Dropping feature due to high p-value: K00076 - p-value: 0.9877126740364282
7524.583380010659 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073']
Dropping newly added feature due to high p-value: K00536 - p-value: 0.09037433103924347
7522.542284854546 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064']
Dropping feature due to high p-value: K00145 - p-value: 0.6864107604726889
7522.542284854546 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064']
7511.050997459974 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059']
Dropping feature due to high p-value: K00057 - p-value: 0.23004787372434532
7511.050997459974 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059']
Dropping feature due to high p-value: K00016 - p-value: 0.4560465794311718
7511.050997459974 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059']
Dropping feature due to high p-value: K00459 - p-value: 0.9713833865870524
7511.050997459974 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059']
Dropping feature due to high p-value: K00079 - p-value: 0.4344104289230778
7511.050997459974 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059']
7508.241816998768 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00060']
Dropping feature due to high p-value: K00084 - p-value: 0.7528651714879757
7508.241816998768 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00060']
Dropping feature due to high p-value: K00456 - p-value: 0.3548483122065528
7508.241816998768 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00060']
Dropping feature due to high p-value: K00148 - p-value: 0.8815048361196168
7508.241816998768 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00060']
Dropping feature due to high p-value: K00060 - p-value: 0.2359640061338999
Dropping feature due to high p-value: K00062 - p-value: 0.11263509145121835
7511.050997459974 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059']
7503.59193493374 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133']
Dropping feature due to high p-value: K00019 - p-value: 0.8609185352290248
7503.59193493374 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133']
Dropping feature due to high p-value: K00083 - p-value: 0.22423711374581567
7503.59193493374 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133']
Dropping feature due to high p-value: K00029 - p-value: 0.48027595402738865
7503.59193493374 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133']
Dropping feature due to high p-value: K00455 - p-value: 0.12721260564535175
7503.59193493374 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133']
Dropping feature due to high p-value: K00363 - p-value: 0.9758705158237464
7503.59193493374 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133']
Dropping feature due to high p-value: K00082 - p-value: 0.7764861723630986
7503.59193493374 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133']
7499.955312420818 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00048']
Dropping feature due to high p-value: K00048 - p-value: 0.7885139792831416
7499.583795776505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00051']
Dropping feature due to high p-value: K00397 - p-value: 0.3241801505687303
7499.583795776505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00051']
Dropping feature due to high p-value: K00258 - p-value: 0.4806711811294754
7499.583795776505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00051']
Dropping feature due to high p-value: K00458 - p-value: 0.749964849550375
7499.583795776505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00051']
Dropping feature due to high p-value: K00305 - p-value: 0.3906294344993262
7499.583795776505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00051']
Dropping feature due to high p-value: K00063 - p-value: 0.6715280964399029
7499.583795776505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00051']
Dropping feature due to high p-value: K00051 - p-value: 0.699031434961916
7500.0078449114835 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050']
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00369 - p-value: 0.926724367016016
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00367 - p-value: 0.11052057504043
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00366 - p-value: 0.15486972207663463
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00300 - p-value: 0.20019567124471804
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00259 - p-value: 0.42642005743607725
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00147 - p-value: 0.823567702453935
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00077 - p-value: 0.33291589302741886
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00299 - p-value: 0.25115408446055276
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00035 - p-value: 0.4982167976765226
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
7494.758760508257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129', 'K00364']
Dropping feature due to high p-value: K00143 - p-value: 0.6451732204285346
7494.758760508257 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129', 'K00364']
Dropping feature due to high p-value: K00364 - p-value: 0.8953162605745556
Dropping feature due to high p-value: K00362 - p-value: 0.5415026356289911
7494.759312833194 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00129']
Dropping feature due to high p-value: K00129 - p-value: 0.22274197324650225
7496.917362495638 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00130']
Dropping feature due to high p-value: K00454 - p-value: 0.28243675428535886
7496.917362495638 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00130']
Dropping feature due to high p-value: K00132 - p-value: 0.40039757099692097
7496.917362495638 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00050', 'K00130']
Dropping feature due to high p-value: K00050 - p-value: 0.14230094569720778
Dropping feature due to high p-value: K00049 - p-value: 0.11771182635019845
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping newly added feature due to high p-value: K00384 - p-value: 0.08882653613535794
Dropping feature due to high p-value: K00055 - p-value: 0.3521398298091427
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00303 - p-value: 0.5551833974566255
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00381 - p-value: 0.20119015277420949
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00007 - p-value: 0.15575306389717253
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00262 - p-value: 0.5269429146286725
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping newly added feature due to high p-value: K00061 - p-value: 0.088096124456529
Dropping feature due to high p-value: K00292 - p-value: 0.10291483581487419
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00034 - p-value: 0.11005345334587699
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00457 - p-value: 0.667162339984448
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00496 - p-value: 0.5440956244303219
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00261 - p-value: 0.3695460467555839
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00170 - p-value: 0.4590853391738082
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
Dropping feature due to high p-value: K00302 - p-value: 0.7315969532868962
7500.406506726396 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130']
7495.349290850612 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130', 'K00128']
Dropping feature due to high p-value: K00365 - p-value: 0.6365319541676079
7495.349290850612 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00130', 'K00128']
Dropping newly added feature due to high p-value: K00260 - p-value: 0.08373489217648855
Dropping feature due to high p-value: K00130 - p-value: 0.958271903898335
Dropping newly added feature due to high p-value: K00290 - p-value: 0.061125198022152695
Dropping newly added feature due to high p-value: K00297 - p-value: 0.05894112313435441
Dropping feature due to high p-value: K00027 - p-value: 0.4600908124870977
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00295 - p-value: 0.47186439118808887
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00291 - p-value: 0.14061063390541476
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping newly added feature due to high p-value: K00383 - p-value: 0.09947279325796717
Dropping feature due to high p-value: user_id - p-value: 0.9844098403442806
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00131 - p-value: 0.9452293750702622
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
7495.348966803591 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00175']
Dropping feature due to high p-value: K00265 - p-value: 0.4797298920643476
7495.348966803591 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00175']
Dropping feature due to high p-value: K00298 - p-value: 0.9215141341234256
7495.348966803591 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00175']
Dropping feature due to high p-value: K00150 - p-value: 0.6586913910015506
7495.348966803591 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00175']
Dropping feature due to high p-value: K00151 - p-value: 0.9779066408142667
7495.348966803591 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00175']
Dropping feature due to high p-value: K00146 - p-value: 0.7652154764143018
7495.348966803591 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00175']
Dropping feature due to high p-value: K00301 - p-value: 0.9742852473267096
7495.348966803591 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00175']
Dropping feature due to high p-value: K00175 - p-value: 0.9824978483562365
Dropping feature due to high p-value: K00378 - p-value: 0.4258180753768942
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00174 - p-value: 0.8922154948715607
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00257 - p-value: 0.2980252325135594
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00293 - p-value: 0.28735173371220346
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00264 - p-value: 0.40306087959836434
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00355 - p-value: 0.547283354085306
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00361 - p-value: 0.8812259486496107
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00480 - p-value: 0.290218845244012
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00387 - p-value: 0.22859339497156628
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00191 - p-value: 0.6200428798311264
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
7495.348472469716 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00193']
Dropping feature due to high p-value: K00193 - p-value: 0.24777020222244595
Dropping feature due to high p-value: K00479 - p-value: 0.5549586792792015
7495.3491476214895 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00126', 'K00114', 'K00127', 'K00115', 'K00004', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128']
Dropping feature due to high p-value: K00126 - p-value: 0.9032972227308326
Dropping feature due to high p-value: K00114 - p-value: 0.7372867706427424
Dropping feature due to high p-value: K00004 - p-value: 0.9879605784978006
7477.386514759637 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00177']
Dropping feature due to high p-value: K00483 - p-value: 0.766288923158253
7477.386514759637 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00177']
Dropping feature due to high p-value: K00386 - p-value: 0.1088715777891812
7477.386514759637 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00177']
Dropping feature due to high p-value: K00177 - p-value: 0.2559680311735032
7477.08596702212 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176']
Dropping feature due to high p-value: K00136 - p-value: 0.9848847083690407
7477.08596702212 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176']
Dropping feature due to high p-value: K00173 - p-value: 0.7590051943896516
7477.08596702212 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176']
Dropping feature due to high p-value: K00356 - p-value: 0.8790214669779173
7477.08596702212 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176']
Dropping feature due to high p-value: K00482 - p-value: 0.9734808665540835
7477.08596702212 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176']
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00267 - p-value: 0.2850361075244111
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00178 - p-value: 0.27607519859933893
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00154 - p-value: 0.195253936827269
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00294 - p-value: 0.4519478647167403
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00519 - p-value: 0.9899990730449176
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00135 - p-value: 0.9275426797744033
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00263 - p-value: 0.2989325046102379
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping newly added feature due to high p-value: K00196 - p-value: 0.06332024195223265
Dropping feature due to high p-value: K00017 - p-value: 0.4458064474197858
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00192 - p-value: 0.9072765396125244
7472.5926461946765 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00127', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00127 - p-value: 0.9634303110493149
Dropping feature due to high p-value: K00520 - p-value: 0.23503047473249394
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00524 - p-value: 0.8046491154561416
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00134 - p-value: 0.8962844625199424
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00195 - p-value: 0.24884337664069323
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00018 - p-value: 0.34306129552350617
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping newly added feature due to high p-value: K00268 - p-value: 0.06354220550218886
Dropping feature due to high p-value: K00518 - p-value: 0.9690876007137234
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00266 - p-value: 0.4478346703627688
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
7472.592261941081 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358', 'K00028']
Dropping feature due to high p-value: K00209 - p-value: 0.36854793644078576
7472.592261941081 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358', 'K00028']
Dropping feature due to high p-value: K00149 - p-value: 0.7249999799278605
7472.592261941081 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358', 'K00028']
Dropping feature due to high p-value: K00481 - p-value: 0.766114249359537
7472.592261941081 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358', 'K00028']
Dropping feature due to high p-value: K00028 - p-value: 0.9799097986839702
Dropping feature due to high p-value: K00153 - p-value: 0.12906280799245656
7472.592146618909 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00358']
Dropping feature due to high p-value: K00358 - p-value: 0.6391650729952021
7472.752485434326 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359']
7472.752629272268 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00194']
Dropping feature due to high p-value: K00194 - p-value: 0.9966129648239309
Dropping feature due to high p-value: K00137 - p-value: 0.1756002039575536
7472.752485434326 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359']
Dropping feature due to high p-value: K00152 - p-value: 0.27292150542467497
7472.752485434326 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359']
Dropping feature due to high p-value: K00517 - p-value: 0.8725080464157869
7472.752485434326 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359']
Dropping feature due to high p-value: K00239 - p-value: 0.5553624702671622
7472.752485434326 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359']
Dropping feature due to high p-value: K00242 - p-value: 0.14993827082482455
7472.752485434326 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359']
Dropping feature due to high p-value: K00410 - p-value: 0.5152370574840717
7472.752485434326 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359']
7470.387316805825 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00243']
Dropping feature due to high p-value: K00408 - p-value: 0.8688473948759272
7470.387316805825 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00243']
7465.5265238294505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00243', 'K00217']
Dropping feature due to high p-value: K00521 - p-value: 0.14687857232689042
7465.5265238294505 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00243', 'K00217']
Dropping feature due to high p-value: K00243 - p-value: 0.7747185369822046
7464.582014666059 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00217', 'K00244']
Dropping feature due to high p-value: K00139 - p-value: 0.23629674619171992
7464.582014666059 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00217', 'K00244']
Dropping feature due to high p-value: K00407 - p-value: 0.7315039482339845
7464.582014666059 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00217', 'K00244']
Dropping feature due to high p-value: K00411 - p-value: 0.25781386847102206
7464.582014666059 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00217', 'K00244']
Dropping feature due to high p-value: K00241 - p-value: 0.9254518619375817
7464.582014666059 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00217', 'K00244']
Dropping feature due to high p-value: K00212 - p-value: 0.4170578506641157
7464.582014666059 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00217', 'K00244']
Dropping feature due to high p-value: K00213 - p-value: 0.5383731259724079
7464.582014666059 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00217', 'K00244']
Dropping feature due to high p-value: K00217 - p-value: 0.662894795770799
7464.5457991337435 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00216']
Dropping feature due to high p-value: K00138 - p-value: 0.1420635330121469
7464.5457991337435 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00216']
Dropping feature due to high p-value: K00522 - p-value: 0.21632005000991938
7464.5457991337435 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00216']
Dropping feature due to high p-value: K00240 - p-value: 0.8726524265364154
7464.5457991337435 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00216']
Dropping feature due to high p-value: K00216 - p-value: 0.36490388245286676
7465.304079628008 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215']
Dropping feature due to high p-value: K00214 - p-value: 0.36426976297681934
7465.304079628008 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00003', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215']
Dropping feature due to high p-value: K00003 - p-value: 0.5894124013727395
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00449 - p-value: 0.18477666678166238
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00444 - p-value: 0.327020100035051
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00503 - p-value: 0.7526304240222759
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00448 - p-value: 0.13689583937095923
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00502 - p-value: 0.8484100362673181
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00447 - p-value: 0.10836185962121456
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00446 - p-value: 0.10589713646632525
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00501 - p-value: 0.6866925642848738
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00445 - p-value: 0.1439340021417115
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00432 - p-value: 0.609512368192529
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00443 - p-value: 0.40107542039851796
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00442 - p-value: 0.4029994202368219
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00433 - p-value: 0.6635091848857968
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00429 - p-value: 0.4992413626160377
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00441 - p-value: 0.8492127140288965
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00450 - p-value: 0.4867828191068616
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00430 - p-value: 0.7050120405425861
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00440 - p-value: 0.9276462446556492
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00431 - p-value: 0.6069374830368126
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00439 - p-value: 0.92407679101592
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00438 - p-value: 0.4346057333880222
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00437 - p-value: 0.475204922038733
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00436 - p-value: 0.4407865111442988
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00435 - p-value: 0.6960096594143398
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00434 - p-value: 0.6009757726338087
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00504 - p-value: 0.45636251395769545
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00511 - p-value: 0.6046060934639899
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping newly added feature due to zero in interval: K00451
Dropping newly added feature due to zero in interval: K00467
Dropping feature due to high p-value: K00427 - p-value: 0.6119784901689902
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00478 - p-value: 0.13700842160653964
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00477 - p-value: 0.7771263899601096
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00476 - p-value: 0.9398804145790844
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00475 - p-value: 0.7061180815518164
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00474 - p-value: 0.9697275341565842
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00473 - p-value: 0.5386100576640832
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00472 - p-value: 0.7291305779172426
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping newly added feature due to zero in interval: K00471
Dropping newly added feature due to zero in interval: K00470
Dropping feature due to high p-value: K00469 - p-value: 0.9275846506157734
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping newly added feature due to zero in interval: K00468
Dropping feature due to high p-value: K00516 - p-value: 0.5723867654632382
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00514 - p-value: 0.6299123690805613
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping newly added feature due to zero in interval: K00466
Dropping newly added feature due to zero in interval: K00452
Dropping feature due to high p-value: K00465 - p-value: 0.553403007025667
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00464 - p-value: 0.7302597730826363
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00463 - p-value: 0.33381457647768864
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00462 - p-value: 0.7006439778641894
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00461 - p-value: 0.9275622790425962
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00460 - p-value: 0.6694137501612604
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00513 - p-value: 0.6850680829010101
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00512 - p-value: 0.6722195210399229
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00510 - p-value: 0.6259003871862536
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00509 - p-value: 0.6744780846830014
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00508 - p-value: 0.397702898641767
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00506 - p-value: 0.49213473720345025
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00505 - p-value: 0.8355367294715329
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00453 - p-value: 0.24439130624549654
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00428 - p-value: 0.5392685128410115
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00278 - p-value: 0.7322620175408185
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping feature due to high p-value: K00426 - p-value: 0.6044294123329099
7439.785199540154 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00002', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026']
Dropping newly added feature due to zero in interval: K00322
Dropping feature due to high p-value: K00002 - p-value: 0.22227562995278782
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00250 - p-value: 0.6662925629221581
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00251 - p-value: 0.7933874920136311
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00252 - p-value: 0.5213821916514447
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00253 - p-value: 0.367024016346987
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00254 - p-value: 0.415644122782564
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00255 - p-value: 0.5681124485224522
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00256 - p-value: 0.4035524787416388
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00269 - p-value: 0.4434883110215513
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00270 - p-value: 0.25281952957341913
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00271 - p-value: 0.20841102866079164
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00272 - p-value: 0.5492915081756828
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00273 - p-value: 0.1864752941270149
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00274 - p-value: 0.10258551819074314
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00275 - p-value: 0.9373751989695304
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00276 - p-value: 0.6153492891711279
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00277 - p-value: 0.8921242731077832
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00279 - p-value: 0.5208212693143783
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00280 - p-value: 0.9339869726803733
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00314 - p-value: 0.5546020663262858
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
7417.260007633073 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00315']
Dropping feature due to high p-value: K00315 - p-value: 0.34475790825808084
Dropping newly added feature due to high p-value: K00316 - p-value: 0.08668482064097137
Dropping feature due to high p-value: K00317 - p-value: 0.8583573255753792
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00318 - p-value: 0.3653372157977791
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00319 - p-value: 0.7805011197664813
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00248 - p-value: 0.5956044086761612
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00247 - p-value: 0.8357735631083916
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00246 - p-value: 0.19629625745852286
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00226 - p-value: 0.5780803949168418
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00005 - p-value: 0.18402056223452168
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00006 - p-value: 0.26161719346721135
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
Dropping feature due to high p-value: K00052 - p-value: 0.13491176765490387
7418.72139429462 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249']
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00070 - p-value: 0.6572078017564182
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00071 - p-value: 0.49318179447619903
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00221 - p-value: 0.8441911788910093
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00222 - p-value: 0.32625536123467525
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00223 - p-value: 0.13476918024366816
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00224 - p-value: 0.7527002195815373
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00225 - p-value: 0.7333521740980189
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping newly added feature due to high p-value: K00227 - p-value: 0.05711662356529846
Dropping feature due to high p-value: K00245 - p-value: 0.4174145829364916
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00228 - p-value: 0.35017748589137165
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00229 - p-value: 0.6978540521795069
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00230 - p-value: 0.24506783609189586
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00231 - p-value: 0.5327668583609281
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00232 - p-value: 0.841555810539727
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00233 - p-value: 0.6172599622943844
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00234 - p-value: 0.7594664250323923
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00235 - p-value: 0.5443760510652531
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00236 - p-value: 0.7080907096225817
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00237 - p-value: 0.47976549287681114
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
Dropping feature due to high p-value: K00238 - p-value: 0.4954981128514142
7413.455858825479 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053']
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00323 - p-value: 0.31688052618428497
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00425 - p-value: 0.6225907441806231
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00324 - p-value: 0.6581114095884815
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00390 - p-value: 0.2308049389602569
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping newly added feature due to zero in interval: K00391
Dropping feature due to high p-value: K00392 - p-value: 0.8005122134972532
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00393 - p-value: 0.4347095412431745
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00400 - p-value: 0.5206368636867875
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00401 - p-value: 0.38879279507555053
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00402 - p-value: 0.37627281460902706
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00403 - p-value: 0.43502505867078867
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00404 - p-value: 0.3465089410491542
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00405 - p-value: 0.3277758190102539
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00406 - p-value: 0.39340487571386207
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00409 - p-value: 0.1290256333040721
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00412 - p-value: 0.7772069651149762
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00413 - p-value: 0.6261343658351901
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00414 - p-value: 0.3299442622179729
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00415 - p-value: 0.9749874619881497
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00416 - p-value: 0.9431404922565013
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00417 - p-value: 0.4062613472012272
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00418 - p-value: 0.38691051911097285
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00419 - p-value: 0.2876295510302317
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00420 - p-value: 0.20338777918439743
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00421 - p-value: 0.8367882951231183
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00422 - p-value: 0.3967253568693896
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00423 - p-value: 0.21861904227438345
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00424 - p-value: 0.667678824625199
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00389 - p-value: 0.3938208329876738
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
Dropping feature due to high p-value: K00388 - p-value: 0.9638279008843306
7412.029390526199 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321']
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00345 - p-value: 0.6298476690842909
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00325 - p-value: 0.23465167694170397
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00326 - p-value: 0.5311769226963218
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00327 - p-value: 0.8522351224162665
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00328 - p-value: 0.9322040246867869
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00329 - p-value: 0.23348963765953734
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00338 - p-value: 0.7148181533837987
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00339 - p-value: 0.6729996004678711
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00340 - p-value: 0.6347535906777577
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00341 - p-value: 0.7079969603628723
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00342 - p-value: 0.6778403780876028
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00343 - p-value: 0.6275541569403137
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00354 - p-value: 0.24268903949540932
7406.836416580877 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00385']
Dropping feature due to high p-value: K00385 - p-value: 0.47150527752142746
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00357 - p-value: 0.5631026252184069
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00370 - p-value: 0.7599429075517683
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00371 - p-value: 0.6545544138665655
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00372 - p-value: 0.5567987985198677
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00373 - p-value: 0.7626737074959108
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00374 - p-value: 0.6381538746932982
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00375 - p-value: 0.84215080027575
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00376 - p-value: 0.5074571945682292
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00377 - p-value: 0.7276153402166061
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00379 - p-value: 0.2827369196151214
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Dropping feature due to high p-value: K00380 - p-value: 0.38702906608355925
7406.032008865821 Features added: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Logit Regression Results
==============================================================================
Dep. Variable: label No. Observations: 18983
Model: Logit Df Residuals: 18963
Method: MLE Df Model: 19
Date: Sat, 16 Dec 2023 Pseudo R-squ.: 0.05967
Time: 18:33:42 Log-Likelihood: -3683.0
converged: True LL-Null: -3916.7
Covariance Type: nonrobust LLR p-value: 3.739e-87
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
const -3.2022 0.047 -68.663 0.000 -3.294 -3.111
x1 -0.8605 0.153 -5.620 0.000 -1.161 -0.560
x2 0.5559 0.072 7.721 0.000 0.415 0.697
x3 -0.2353 0.062 -3.814 0.000 -0.356 -0.114
x4 0.2874 0.059 4.912 0.000 0.173 0.402
x5 0.1252 0.063 1.981 0.048 0.001 0.249
x6 -0.2208 0.102 -2.156 0.031 -0.422 -0.020
x7 0.1291 0.037 3.495 0.000 0.057 0.201
x8 -0.1284 0.062 -2.073 0.038 -0.250 -0.007
x9 -0.2124 0.077 -2.768 0.006 -0.363 -0.062
x10 0.1084 0.023 4.679 0.000 0.063 0.154
x11 0.1392 0.027 5.217 0.000 0.087 0.191
x12 0.0914 0.028 3.213 0.001 0.036 0.147
x13 0.3256 0.059 5.542 0.000 0.210 0.441
x14 0.0879 0.032 2.732 0.006 0.025 0.151
x15 0.1920 0.031 6.267 0.000 0.132 0.252
x16 -0.3174 0.070 -4.522 0.000 -0.455 -0.180
x17 -0.1813 0.074 -2.458 0.014 -0.326 -0.037
x18 0.0616 0.027 2.275 0.023 0.009 0.115
x19 -0.2189 0.095 -2.305 0.021 -0.405 -0.033
==============================================================================
Final selected features: ['K00282', 'count_of_nan_zero', 'K00312', 'K00115', 'K00289', 'K00073', 'K00064', 'K00059', 'K00133', 'K00128', 'K00176', 'K00359', 'K00244', 'K00215', 'K00026', 'K00249', 'K00053', 'K00321', 'K00382']
Tempo de execução: 360.5336821079254 segundos
# Run a logisticregression model with the selected features
start_timer()
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score, precision_score, roc_auc_score, balanced_accuracy_score
# Separate features (X) and target (y)
X = data[selected_features]
y = data["label"]
# RandomUnderSampler
#from imblearn.under_sampling import RandomUnderSampler
#sampler = RandomUnderSampler(sampling_strategy='majority', random_state=42)
# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
# Resample
#X_train, y_train = sampler.fit_resample(X_train, y_train)
model = LogisticRegression(class_weight = 'balanced', max_iter = 20000, n_jobs = 16)
model.fit(X_train, y_train)
# Evaluate the model
yhat = model.predict(X_test)
# Performance Metrics
tpr = recall_score(y_test, yhat)
fpr = 1 - tpr
accuracy = accuracy_score(y_test, yhat)
f1 = f1_score(y_test, yhat)
precision = precision_score(y_test, yhat)
roc_auc = roc_auc_score(y_test, yhat)
balanced_f1 = f1_score(y_test, yhat, average='weighted')
balanced_accuracy = balanced_accuracy_score(y_test, yhat)
ks = np.max(tpr - fpr)
print('Accuracy: %.2f%%' % (accuracy * 100))
print('F1 Score: %.3f' % f1)
print('Precision: %.3f' % precision)
print('ROC AUC: %.3f' % roc_auc)
print('Balanced F1 Score: %.3f' % balanced_f1)
print('Balanced Accuracy: %.3f' % balanced_accuracy)
print('KS: %.3f' % ks)
end_timer()
Accuracy: 55.07% F1 Score: 0.139 Precision: 0.077 ROC AUC: 0.616 Balanced F1 Score: 0.667 Balanced Accuracy: 0.616 KS: 0.380 Tempo de execução: 7.078934907913208 segundos
Accuracy: 55.07% F1 Score: 0.139 Precision: 0.077 ROC AUC: 0.616 Balanced F1 Score: 0.667 Balanced Accuracy: 0.616 KS: 0.380 Tempo de execução: 5.4849231243133545 segundos
### Foward Feature Selection
import statsmodels.api as sm
import pandas as pd
# Copy the dataset and fill NaN values with 0
data = df_droped_001.copy()
data = data.fillna(0)
# Separate features (X) and target (y)
X = data.drop("label", axis=1)
y = data["label"]
# Create an empty list to store selected features
selected_features = []
# Iterate through features in the order of df_IV_001
for feature in df_IV_001['Feature']:
# Add the current feature to the selected features list
selected_features.append(feature)
# Subset X with selected features
X_selected = X[selected_features]
scaler = StandardScaler()
X_selected = scaler.fit_transform(X_selected)
# Add constant column for intercept in logistic regression
X_selected = sm.add_constant(X_selected)
# Create and fit the logistic regression model
model = sm.Logit(y, X_selected)
result = model.fit_regularized(alpha=.5, trim_mode ='size' , method='l1', max_iter=100000000, disp=False)
# Check if the optimization converged
if result.mle_retvals['converged']:
# Print summary of the logistic regression model
print(result.aic,f"Features added: {selected_features}")
print(result.summary())
else:
print(f"Features added: {selected_features}")
print("Model did not converge\n")
# Print the final selected features
print("Final selected features:", selected_features)
# Install LightGBM Classifier
%pip install lightgbm
Collecting lightgbm Downloading lightgbm-4.2.0-py3-none-win_amd64.whl.metadata (19 kB) Requirement already satisfied: numpy in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from lightgbm) (1.26.2) Requirement already satisfied: scipy in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from lightgbm) (1.11.4) Downloading lightgbm-4.2.0-py3-none-win_amd64.whl (1.3 MB) ---------------------------------------- 0.0/1.3 MB ? eta -:--:-- ---------------------------------------- 0.0/1.3 MB ? eta -:--:-- - -------------------------------------- 0.0/1.3 MB 495.5 kB/s eta 0:00:03 --- ------------------------------------ 0.1/1.3 MB 939.4 kB/s eta 0:00:02 ----- ---------------------------------- 0.2/1.3 MB 1.1 MB/s eta 0:00:02 ------ --------------------------------- 0.2/1.3 MB 1.1 MB/s eta 0:00:01 --------- ------------------------------ 0.3/1.3 MB 1.1 MB/s eta 0:00:01 ---------- ----------------------------- 0.4/1.3 MB 1.2 MB/s eta 0:00:01 ------------- -------------------------- 0.4/1.3 MB 1.3 MB/s eta 0:00:01 --------------- ------------------------ 0.5/1.3 MB 1.3 MB/s eta 0:00:01 ---------------- ----------------------- 0.6/1.3 MB 1.2 MB/s eta 0:00:01 ----------------- ---------------------- 0.6/1.3 MB 1.2 MB/s eta 0:00:01 ------------------- -------------------- 0.7/1.3 MB 1.2 MB/s eta 0:00:01 ---------------------- ----------------- 0.7/1.3 MB 1.3 MB/s eta 0:00:01 ------------------------- -------------- 0.8/1.3 MB 1.3 MB/s eta 0:00:01 --------------------------- ------------ 0.9/1.3 MB 1.4 MB/s eta 0:00:01 ----------------------------- ---------- 1.0/1.3 MB 1.4 MB/s eta 0:00:01 -------------------------------- ------- 1.1/1.3 MB 1.4 MB/s eta 0:00:01 --------------------------------- ------ 1.1/1.3 MB 1.4 MB/s eta 0:00:01 ------------------------------------ --- 1.2/1.3 MB 1.4 MB/s eta 0:00:01 --------------------------------------- 1.3/1.3 MB 1.4 MB/s eta 0:00:01 ---------------------------------------- 1.3/1.3 MB 1.4 MB/s eta 0:00:00 Installing collected packages: lightgbm Successfully installed lightgbm-4.2.0 Note: you may need to restart the kernel to use updated packages.
Com o propósito em aprender (na prática), extrair o máximo de informações dos dados e aplicar o conhecimento adquirido durante o curso, optamos por avaliar diversos algoritmos para selecionar o mais adequado ao problema em questão. Essa abordagem nos possibilita escolher o modelo que melhor se adapta às características específicas do nosso conjunto de dados, visando obter resultados mais otimizados nas métricas. Abaixo estão os algoritmos que foram avaliados:
df_cleandata = pd.read_excel(f'{files_directory}/MBA001_cleandata.xlsx', index_col=False)
df_cleandata = df_cleandata.drop(['Unnamed: 0'], axis=1)
df_cleandata
--------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[3], line 1 ----> 1 df_cleandata = pd.read_excel(f'{files_directory}/MBA001_cleandata.xlsx', index_col=False) 2 df_cleandata = df_cleandata.drop(['Unnamed: 0'], axis=1) 3 df_cleandata NameError: name 'pd' is not defined
df_droped_001 = pd.read_csv(f'{files_directory}/df_droped_001.csv', index_col=False)
df_droped_001
| user_id | bank | K00001 | K00002 | K00003 | K00004 | K00005 | K00006 | K00007 | K00013 | ... | K00534 | K00535 | K00536 | K00537 | K00538 | K00540 | count_of_nan | count_of_nan_zero | count_of_zero | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 14.0 | 14.0 | 14.0 | 14.0 | 0.0 | 0.0 | NaN | 0.0 | ... | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 129 | 453 | 324 | 0 |
| 1 | 1 | 1 | 19.0 | 19.0 | 19.0 | 19.0 | 0.0 | 0.0 | 41.0 | 14.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 25 | 375 | 350 | 0 |
| 2 | 2 | 0 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | NaN | 5.0 | ... | 0.0 | 20.0 | 0.0 | 0.0 | 1.0 | 20.0 | 91 | 426 | 335 | 0 |
| 3 | 3 | 2 | 21.0 | 21.0 | 21.0 | 21.0 | 0.0 | 0.0 | 310.0 | NaN | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 180 | 472 | 292 | 0 |
| 4 | 4 | 3 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 257.0 | NaN | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 95 | 382 | 287 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18978 | 18978 | 2 | 13.0 | 13.0 | 13.0 | 13.0 | 0.0 | 0.0 | 95.0 | 14.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 106 | 368 | 262 | 0 |
| 18979 | 18979 | 0 | 8.0 | 8.0 | 8.0 | 8.0 | 0.0 | 0.0 | NaN | 5.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 526 | 26 | 0 |
| 18980 | 18980 | 2 | 16.0 | 16.0 | 16.0 | 16.0 | 0.0 | 0.0 | 217.0 | 7.0 | ... | 1090.0 | 1155.0 | 1.0 | 6.0 | 10.0 | 385.0 | 21 | 291 | 270 | 0 |
| 18981 | 18981 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 2.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 500 | 530 | 30 | 0 |
| 18982 | 18982 | 0 | 12.0 | 12.0 | 12.0 | 12.0 | 0.0 | 0.0 | NaN | 11.0 | ... | 32.0 | 42.0 | 1.0 | 2.0 | 3.0 | 14.0 | 30 | 351 | 321 | 0 |
18983 rows × 481 columns
df = df_droped_001.copy()
df.columns[479]
'count_of_zero'
import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from imblearn.over_sampling import RandomOverSampler, SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.combine import SMOTEENN, SMOTETomek
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc, make_scorer
from sklearn.impute import SimpleImputer
import os
# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.columns[1:479]
target = df['label']
# Define the hyperparameters for each model
hyperparameters = {
'Logistic Regression': {'C': [0.1, 1, 10]},
'k-Nearest Neighbors (k-NN)': {'n_neighbors': [3, 5, 7]},
'Support Vector Machines (SVM)': {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf']},
'Neural Networks': {'hidden_layer_sizes': [(50,), (100,), (50, 50)]},
'Naive Bayes': {},
'Decision Trees': {'max_depth': [None, 10, 20]},
'Random Forest': {'n_estimators': [50, 100, 200]},
'Gradient Boosting Machines (GBM)': {'n_estimators': [50, 100, 200]},
'XGBoost': {'n_estimators': [50, 100, 200]},
'LightGBM': {'n_estimators': [50, 100, 200]}
}
# Define the imbalanced data handling techniques
sampling_techniques = {
'Oversampling': RandomOverSampler(),
'Undersampling': RandomUnderSampler(),
'Hybrid Sampling (SMOTEENN)': SMOTEENN(),
'Hybrid Sampling (SMOTETomek)': SMOTETomek()
}
# Define the imputation techniques
imputation_techniques = {
'Zero Imputation': SimpleImputer(strategy='constant', fill_value=0),
'Mean Imputation': SimpleImputer(strategy='mean'),
'Median Imputation': SimpleImputer(strategy='median')
}
# Define a function to calculate KS and Gini
def calculate_ks_gini(y_true, y_prob):
fpr, tpr, thresholds = roc_curve(y_true, y_prob)
ks = max(tpr - fpr)
gini = 2 * auc(fpr, tpr) - 1
return ks, gini
# Create a list to store the results
results = []
# Define k-fold cross-validation
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)
# Iterate through each model
for model_name, model in [
('Logistic Regression', LogisticRegression()),
('k-Nearest Neighbors (k-NN)', KNeighborsClassifier()),
('Support Vector Machines (SVM)', SVC(probability=True)),
('Neural Networks', MLPClassifier()),
('Naive Bayes', GaussianNB()),
('Decision Trees', DecisionTreeClassifier()),
('Random Forest', RandomForestClassifier()),
('Gradient Boosting Machines (GBM)', GradientBoostingClassifier()),
('XGBoost', XGBClassifier()),
('LightGBM', LGBMClassifier())
]:
# Iterate through each imbalanced data handling technique
for sampling_name, sampling_technique in sampling_techniques.items():
# Iterate through each imputation technique
for imputation_name, imputation_technique in imputation_techniques.items():
# Create a new dataframe to store the results for this model, technique combination
df_result = pd.DataFrame(columns=['Algorithm', 'Metrics', 'Hyperparameters', 'Imputation Technique', 'Imbalance Class Technique', 'Model Unique Code'])
# Perform k-fold cross-validation
for fold, (train_index, test_index) in enumerate(k_fold.split(df[features], target)):
X_train, X_test = df[features].iloc[train_index], df[features].iloc[test_index]
y_train, y_test = target.iloc[train_index], target.iloc[test_index]
# Apply imputation technique on the train and test sets
X_train_imputed = imputation_technique.fit_transform(X_train)
X_test_imputed = imputation_technique.transform(X_test)
# Apply the imbalanced data handling technique on the train set
X_train_resampled, y_train_resampled = sampling_technique.fit_resample(X_train_imputed, y_train)
# Train the model on the resampled train set
model.fit(X_train_resampled, y_train_resampled)
# Make predictions on the test set
y_pred = model.predict(X_test_imputed)
y_prob = model.predict_proba(X_test_imputed)[:, 1]
# Calculate the evaluation metrics
metrics = {
'Accuracy': accuracy_score(y_test, y_pred),
'Precision': precision_score(y_test, y_pred),
'Recall': recall_score(y_test, y_pred),
'F1-score': f1_score(y_test, y_pred),
'AUC': roc_auc_score(y_test, y_prob)
}
# Calculate KS and Gini
ks, gini = calculate_ks_gini(y_test, y_prob)
metrics['KS'] = ks
metrics['Gini'] = gini
# Append the results to the dataframe
df_result = pd.concat([df_result, pd.DataFrame({
'Algorithm': model_name,
'Metrics': metrics,
'Hyperparameters': model.get_params(),
'Imputation Technique': imputation_name,
'Imbalance Class Technique': sampling_name,
'Model Unique Code': f"{model_name}_{sampling_name}_{imputation_name}_fold_{fold}"
})], ignore_index=True, axis=0)
# Append the results for this model, technique combination to the overall results list
results.append(df_result)
# Combine all the results dataframes into one final dataframe
df_results_final = pd.concat(results, ignore_index=True)
# Save each model in a separate file with name corresponding to model unique code
for model_unique_code in df_results_final['Model Unique Code'].unique():
df_model = df_results_final[df_results_final['Model Unique Code'] == model_unique_code]
df_model.to_csv(f"{model_unique_code}.csv", index=False)
# Display the final dataframe
print(df_results_final)
[LightGBM] [Info] Number of positive: 14423, number of negative: 14423
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.027280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61079
[LightGBM] [Info] Number of data points in the train set: 28846, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14350, number of negative: 14350
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.026250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61069
[LightGBM] [Info] Number of data points in the train set: 28700, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14373, number of negative: 14373
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61075
[LightGBM] [Info] Number of data points in the train set: 28746, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14383, number of negative: 14383
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031740 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61348
[LightGBM] [Info] Number of data points in the train set: 28766, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14403, number of negative: 14403
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.026522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61052
[LightGBM] [Info] Number of data points in the train set: 28806, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14423, number of negative: 14423
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.054988 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60775
[LightGBM] [Info] Number of data points in the train set: 28846, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14350, number of negative: 14350
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042106 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60808
[LightGBM] [Info] Number of data points in the train set: 28700, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14373, number of negative: 14373
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.052299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60542
[LightGBM] [Info] Number of data points in the train set: 28746, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14383, number of negative: 14383
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.051698 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61184
[LightGBM] [Info] Number of data points in the train set: 28766, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14403, number of negative: 14403
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.050930 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60577
[LightGBM] [Info] Number of data points in the train set: 28806, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14423, number of negative: 14423
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033512 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60857
[LightGBM] [Info] Number of data points in the train set: 28846, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14350, number of negative: 14350
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.030020 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60919
[LightGBM] [Info] Number of data points in the train set: 28700, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14373, number of negative: 14373
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60903
[LightGBM] [Info] Number of data points in the train set: 28746, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14383, number of negative: 14383
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031061 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 61157
[LightGBM] [Info] Number of data points in the train set: 28766, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14403, number of negative: 14403
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029650 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 60896
[LightGBM] [Info] Number of data points in the train set: 28806, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 763, number of negative: 763
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 25537
[LightGBM] [Info] Number of data points in the train set: 1526, number of used features: 360
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 836, number of negative: 836
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005639 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 27631
[LightGBM] [Info] Number of data points in the train set: 1672, number of used features: 370
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 813, number of negative: 813
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004824 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 26986
[LightGBM] [Info] Number of data points in the train set: 1626, number of used features: 366
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 804, number of negative: 804
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 26277
[LightGBM] [Info] Number of data points in the train set: 1608, number of used features: 370
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 784, number of negative: 784
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 25705
[LightGBM] [Info] Number of data points in the train set: 1568, number of used features: 370
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 763, number of negative: 763
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008856 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32150
[LightGBM] [Info] Number of data points in the train set: 1526, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 836, number of negative: 836
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009008 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 34012
[LightGBM] [Info] Number of data points in the train set: 1672, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 813, number of negative: 813
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008870 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 33675
[LightGBM] [Info] Number of data points in the train set: 1626, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 804, number of negative: 804
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007749 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32578
[LightGBM] [Info] Number of data points in the train set: 1608, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 784, number of negative: 784
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008739 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 32425
[LightGBM] [Info] Number of data points in the train set: 1568, number of used features: 477
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 763, number of negative: 763
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005896 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29267
[LightGBM] [Info] Number of data points in the train set: 1526, number of used features: 366
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 836, number of negative: 836
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30965
[LightGBM] [Info] Number of data points in the train set: 1672, number of used features: 378
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 813, number of negative: 813
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005812 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30175
[LightGBM] [Info] Number of data points in the train set: 1626, number of used features: 372
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 804, number of negative: 804
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006146 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 30115
[LightGBM] [Info] Number of data points in the train set: 1608, number of used features: 368
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 784, number of negative: 784
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004836 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 29968
[LightGBM] [Info] Number of data points in the train set: 1568, number of used features: 368
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 12129, number of negative: 9855
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036118 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 94789
[LightGBM] [Info] Number of data points in the train set: 21984, number of used features: 457
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.551719 -> initscore=0.207620
[LightGBM] [Info] Start training from score 0.207620
[LightGBM] [Info] Number of positive: 12069, number of negative: 9597
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031534 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 94279
[LightGBM] [Info] Number of data points in the train set: 21666, number of used features: 450
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.557048 -> initscore=0.229190
[LightGBM] [Info] Start training from score 0.229190
[LightGBM] [Info] Number of positive: 12393, number of negative: 9808
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029992 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 95223
[LightGBM] [Info] Number of data points in the train set: 22201, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.558218 -> initscore=0.233933
[LightGBM] [Info] Start training from score 0.233933
[LightGBM] [Info] Number of positive: 12157, number of negative: 9790
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029843 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 94458
[LightGBM] [Info] Number of data points in the train set: 21947, number of used features: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.553925 -> initscore=0.216544
[LightGBM] [Info] Start training from score 0.216544
[LightGBM] [Info] Number of positive: 12207, number of negative: 9770
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032741 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 94284
[LightGBM] [Info] Number of data points in the train set: 21977, number of used features: 453
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.555444 -> initscore=0.222693
[LightGBM] [Info] Start training from score 0.222693
[LightGBM] [Info] Number of positive: 12159, number of negative: 10161
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.058540 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120694
[LightGBM] [Info] Number of data points in the train set: 22320, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.544758 -> initscore=0.179513
[LightGBM] [Info] Start training from score 0.179513
[LightGBM] [Info] Number of positive: 12119, number of negative: 9928
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.060232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 121190
[LightGBM] [Info] Number of data points in the train set: 22047, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.549689 -> initscore=0.199415
[LightGBM] [Info] Start training from score 0.199415
[LightGBM] [Info] Number of positive: 12292, number of negative: 10028
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.060339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120588
[LightGBM] [Info] Number of data points in the train set: 22320, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.550717 -> initscore=0.203567
[LightGBM] [Info] Start training from score 0.203567
[LightGBM] [Info] Number of positive: 12077, number of negative: 10178
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.061489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120890
[LightGBM] [Info] Number of data points in the train set: 22255, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.542665 -> initscore=0.171074
[LightGBM] [Info] Start training from score 0.171074
[LightGBM] [Info] Number of positive: 12285, number of negative: 10113
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.051625 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120685
[LightGBM] [Info] Number of data points in the train set: 22398, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.548486 -> initscore=0.194557
[LightGBM] [Info] Start training from score 0.194557
[LightGBM] [Info] Number of positive: 12057, number of negative: 9953
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036696 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 95280
[LightGBM] [Info] Number of data points in the train set: 22010, number of used features: 457
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.547796 -> initscore=0.191771
[LightGBM] [Info] Start training from score 0.191771
[LightGBM] [Info] Number of positive: 12091, number of negative: 9699
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 93706
[LightGBM] [Info] Number of data points in the train set: 21790, number of used features: 448
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.554888 -> initscore=0.220439
[LightGBM] [Info] Start training from score 0.220439
[LightGBM] [Info] Number of positive: 12315, number of negative: 9885
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 95109
[LightGBM] [Info] Number of data points in the train set: 22200, number of used features: 459
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.554730 -> initscore=0.219800
[LightGBM] [Info] Start training from score 0.219800
[LightGBM] [Info] Number of positive: 12055, number of negative: 9804
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039955 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 94328
[LightGBM] [Info] Number of data points in the train set: 21859, number of used features: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.551489 -> initscore=0.206689
[LightGBM] [Info] Start training from score 0.206689
[LightGBM] [Info] Number of positive: 12308, number of negative: 9926
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 94001
[LightGBM] [Info] Number of data points in the train set: 22234, number of used features: 453
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.553567 -> initscore=0.215092
[LightGBM] [Info] Start training from score 0.215092
[LightGBM] [Info] Number of positive: 14204, number of negative: 14204
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 96056
[LightGBM] [Info] Number of data points in the train set: 28408, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14161, number of negative: 14161
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040138 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 95064
[LightGBM] [Info] Number of data points in the train set: 28322, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14166, number of negative: 14166
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 96704
[LightGBM] [Info] Number of data points in the train set: 28332, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14204, number of negative: 14204
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045504 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 95996
[LightGBM] [Info] Number of data points in the train set: 28408, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14218, number of negative: 14218
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038600 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 95900
[LightGBM] [Info] Number of data points in the train set: 28436, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14249, number of negative: 14249
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.065897 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120798
[LightGBM] [Info] Number of data points in the train set: 28498, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14198, number of negative: 14198
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120954
[LightGBM] [Info] Number of data points in the train set: 28396, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14212, number of negative: 14212
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.081952 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120937
[LightGBM] [Info] Number of data points in the train set: 28424, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14210, number of negative: 14210
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.076576 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120988
[LightGBM] [Info] Number of data points in the train set: 28420, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14237, number of negative: 14237
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.077103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 120426
[LightGBM] [Info] Number of data points in the train set: 28474, number of used features: 478
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14224, number of negative: 14224
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 96402
[LightGBM] [Info] Number of data points in the train set: 28448, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14178, number of negative: 14178
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 94950
[LightGBM] [Info] Number of data points in the train set: 28356, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14192, number of negative: 14192
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.049207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 96383
[LightGBM] [Info] Number of data points in the train set: 28384, number of used features: 463
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14174, number of negative: 14174
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044819 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 96036
[LightGBM] [Info] Number of data points in the train set: 28348, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of positive: 14217, number of negative: 14217
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046697 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 95550
[LightGBM] [Info] Number of data points in the train set: 28434, number of used features: 461
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
Algorithm Metrics Hyperparameters Imputation Technique \
0 Logistic Regression 0.494864 NaN Zero Imputation
1 Logistic Regression 0.086572 NaN Zero Imputation
2 Logistic Regression 0.742616 NaN Zero Imputation
3 Logistic Regression 0.155066 NaN Zero Imputation
4 Logistic Regression 0.623146 NaN Zero Imputation
... ... ... ... ...
14455 LightGBM NaN 0.0 Median Imputation
14456 LightGBM NaN 0.0 Median Imputation
14457 LightGBM NaN 1.0 Median Imputation
14458 LightGBM NaN 200000 Median Imputation
14459 LightGBM NaN 0 Median Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
... ...
14455 Hybrid Sampling (SMOTETomek)
14456 Hybrid Sampling (SMOTETomek)
14457 Hybrid Sampling (SMOTETomek)
14458 Hybrid Sampling (SMOTETomek)
14459 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Logistic Regression_Oversampling_Zero Imputati...
1 Logistic Regression_Oversampling_Zero Imputati...
2 Logistic Regression_Oversampling_Zero Imputati...
3 Logistic Regression_Oversampling_Zero Imputati...
4 Logistic Regression_Oversampling_Zero Imputati...
... ...
14455 LightGBM_Hybrid Sampling (SMOTETomek)_Median I...
14456 LightGBM_Hybrid Sampling (SMOTETomek)_Median I...
14457 LightGBM_Hybrid Sampling (SMOTETomek)_Median I...
14458 LightGBM_Hybrid Sampling (SMOTETomek)_Median I...
14459 LightGBM_Hybrid Sampling (SMOTETomek)_Median I...
[14460 rows x 6 columns]
display(results)
[ Algorithm Metrics Hyperparameters Imputation Technique \
0 Logistic Regression 0.494864 NaN Zero Imputation
1 Logistic Regression 0.086572 NaN Zero Imputation
2 Logistic Regression 0.742616 NaN Zero Imputation
3 Logistic Regression 0.155066 NaN Zero Imputation
4 Logistic Regression 0.623146 NaN Zero Imputation
5 Logistic Regression 0.230842 NaN Zero Imputation
6 Logistic Regression 0.246291 NaN Zero Imputation
7 Logistic Regression NaN 1.0 Zero Imputation
8 Logistic Regression NaN None Zero Imputation
9 Logistic Regression NaN False Zero Imputation
10 Logistic Regression NaN True Zero Imputation
11 Logistic Regression NaN 1 Zero Imputation
12 Logistic Regression NaN None Zero Imputation
13 Logistic Regression NaN 100 Zero Imputation
14 Logistic Regression NaN auto Zero Imputation
15 Logistic Regression NaN None Zero Imputation
16 Logistic Regression NaN l2 Zero Imputation
17 Logistic Regression NaN None Zero Imputation
18 Logistic Regression NaN lbfgs Zero Imputation
19 Logistic Regression NaN 0.0001 Zero Imputation
20 Logistic Regression NaN 0 Zero Imputation
21 Logistic Regression NaN False Zero Imputation
22 Logistic Regression 0.460890 NaN Zero Imputation
23 Logistic Regression 0.058603 NaN Zero Imputation
24 Logistic Regression 0.762195 NaN Zero Imputation
25 Logistic Regression 0.108838 NaN Zero Imputation
26 Logistic Regression 0.648632 NaN Zero Imputation
27 Logistic Regression 0.236828 NaN Zero Imputation
28 Logistic Regression 0.297263 NaN Zero Imputation
29 Logistic Regression NaN 1.0 Zero Imputation
30 Logistic Regression NaN None Zero Imputation
31 Logistic Regression NaN False Zero Imputation
32 Logistic Regression NaN True Zero Imputation
33 Logistic Regression NaN 1 Zero Imputation
34 Logistic Regression NaN None Zero Imputation
35 Logistic Regression NaN 100 Zero Imputation
36 Logistic Regression NaN auto Zero Imputation
37 Logistic Regression NaN None Zero Imputation
38 Logistic Regression NaN l2 Zero Imputation
39 Logistic Regression NaN None Zero Imputation
40 Logistic Regression NaN lbfgs Zero Imputation
41 Logistic Regression NaN 0.0001 Zero Imputation
42 Logistic Regression NaN 0 Zero Imputation
43 Logistic Regression NaN False Zero Imputation
44 Logistic Regression 0.454043 NaN Zero Imputation
45 Logistic Regression 0.063426 NaN Zero Imputation
46 Logistic Regression 0.732620 NaN Zero Imputation
47 Logistic Regression 0.116745 NaN Zero Imputation
48 Logistic Regression 0.641625 NaN Zero Imputation
49 Logistic Regression 0.207897 NaN Zero Imputation
50 Logistic Regression 0.283251 NaN Zero Imputation
51 Logistic Regression NaN 1.0 Zero Imputation
52 Logistic Regression NaN None Zero Imputation
53 Logistic Regression NaN False Zero Imputation
54 Logistic Regression NaN True Zero Imputation
55 Logistic Regression NaN 1 Zero Imputation
56 Logistic Regression NaN None Zero Imputation
57 Logistic Regression NaN 100 Zero Imputation
58 Logistic Regression NaN auto Zero Imputation
59 Logistic Regression NaN None Zero Imputation
60 Logistic Regression NaN l2 Zero Imputation
61 Logistic Regression NaN None Zero Imputation
62 Logistic Regression NaN lbfgs Zero Imputation
63 Logistic Regression NaN 0.0001 Zero Imputation
64 Logistic Regression NaN 0 Zero Imputation
65 Logistic Regression NaN False Zero Imputation
66 Logistic Regression 0.448894 NaN Zero Imputation
67 Logistic Regression 0.068699 NaN Zero Imputation
68 Logistic Regression 0.770408 NaN Zero Imputation
69 Logistic Regression 0.126149 NaN Zero Imputation
70 Logistic Regression 0.661925 NaN Zero Imputation
71 Logistic Regression 0.246003 NaN Zero Imputation
72 Logistic Regression 0.323851 NaN Zero Imputation
73 Logistic Regression NaN 1.0 Zero Imputation
74 Logistic Regression NaN None Zero Imputation
75 Logistic Regression NaN False Zero Imputation
76 Logistic Regression NaN True Zero Imputation
77 Logistic Regression NaN 1 Zero Imputation
78 Logistic Regression NaN None Zero Imputation
79 Logistic Regression NaN 100 Zero Imputation
80 Logistic Regression NaN auto Zero Imputation
81 Logistic Regression NaN None Zero Imputation
82 Logistic Regression NaN l2 Zero Imputation
83 Logistic Regression NaN None Zero Imputation
84 Logistic Regression NaN lbfgs Zero Imputation
85 Logistic Regression NaN 0.0001 Zero Imputation
86 Logistic Regression NaN 0 Zero Imputation
87 Logistic Regression NaN False Zero Imputation
88 Logistic Regression 0.468651 NaN Zero Imputation
89 Logistic Regression 0.075436 NaN Zero Imputation
90 Logistic Regression 0.740741 NaN Zero Imputation
91 Logistic Regression 0.136928 NaN Zero Imputation
92 Logistic Regression 0.658384 NaN Zero Imputation
93 Logistic Regression 0.251971 NaN Zero Imputation
94 Logistic Regression 0.316768 NaN Zero Imputation
95 Logistic Regression NaN 1.0 Zero Imputation
96 Logistic Regression NaN None Zero Imputation
97 Logistic Regression NaN False Zero Imputation
98 Logistic Regression NaN True Zero Imputation
99 Logistic Regression NaN 1 Zero Imputation
100 Logistic Regression NaN None Zero Imputation
101 Logistic Regression NaN 100 Zero Imputation
102 Logistic Regression NaN auto Zero Imputation
103 Logistic Regression NaN None Zero Imputation
104 Logistic Regression NaN l2 Zero Imputation
105 Logistic Regression NaN None Zero Imputation
106 Logistic Regression NaN lbfgs Zero Imputation
107 Logistic Regression NaN 0.0001 Zero Imputation
108 Logistic Regression NaN 0 Zero Imputation
109 Logistic Regression NaN False Zero Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
45 Oversampling
46 Oversampling
47 Oversampling
48 Oversampling
49 Oversampling
50 Oversampling
51 Oversampling
52 Oversampling
53 Oversampling
54 Oversampling
55 Oversampling
56 Oversampling
57 Oversampling
58 Oversampling
59 Oversampling
60 Oversampling
61 Oversampling
62 Oversampling
63 Oversampling
64 Oversampling
65 Oversampling
66 Oversampling
67 Oversampling
68 Oversampling
69 Oversampling
70 Oversampling
71 Oversampling
72 Oversampling
73 Oversampling
74 Oversampling
75 Oversampling
76 Oversampling
77 Oversampling
78 Oversampling
79 Oversampling
80 Oversampling
81 Oversampling
82 Oversampling
83 Oversampling
84 Oversampling
85 Oversampling
86 Oversampling
87 Oversampling
88 Oversampling
89 Oversampling
90 Oversampling
91 Oversampling
92 Oversampling
93 Oversampling
94 Oversampling
95 Oversampling
96 Oversampling
97 Oversampling
98 Oversampling
99 Oversampling
100 Oversampling
101 Oversampling
102 Oversampling
103 Oversampling
104 Oversampling
105 Oversampling
106 Oversampling
107 Oversampling
108 Oversampling
109 Oversampling
Model Unique Code
0 Logistic Regression_Oversampling_Zero Imputati...
1 Logistic Regression_Oversampling_Zero Imputati...
2 Logistic Regression_Oversampling_Zero Imputati...
3 Logistic Regression_Oversampling_Zero Imputati...
4 Logistic Regression_Oversampling_Zero Imputati...
5 Logistic Regression_Oversampling_Zero Imputati...
6 Logistic Regression_Oversampling_Zero Imputati...
7 Logistic Regression_Oversampling_Zero Imputati...
8 Logistic Regression_Oversampling_Zero Imputati...
9 Logistic Regression_Oversampling_Zero Imputati...
10 Logistic Regression_Oversampling_Zero Imputati...
11 Logistic Regression_Oversampling_Zero Imputati...
12 Logistic Regression_Oversampling_Zero Imputati...
13 Logistic Regression_Oversampling_Zero Imputati...
14 Logistic Regression_Oversampling_Zero Imputati...
15 Logistic Regression_Oversampling_Zero Imputati...
16 Logistic Regression_Oversampling_Zero Imputati...
17 Logistic Regression_Oversampling_Zero Imputati...
18 Logistic Regression_Oversampling_Zero Imputati...
19 Logistic Regression_Oversampling_Zero Imputati...
20 Logistic Regression_Oversampling_Zero Imputati...
21 Logistic Regression_Oversampling_Zero Imputati...
22 Logistic Regression_Oversampling_Zero Imputati...
23 Logistic Regression_Oversampling_Zero Imputati...
24 Logistic Regression_Oversampling_Zero Imputati...
25 Logistic Regression_Oversampling_Zero Imputati...
26 Logistic Regression_Oversampling_Zero Imputati...
27 Logistic Regression_Oversampling_Zero Imputati...
28 Logistic Regression_Oversampling_Zero Imputati...
29 Logistic Regression_Oversampling_Zero Imputati...
30 Logistic Regression_Oversampling_Zero Imputati...
31 Logistic Regression_Oversampling_Zero Imputati...
32 Logistic Regression_Oversampling_Zero Imputati...
33 Logistic Regression_Oversampling_Zero Imputati...
34 Logistic Regression_Oversampling_Zero Imputati...
35 Logistic Regression_Oversampling_Zero Imputati...
36 Logistic Regression_Oversampling_Zero Imputati...
37 Logistic Regression_Oversampling_Zero Imputati...
38 Logistic Regression_Oversampling_Zero Imputati...
39 Logistic Regression_Oversampling_Zero Imputati...
40 Logistic Regression_Oversampling_Zero Imputati...
41 Logistic Regression_Oversampling_Zero Imputati...
42 Logistic Regression_Oversampling_Zero Imputati...
43 Logistic Regression_Oversampling_Zero Imputati...
44 Logistic Regression_Oversampling_Zero Imputati...
45 Logistic Regression_Oversampling_Zero Imputati...
46 Logistic Regression_Oversampling_Zero Imputati...
47 Logistic Regression_Oversampling_Zero Imputati...
48 Logistic Regression_Oversampling_Zero Imputati...
49 Logistic Regression_Oversampling_Zero Imputati...
50 Logistic Regression_Oversampling_Zero Imputati...
51 Logistic Regression_Oversampling_Zero Imputati...
52 Logistic Regression_Oversampling_Zero Imputati...
53 Logistic Regression_Oversampling_Zero Imputati...
54 Logistic Regression_Oversampling_Zero Imputati...
55 Logistic Regression_Oversampling_Zero Imputati...
56 Logistic Regression_Oversampling_Zero Imputati...
57 Logistic Regression_Oversampling_Zero Imputati...
58 Logistic Regression_Oversampling_Zero Imputati...
59 Logistic Regression_Oversampling_Zero Imputati...
60 Logistic Regression_Oversampling_Zero Imputati...
61 Logistic Regression_Oversampling_Zero Imputati...
62 Logistic Regression_Oversampling_Zero Imputati...
63 Logistic Regression_Oversampling_Zero Imputati...
64 Logistic Regression_Oversampling_Zero Imputati...
65 Logistic Regression_Oversampling_Zero Imputati...
66 Logistic Regression_Oversampling_Zero Imputati...
67 Logistic Regression_Oversampling_Zero Imputati...
68 Logistic Regression_Oversampling_Zero Imputati...
69 Logistic Regression_Oversampling_Zero Imputati...
70 Logistic Regression_Oversampling_Zero Imputati...
71 Logistic Regression_Oversampling_Zero Imputati...
72 Logistic Regression_Oversampling_Zero Imputati...
73 Logistic Regression_Oversampling_Zero Imputati...
74 Logistic Regression_Oversampling_Zero Imputati...
75 Logistic Regression_Oversampling_Zero Imputati...
76 Logistic Regression_Oversampling_Zero Imputati...
77 Logistic Regression_Oversampling_Zero Imputati...
78 Logistic Regression_Oversampling_Zero Imputati...
79 Logistic Regression_Oversampling_Zero Imputati...
80 Logistic Regression_Oversampling_Zero Imputati...
81 Logistic Regression_Oversampling_Zero Imputati...
82 Logistic Regression_Oversampling_Zero Imputati...
83 Logistic Regression_Oversampling_Zero Imputati...
84 Logistic Regression_Oversampling_Zero Imputati...
85 Logistic Regression_Oversampling_Zero Imputati...
86 Logistic Regression_Oversampling_Zero Imputati...
87 Logistic Regression_Oversampling_Zero Imputati...
88 Logistic Regression_Oversampling_Zero Imputati...
89 Logistic Regression_Oversampling_Zero Imputati...
90 Logistic Regression_Oversampling_Zero Imputati...
91 Logistic Regression_Oversampling_Zero Imputati...
92 Logistic Regression_Oversampling_Zero Imputati...
93 Logistic Regression_Oversampling_Zero Imputati...
94 Logistic Regression_Oversampling_Zero Imputati...
95 Logistic Regression_Oversampling_Zero Imputati...
96 Logistic Regression_Oversampling_Zero Imputati...
97 Logistic Regression_Oversampling_Zero Imputati...
98 Logistic Regression_Oversampling_Zero Imputati...
99 Logistic Regression_Oversampling_Zero Imputati...
100 Logistic Regression_Oversampling_Zero Imputati...
101 Logistic Regression_Oversampling_Zero Imputati...
102 Logistic Regression_Oversampling_Zero Imputati...
103 Logistic Regression_Oversampling_Zero Imputati...
104 Logistic Regression_Oversampling_Zero Imputati...
105 Logistic Regression_Oversampling_Zero Imputati...
106 Logistic Regression_Oversampling_Zero Imputati...
107 Logistic Regression_Oversampling_Zero Imputati...
108 Logistic Regression_Oversampling_Zero Imputati...
109 Logistic Regression_Oversampling_Zero Imputati... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Logistic Regression 0.436134 NaN Mean Imputation
1 Logistic Regression 0.080617 NaN Mean Imputation
2 Logistic Regression 0.772152 NaN Mean Imputation
3 Logistic Regression 0.145991 NaN Mean Imputation
4 Logistic Regression 0.635176 NaN Mean Imputation
5 Logistic Regression 0.239617 NaN Mean Imputation
6 Logistic Regression 0.270352 NaN Mean Imputation
7 Logistic Regression NaN 1.0 Mean Imputation
8 Logistic Regression NaN None Mean Imputation
9 Logistic Regression NaN False Mean Imputation
10 Logistic Regression NaN True Mean Imputation
11 Logistic Regression NaN 1 Mean Imputation
12 Logistic Regression NaN None Mean Imputation
13 Logistic Regression NaN 100 Mean Imputation
14 Logistic Regression NaN auto Mean Imputation
15 Logistic Regression NaN None Mean Imputation
16 Logistic Regression NaN l2 Mean Imputation
17 Logistic Regression NaN None Mean Imputation
18 Logistic Regression NaN lbfgs Mean Imputation
19 Logistic Regression NaN 0.0001 Mean Imputation
20 Logistic Regression NaN 0 Mean Imputation
21 Logistic Regression NaN False Mean Imputation
22 Logistic Regression 0.459310 NaN Mean Imputation
23 Logistic Regression 0.057196 NaN Mean Imputation
24 Logistic Regression 0.743902 NaN Mean Imputation
25 Logistic Regression 0.106226 NaN Mean Imputation
26 Logistic Regression 0.623355 NaN Mean Imputation
27 Logistic Regression 0.216734 NaN Mean Imputation
28 Logistic Regression 0.246710 NaN Mean Imputation
29 Logistic Regression NaN 1.0 Mean Imputation
30 Logistic Regression NaN None Mean Imputation
31 Logistic Regression NaN False Mean Imputation
32 Logistic Regression NaN True Mean Imputation
33 Logistic Regression NaN 1 Mean Imputation
34 Logistic Regression NaN None Mean Imputation
35 Logistic Regression NaN 100 Mean Imputation
36 Logistic Regression NaN auto Mean Imputation
37 Logistic Regression NaN None Mean Imputation
38 Logistic Regression NaN l2 Mean Imputation
39 Logistic Regression NaN None Mean Imputation
40 Logistic Regression NaN lbfgs Mean Imputation
41 Logistic Regression NaN 0.0001 Mean Imputation
42 Logistic Regression NaN 0 Mean Imputation
43 Logistic Regression NaN False Mean Imputation
44 Logistic Regression 0.451936 NaN Mean Imputation
45 Logistic Regression 0.064798 NaN Mean Imputation
46 Logistic Regression 0.754011 NaN Mean Imputation
47 Logistic Regression 0.119340 NaN Mean Imputation
48 Logistic Regression 0.637962 NaN Mean Imputation
49 Logistic Regression 0.225584 NaN Mean Imputation
50 Logistic Regression 0.275924 NaN Mean Imputation
51 Logistic Regression NaN 1.0 Mean Imputation
52 Logistic Regression NaN None Mean Imputation
53 Logistic Regression NaN False Mean Imputation
54 Logistic Regression NaN True Mean Imputation
55 Logistic Regression NaN 1 Mean Imputation
56 Logistic Regression NaN None Mean Imputation
57 Logistic Regression NaN 100 Mean Imputation
58 Logistic Regression NaN auto Mean Imputation
59 Logistic Regression NaN None Mean Imputation
60 Logistic Regression NaN l2 Mean Imputation
61 Logistic Regression NaN None Mean Imputation
62 Logistic Regression NaN lbfgs Mean Imputation
63 Logistic Regression NaN 0.0001 Mean Imputation
64 Logistic Regression NaN 0 Mean Imputation
65 Logistic Regression NaN False Mean Imputation
66 Logistic Regression 0.482350 NaN Mean Imputation
67 Logistic Regression 0.071255 NaN Mean Imputation
68 Logistic Regression 0.750000 NaN Mean Imputation
69 Logistic Regression 0.130146 NaN Mean Imputation
70 Logistic Regression 0.644099 NaN Mean Imputation
71 Logistic Regression 0.252166 NaN Mean Imputation
72 Logistic Regression 0.288199 NaN Mean Imputation
73 Logistic Regression NaN 1.0 Mean Imputation
74 Logistic Regression NaN None Mean Imputation
75 Logistic Regression NaN False Mean Imputation
76 Logistic Regression NaN True Mean Imputation
77 Logistic Regression NaN 1 Mean Imputation
78 Logistic Regression NaN None Mean Imputation
79 Logistic Regression NaN 100 Mean Imputation
80 Logistic Regression NaN auto Mean Imputation
81 Logistic Regression NaN None Mean Imputation
82 Logistic Regression NaN l2 Mean Imputation
83 Logistic Regression NaN None Mean Imputation
84 Logistic Regression NaN lbfgs Mean Imputation
85 Logistic Regression NaN 0.0001 Mean Imputation
86 Logistic Regression NaN 0 Mean Imputation
87 Logistic Regression NaN False Mean Imputation
88 Logistic Regression 0.465490 NaN Mean Imputation
89 Logistic Regression 0.075012 NaN Mean Imputation
90 Logistic Regression 0.740741 NaN Mean Imputation
91 Logistic Regression 0.136228 NaN Mean Imputation
92 Logistic Regression 0.648727 NaN Mean Imputation
93 Logistic Regression 0.246343 NaN Mean Imputation
94 Logistic Regression 0.297454 NaN Mean Imputation
95 Logistic Regression NaN 1.0 Mean Imputation
96 Logistic Regression NaN None Mean Imputation
97 Logistic Regression NaN False Mean Imputation
98 Logistic Regression NaN True Mean Imputation
99 Logistic Regression NaN 1 Mean Imputation
100 Logistic Regression NaN None Mean Imputation
101 Logistic Regression NaN 100 Mean Imputation
102 Logistic Regression NaN auto Mean Imputation
103 Logistic Regression NaN None Mean Imputation
104 Logistic Regression NaN l2 Mean Imputation
105 Logistic Regression NaN None Mean Imputation
106 Logistic Regression NaN lbfgs Mean Imputation
107 Logistic Regression NaN 0.0001 Mean Imputation
108 Logistic Regression NaN 0 Mean Imputation
109 Logistic Regression NaN False Mean Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
45 Oversampling
46 Oversampling
47 Oversampling
48 Oversampling
49 Oversampling
50 Oversampling
51 Oversampling
52 Oversampling
53 Oversampling
54 Oversampling
55 Oversampling
56 Oversampling
57 Oversampling
58 Oversampling
59 Oversampling
60 Oversampling
61 Oversampling
62 Oversampling
63 Oversampling
64 Oversampling
65 Oversampling
66 Oversampling
67 Oversampling
68 Oversampling
69 Oversampling
70 Oversampling
71 Oversampling
72 Oversampling
73 Oversampling
74 Oversampling
75 Oversampling
76 Oversampling
77 Oversampling
78 Oversampling
79 Oversampling
80 Oversampling
81 Oversampling
82 Oversampling
83 Oversampling
84 Oversampling
85 Oversampling
86 Oversampling
87 Oversampling
88 Oversampling
89 Oversampling
90 Oversampling
91 Oversampling
92 Oversampling
93 Oversampling
94 Oversampling
95 Oversampling
96 Oversampling
97 Oversampling
98 Oversampling
99 Oversampling
100 Oversampling
101 Oversampling
102 Oversampling
103 Oversampling
104 Oversampling
105 Oversampling
106 Oversampling
107 Oversampling
108 Oversampling
109 Oversampling
Model Unique Code
0 Logistic Regression_Oversampling_Mean Imputati...
1 Logistic Regression_Oversampling_Mean Imputati...
2 Logistic Regression_Oversampling_Mean Imputati...
3 Logistic Regression_Oversampling_Mean Imputati...
4 Logistic Regression_Oversampling_Mean Imputati...
5 Logistic Regression_Oversampling_Mean Imputati...
6 Logistic Regression_Oversampling_Mean Imputati...
7 Logistic Regression_Oversampling_Mean Imputati...
8 Logistic Regression_Oversampling_Mean Imputati...
9 Logistic Regression_Oversampling_Mean Imputati...
10 Logistic Regression_Oversampling_Mean Imputati...
11 Logistic Regression_Oversampling_Mean Imputati...
12 Logistic Regression_Oversampling_Mean Imputati...
13 Logistic Regression_Oversampling_Mean Imputati...
14 Logistic Regression_Oversampling_Mean Imputati...
15 Logistic Regression_Oversampling_Mean Imputati...
16 Logistic Regression_Oversampling_Mean Imputati...
17 Logistic Regression_Oversampling_Mean Imputati...
18 Logistic Regression_Oversampling_Mean Imputati...
19 Logistic Regression_Oversampling_Mean Imputati...
20 Logistic Regression_Oversampling_Mean Imputati...
21 Logistic Regression_Oversampling_Mean Imputati...
22 Logistic Regression_Oversampling_Mean Imputati...
23 Logistic Regression_Oversampling_Mean Imputati...
24 Logistic Regression_Oversampling_Mean Imputati...
25 Logistic Regression_Oversampling_Mean Imputati...
26 Logistic Regression_Oversampling_Mean Imputati...
27 Logistic Regression_Oversampling_Mean Imputati...
28 Logistic Regression_Oversampling_Mean Imputati...
29 Logistic Regression_Oversampling_Mean Imputati...
30 Logistic Regression_Oversampling_Mean Imputati...
31 Logistic Regression_Oversampling_Mean Imputati...
32 Logistic Regression_Oversampling_Mean Imputati...
33 Logistic Regression_Oversampling_Mean Imputati...
34 Logistic Regression_Oversampling_Mean Imputati...
35 Logistic Regression_Oversampling_Mean Imputati...
36 Logistic Regression_Oversampling_Mean Imputati...
37 Logistic Regression_Oversampling_Mean Imputati...
38 Logistic Regression_Oversampling_Mean Imputati...
39 Logistic Regression_Oversampling_Mean Imputati...
40 Logistic Regression_Oversampling_Mean Imputati...
41 Logistic Regression_Oversampling_Mean Imputati...
42 Logistic Regression_Oversampling_Mean Imputati...
43 Logistic Regression_Oversampling_Mean Imputati...
44 Logistic Regression_Oversampling_Mean Imputati...
45 Logistic Regression_Oversampling_Mean Imputati...
46 Logistic Regression_Oversampling_Mean Imputati...
47 Logistic Regression_Oversampling_Mean Imputati...
48 Logistic Regression_Oversampling_Mean Imputati...
49 Logistic Regression_Oversampling_Mean Imputati...
50 Logistic Regression_Oversampling_Mean Imputati...
51 Logistic Regression_Oversampling_Mean Imputati...
52 Logistic Regression_Oversampling_Mean Imputati...
53 Logistic Regression_Oversampling_Mean Imputati...
54 Logistic Regression_Oversampling_Mean Imputati...
55 Logistic Regression_Oversampling_Mean Imputati...
56 Logistic Regression_Oversampling_Mean Imputati...
57 Logistic Regression_Oversampling_Mean Imputati...
58 Logistic Regression_Oversampling_Mean Imputati...
59 Logistic Regression_Oversampling_Mean Imputati...
60 Logistic Regression_Oversampling_Mean Imputati...
61 Logistic Regression_Oversampling_Mean Imputati...
62 Logistic Regression_Oversampling_Mean Imputati...
63 Logistic Regression_Oversampling_Mean Imputati...
64 Logistic Regression_Oversampling_Mean Imputati...
65 Logistic Regression_Oversampling_Mean Imputati...
66 Logistic Regression_Oversampling_Mean Imputati...
67 Logistic Regression_Oversampling_Mean Imputati...
68 Logistic Regression_Oversampling_Mean Imputati...
69 Logistic Regression_Oversampling_Mean Imputati...
70 Logistic Regression_Oversampling_Mean Imputati...
71 Logistic Regression_Oversampling_Mean Imputati...
72 Logistic Regression_Oversampling_Mean Imputati...
73 Logistic Regression_Oversampling_Mean Imputati...
74 Logistic Regression_Oversampling_Mean Imputati...
75 Logistic Regression_Oversampling_Mean Imputati...
76 Logistic Regression_Oversampling_Mean Imputati...
77 Logistic Regression_Oversampling_Mean Imputati...
78 Logistic Regression_Oversampling_Mean Imputati...
79 Logistic Regression_Oversampling_Mean Imputati...
80 Logistic Regression_Oversampling_Mean Imputati...
81 Logistic Regression_Oversampling_Mean Imputati...
82 Logistic Regression_Oversampling_Mean Imputati...
83 Logistic Regression_Oversampling_Mean Imputati...
84 Logistic Regression_Oversampling_Mean Imputati...
85 Logistic Regression_Oversampling_Mean Imputati...
86 Logistic Regression_Oversampling_Mean Imputati...
87 Logistic Regression_Oversampling_Mean Imputati...
88 Logistic Regression_Oversampling_Mean Imputati...
89 Logistic Regression_Oversampling_Mean Imputati...
90 Logistic Regression_Oversampling_Mean Imputati...
91 Logistic Regression_Oversampling_Mean Imputati...
92 Logistic Regression_Oversampling_Mean Imputati...
93 Logistic Regression_Oversampling_Mean Imputati...
94 Logistic Regression_Oversampling_Mean Imputati...
95 Logistic Regression_Oversampling_Mean Imputati...
96 Logistic Regression_Oversampling_Mean Imputati...
97 Logistic Regression_Oversampling_Mean Imputati...
98 Logistic Regression_Oversampling_Mean Imputati...
99 Logistic Regression_Oversampling_Mean Imputati...
100 Logistic Regression_Oversampling_Mean Imputati...
101 Logistic Regression_Oversampling_Mean Imputati...
102 Logistic Regression_Oversampling_Mean Imputati...
103 Logistic Regression_Oversampling_Mean Imputati...
104 Logistic Regression_Oversampling_Mean Imputati...
105 Logistic Regression_Oversampling_Mean Imputati...
106 Logistic Regression_Oversampling_Mean Imputati...
107 Logistic Regression_Oversampling_Mean Imputati...
108 Logistic Regression_Oversampling_Mean Imputati...
109 Logistic Regression_Oversampling_Mean Imputati... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Logistic Regression 0.495654 NaN Median Imputation
1 Logistic Regression 0.085884 NaN Median Imputation
2 Logistic Regression 0.734177 NaN Median Imputation
3 Logistic Regression 0.153778 NaN Median Imputation
4 Logistic Regression 0.617012 NaN Median Imputation
5 Logistic Regression 0.231823 NaN Median Imputation
6 Logistic Regression 0.234024 NaN Median Imputation
7 Logistic Regression NaN 1.0 Median Imputation
8 Logistic Regression NaN None Median Imputation
9 Logistic Regression NaN False Median Imputation
10 Logistic Regression NaN True Median Imputation
11 Logistic Regression NaN 1 Median Imputation
12 Logistic Regression NaN None Median Imputation
13 Logistic Regression NaN 100 Median Imputation
14 Logistic Regression NaN auto Median Imputation
15 Logistic Regression NaN None Median Imputation
16 Logistic Regression NaN l2 Median Imputation
17 Logistic Regression NaN None Median Imputation
18 Logistic Regression NaN lbfgs Median Imputation
19 Logistic Regression NaN 0.0001 Median Imputation
20 Logistic Regression NaN 0 Median Imputation
21 Logistic Regression NaN False Median Imputation
22 Logistic Regression 0.555439 NaN Median Imputation
23 Logistic Regression 0.060554 NaN Median Imputation
24 Logistic Regression 0.640244 NaN Median Imputation
25 Logistic Regression 0.110643 NaN Median Imputation
26 Logistic Regression 0.640560 NaN Median Imputation
27 Logistic Regression 0.239804 NaN Median Imputation
28 Logistic Regression 0.281121 NaN Median Imputation
29 Logistic Regression NaN 1.0 Median Imputation
30 Logistic Regression NaN None Median Imputation
31 Logistic Regression NaN False Median Imputation
32 Logistic Regression NaN True Median Imputation
33 Logistic Regression NaN 1 Median Imputation
34 Logistic Regression NaN None Median Imputation
35 Logistic Regression NaN 100 Median Imputation
36 Logistic Regression NaN auto Median Imputation
37 Logistic Regression NaN None Median Imputation
38 Logistic Regression NaN l2 Median Imputation
39 Logistic Regression NaN None Median Imputation
40 Logistic Regression NaN lbfgs Median Imputation
41 Logistic Regression NaN 0.0001 Median Imputation
42 Logistic Regression NaN 0 Median Imputation
43 Logistic Regression NaN False Median Imputation
44 Logistic Regression 0.527258 NaN Median Imputation
45 Logistic Regression 0.065875 NaN Median Imputation
46 Logistic Regression 0.652406 NaN Median Imputation
47 Logistic Regression 0.119667 NaN Median Imputation
48 Logistic Regression 0.634617 NaN Median Imputation
49 Logistic Regression 0.217145 NaN Median Imputation
50 Logistic Regression 0.269234 NaN Median Imputation
51 Logistic Regression NaN 1.0 Median Imputation
52 Logistic Regression NaN None Median Imputation
53 Logistic Regression NaN False Median Imputation
54 Logistic Regression NaN True Median Imputation
55 Logistic Regression NaN 1 Median Imputation
56 Logistic Regression NaN None Median Imputation
57 Logistic Regression NaN 100 Median Imputation
58 Logistic Regression NaN auto Median Imputation
59 Logistic Regression NaN None Median Imputation
60 Logistic Regression NaN l2 Median Imputation
61 Logistic Regression NaN None Median Imputation
62 Logistic Regression NaN lbfgs Median Imputation
63 Logistic Regression NaN 0.0001 Median Imputation
64 Logistic Regression NaN 0 Median Imputation
65 Logistic Regression NaN False Median Imputation
66 Logistic Regression 0.544257 NaN Median Imputation
67 Logistic Regression 0.074834 NaN Median Imputation
68 Logistic Regression 0.688776 NaN Median Imputation
69 Logistic Regression 0.135000 NaN Median Imputation
70 Logistic Regression 0.655620 NaN Median Imputation
71 Logistic Regression 0.251633 NaN Median Imputation
72 Logistic Regression 0.311240 NaN Median Imputation
73 Logistic Regression NaN 1.0 Median Imputation
74 Logistic Regression NaN None Median Imputation
75 Logistic Regression NaN False Median Imputation
76 Logistic Regression NaN True Median Imputation
77 Logistic Regression NaN 1 Median Imputation
78 Logistic Regression NaN None Median Imputation
79 Logistic Regression NaN 100 Median Imputation
80 Logistic Regression NaN auto Median Imputation
81 Logistic Regression NaN None Median Imputation
82 Logistic Regression NaN l2 Median Imputation
83 Logistic Regression NaN None Median Imputation
84 Logistic Regression NaN lbfgs Median Imputation
85 Logistic Regression NaN 0.0001 Median Imputation
86 Logistic Regression NaN 0 Median Imputation
87 Logistic Regression NaN False Median Imputation
88 Logistic Regression 0.562961 NaN Median Imputation
89 Logistic Regression 0.081739 NaN Median Imputation
90 Logistic Regression 0.652778 NaN Median Imputation
91 Logistic Regression 0.145286 NaN Median Imputation
92 Logistic Regression 0.642832 NaN Median Imputation
93 Logistic Regression 0.239898 NaN Median Imputation
94 Logistic Regression 0.285665 NaN Median Imputation
95 Logistic Regression NaN 1.0 Median Imputation
96 Logistic Regression NaN None Median Imputation
97 Logistic Regression NaN False Median Imputation
98 Logistic Regression NaN True Median Imputation
99 Logistic Regression NaN 1 Median Imputation
100 Logistic Regression NaN None Median Imputation
101 Logistic Regression NaN 100 Median Imputation
102 Logistic Regression NaN auto Median Imputation
103 Logistic Regression NaN None Median Imputation
104 Logistic Regression NaN l2 Median Imputation
105 Logistic Regression NaN None Median Imputation
106 Logistic Regression NaN lbfgs Median Imputation
107 Logistic Regression NaN 0.0001 Median Imputation
108 Logistic Regression NaN 0 Median Imputation
109 Logistic Regression NaN False Median Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
45 Oversampling
46 Oversampling
47 Oversampling
48 Oversampling
49 Oversampling
50 Oversampling
51 Oversampling
52 Oversampling
53 Oversampling
54 Oversampling
55 Oversampling
56 Oversampling
57 Oversampling
58 Oversampling
59 Oversampling
60 Oversampling
61 Oversampling
62 Oversampling
63 Oversampling
64 Oversampling
65 Oversampling
66 Oversampling
67 Oversampling
68 Oversampling
69 Oversampling
70 Oversampling
71 Oversampling
72 Oversampling
73 Oversampling
74 Oversampling
75 Oversampling
76 Oversampling
77 Oversampling
78 Oversampling
79 Oversampling
80 Oversampling
81 Oversampling
82 Oversampling
83 Oversampling
84 Oversampling
85 Oversampling
86 Oversampling
87 Oversampling
88 Oversampling
89 Oversampling
90 Oversampling
91 Oversampling
92 Oversampling
93 Oversampling
94 Oversampling
95 Oversampling
96 Oversampling
97 Oversampling
98 Oversampling
99 Oversampling
100 Oversampling
101 Oversampling
102 Oversampling
103 Oversampling
104 Oversampling
105 Oversampling
106 Oversampling
107 Oversampling
108 Oversampling
109 Oversampling
Model Unique Code
0 Logistic Regression_Oversampling_Median Imputa...
1 Logistic Regression_Oversampling_Median Imputa...
2 Logistic Regression_Oversampling_Median Imputa...
3 Logistic Regression_Oversampling_Median Imputa...
4 Logistic Regression_Oversampling_Median Imputa...
5 Logistic Regression_Oversampling_Median Imputa...
6 Logistic Regression_Oversampling_Median Imputa...
7 Logistic Regression_Oversampling_Median Imputa...
8 Logistic Regression_Oversampling_Median Imputa...
9 Logistic Regression_Oversampling_Median Imputa...
10 Logistic Regression_Oversampling_Median Imputa...
11 Logistic Regression_Oversampling_Median Imputa...
12 Logistic Regression_Oversampling_Median Imputa...
13 Logistic Regression_Oversampling_Median Imputa...
14 Logistic Regression_Oversampling_Median Imputa...
15 Logistic Regression_Oversampling_Median Imputa...
16 Logistic Regression_Oversampling_Median Imputa...
17 Logistic Regression_Oversampling_Median Imputa...
18 Logistic Regression_Oversampling_Median Imputa...
19 Logistic Regression_Oversampling_Median Imputa...
20 Logistic Regression_Oversampling_Median Imputa...
21 Logistic Regression_Oversampling_Median Imputa...
22 Logistic Regression_Oversampling_Median Imputa...
23 Logistic Regression_Oversampling_Median Imputa...
24 Logistic Regression_Oversampling_Median Imputa...
25 Logistic Regression_Oversampling_Median Imputa...
26 Logistic Regression_Oversampling_Median Imputa...
27 Logistic Regression_Oversampling_Median Imputa...
28 Logistic Regression_Oversampling_Median Imputa...
29 Logistic Regression_Oversampling_Median Imputa...
30 Logistic Regression_Oversampling_Median Imputa...
31 Logistic Regression_Oversampling_Median Imputa...
32 Logistic Regression_Oversampling_Median Imputa...
33 Logistic Regression_Oversampling_Median Imputa...
34 Logistic Regression_Oversampling_Median Imputa...
35 Logistic Regression_Oversampling_Median Imputa...
36 Logistic Regression_Oversampling_Median Imputa...
37 Logistic Regression_Oversampling_Median Imputa...
38 Logistic Regression_Oversampling_Median Imputa...
39 Logistic Regression_Oversampling_Median Imputa...
40 Logistic Regression_Oversampling_Median Imputa...
41 Logistic Regression_Oversampling_Median Imputa...
42 Logistic Regression_Oversampling_Median Imputa...
43 Logistic Regression_Oversampling_Median Imputa...
44 Logistic Regression_Oversampling_Median Imputa...
45 Logistic Regression_Oversampling_Median Imputa...
46 Logistic Regression_Oversampling_Median Imputa...
47 Logistic Regression_Oversampling_Median Imputa...
48 Logistic Regression_Oversampling_Median Imputa...
49 Logistic Regression_Oversampling_Median Imputa...
50 Logistic Regression_Oversampling_Median Imputa...
51 Logistic Regression_Oversampling_Median Imputa...
52 Logistic Regression_Oversampling_Median Imputa...
53 Logistic Regression_Oversampling_Median Imputa...
54 Logistic Regression_Oversampling_Median Imputa...
55 Logistic Regression_Oversampling_Median Imputa...
56 Logistic Regression_Oversampling_Median Imputa...
57 Logistic Regression_Oversampling_Median Imputa...
58 Logistic Regression_Oversampling_Median Imputa...
59 Logistic Regression_Oversampling_Median Imputa...
60 Logistic Regression_Oversampling_Median Imputa...
61 Logistic Regression_Oversampling_Median Imputa...
62 Logistic Regression_Oversampling_Median Imputa...
63 Logistic Regression_Oversampling_Median Imputa...
64 Logistic Regression_Oversampling_Median Imputa...
65 Logistic Regression_Oversampling_Median Imputa...
66 Logistic Regression_Oversampling_Median Imputa...
67 Logistic Regression_Oversampling_Median Imputa...
68 Logistic Regression_Oversampling_Median Imputa...
69 Logistic Regression_Oversampling_Median Imputa...
70 Logistic Regression_Oversampling_Median Imputa...
71 Logistic Regression_Oversampling_Median Imputa...
72 Logistic Regression_Oversampling_Median Imputa...
73 Logistic Regression_Oversampling_Median Imputa...
74 Logistic Regression_Oversampling_Median Imputa...
75 Logistic Regression_Oversampling_Median Imputa...
76 Logistic Regression_Oversampling_Median Imputa...
77 Logistic Regression_Oversampling_Median Imputa...
78 Logistic Regression_Oversampling_Median Imputa...
79 Logistic Regression_Oversampling_Median Imputa...
80 Logistic Regression_Oversampling_Median Imputa...
81 Logistic Regression_Oversampling_Median Imputa...
82 Logistic Regression_Oversampling_Median Imputa...
83 Logistic Regression_Oversampling_Median Imputa...
84 Logistic Regression_Oversampling_Median Imputa...
85 Logistic Regression_Oversampling_Median Imputa...
86 Logistic Regression_Oversampling_Median Imputa...
87 Logistic Regression_Oversampling_Median Imputa...
88 Logistic Regression_Oversampling_Median Imputa...
89 Logistic Regression_Oversampling_Median Imputa...
90 Logistic Regression_Oversampling_Median Imputa...
91 Logistic Regression_Oversampling_Median Imputa...
92 Logistic Regression_Oversampling_Median Imputa...
93 Logistic Regression_Oversampling_Median Imputa...
94 Logistic Regression_Oversampling_Median Imputa...
95 Logistic Regression_Oversampling_Median Imputa...
96 Logistic Regression_Oversampling_Median Imputa...
97 Logistic Regression_Oversampling_Median Imputa...
98 Logistic Regression_Oversampling_Median Imputa...
99 Logistic Regression_Oversampling_Median Imputa...
100 Logistic Regression_Oversampling_Median Imputa...
101 Logistic Regression_Oversampling_Median Imputa...
102 Logistic Regression_Oversampling_Median Imputa...
103 Logistic Regression_Oversampling_Median Imputa...
104 Logistic Regression_Oversampling_Median Imputa...
105 Logistic Regression_Oversampling_Median Imputa...
106 Logistic Regression_Oversampling_Median Imputa...
107 Logistic Regression_Oversampling_Median Imputa...
108 Logistic Regression_Oversampling_Median Imputa...
109 Logistic Regression_Oversampling_Median Imputa... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Logistic Regression 0.454569 NaN Zero Imputation
1 Logistic Regression 0.082423 NaN Zero Imputation
2 Logistic Regression 0.763713 NaN Zero Imputation
3 Logistic Regression 0.148788 NaN Zero Imputation
4 Logistic Regression 0.620113 NaN Zero Imputation
5 Logistic Regression 0.226164 NaN Zero Imputation
6 Logistic Regression 0.240225 NaN Zero Imputation
7 Logistic Regression NaN 1.0 Zero Imputation
8 Logistic Regression NaN None Zero Imputation
9 Logistic Regression NaN False Zero Imputation
10 Logistic Regression NaN True Zero Imputation
11 Logistic Regression NaN 1 Zero Imputation
12 Logistic Regression NaN None Zero Imputation
13 Logistic Regression NaN 100 Zero Imputation
14 Logistic Regression NaN auto Zero Imputation
15 Logistic Regression NaN None Zero Imputation
16 Logistic Regression NaN l2 Zero Imputation
17 Logistic Regression NaN None Zero Imputation
18 Logistic Regression NaN lbfgs Zero Imputation
19 Logistic Regression NaN 0.0001 Zero Imputation
20 Logistic Regression NaN 0 Zero Imputation
21 Logistic Regression NaN False Zero Imputation
22 Logistic Regression 0.441138 NaN Zero Imputation
23 Logistic Regression 0.056210 NaN Zero Imputation
24 Logistic Regression 0.756098 NaN Zero Imputation
25 Logistic Regression 0.104641 NaN Zero Imputation
26 Logistic Regression 0.632667 NaN Zero Imputation
27 Logistic Regression 0.211379 NaN Zero Imputation
28 Logistic Regression 0.265334 NaN Zero Imputation
29 Logistic Regression NaN 1.0 Zero Imputation
30 Logistic Regression NaN None Zero Imputation
31 Logistic Regression NaN False Zero Imputation
32 Logistic Regression NaN True Zero Imputation
33 Logistic Regression NaN 1 Zero Imputation
34 Logistic Regression NaN None Zero Imputation
35 Logistic Regression NaN 100 Zero Imputation
36 Logistic Regression NaN auto Zero Imputation
37 Logistic Regression NaN None Zero Imputation
38 Logistic Regression NaN l2 Zero Imputation
39 Logistic Regression NaN None Zero Imputation
40 Logistic Regression NaN lbfgs Zero Imputation
41 Logistic Regression NaN 0.0001 Zero Imputation
42 Logistic Regression NaN 0 Zero Imputation
43 Logistic Regression NaN False Zero Imputation
44 Logistic Regression 0.430866 NaN Zero Imputation
45 Logistic Regression 0.064431 NaN Zero Imputation
46 Logistic Regression 0.780749 NaN Zero Imputation
47 Logistic Regression 0.119038 NaN Zero Imputation
48 Logistic Regression 0.632760 NaN Zero Imputation
49 Logistic Regression 0.214095 NaN Zero Imputation
50 Logistic Regression 0.265519 NaN Zero Imputation
51 Logistic Regression NaN 1.0 Zero Imputation
52 Logistic Regression NaN None Zero Imputation
53 Logistic Regression NaN False Zero Imputation
54 Logistic Regression NaN True Zero Imputation
55 Logistic Regression NaN 1 Zero Imputation
56 Logistic Regression NaN None Zero Imputation
57 Logistic Regression NaN 100 Zero Imputation
58 Logistic Regression NaN auto Zero Imputation
59 Logistic Regression NaN None Zero Imputation
60 Logistic Regression NaN l2 Zero Imputation
61 Logistic Regression NaN None Zero Imputation
62 Logistic Regression NaN lbfgs Zero Imputation
63 Logistic Regression NaN 0.0001 Zero Imputation
64 Logistic Regression NaN 0 Zero Imputation
65 Logistic Regression NaN False Zero Imputation
66 Logistic Regression 0.444942 NaN Zero Imputation
67 Logistic Regression 0.067059 NaN Zero Imputation
68 Logistic Regression 0.755102 NaN Zero Imputation
69 Logistic Regression 0.123179 NaN Zero Imputation
70 Logistic Regression 0.642907 NaN Zero Imputation
71 Logistic Regression 0.228339 NaN Zero Imputation
72 Logistic Regression 0.285815 NaN Zero Imputation
73 Logistic Regression NaN 1.0 Zero Imputation
74 Logistic Regression NaN None Zero Imputation
75 Logistic Regression NaN False Zero Imputation
76 Logistic Regression NaN True Zero Imputation
77 Logistic Regression NaN 1 Zero Imputation
78 Logistic Regression NaN None Zero Imputation
79 Logistic Regression NaN 100 Zero Imputation
80 Logistic Regression NaN auto Zero Imputation
81 Logistic Regression NaN None Zero Imputation
82 Logistic Regression NaN l2 Zero Imputation
83 Logistic Regression NaN None Zero Imputation
84 Logistic Regression NaN lbfgs Zero Imputation
85 Logistic Regression NaN 0.0001 Zero Imputation
86 Logistic Regression NaN 0 Zero Imputation
87 Logistic Regression NaN False Zero Imputation
88 Logistic Regression 0.461538 NaN Zero Imputation
89 Logistic Regression 0.073694 NaN Zero Imputation
90 Logistic Regression 0.731481 NaN Zero Imputation
91 Logistic Regression 0.133898 NaN Zero Imputation
92 Logistic Regression 0.631612 NaN Zero Imputation
93 Logistic Regression 0.217577 NaN Zero Imputation
94 Logistic Regression 0.263224 NaN Zero Imputation
95 Logistic Regression NaN 1.0 Zero Imputation
96 Logistic Regression NaN None Zero Imputation
97 Logistic Regression NaN False Zero Imputation
98 Logistic Regression NaN True Zero Imputation
99 Logistic Regression NaN 1 Zero Imputation
100 Logistic Regression NaN None Zero Imputation
101 Logistic Regression NaN 100 Zero Imputation
102 Logistic Regression NaN auto Zero Imputation
103 Logistic Regression NaN None Zero Imputation
104 Logistic Regression NaN l2 Zero Imputation
105 Logistic Regression NaN None Zero Imputation
106 Logistic Regression NaN lbfgs Zero Imputation
107 Logistic Regression NaN 0.0001 Zero Imputation
108 Logistic Regression NaN 0 Zero Imputation
109 Logistic Regression NaN False Zero Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
45 Undersampling
46 Undersampling
47 Undersampling
48 Undersampling
49 Undersampling
50 Undersampling
51 Undersampling
52 Undersampling
53 Undersampling
54 Undersampling
55 Undersampling
56 Undersampling
57 Undersampling
58 Undersampling
59 Undersampling
60 Undersampling
61 Undersampling
62 Undersampling
63 Undersampling
64 Undersampling
65 Undersampling
66 Undersampling
67 Undersampling
68 Undersampling
69 Undersampling
70 Undersampling
71 Undersampling
72 Undersampling
73 Undersampling
74 Undersampling
75 Undersampling
76 Undersampling
77 Undersampling
78 Undersampling
79 Undersampling
80 Undersampling
81 Undersampling
82 Undersampling
83 Undersampling
84 Undersampling
85 Undersampling
86 Undersampling
87 Undersampling
88 Undersampling
89 Undersampling
90 Undersampling
91 Undersampling
92 Undersampling
93 Undersampling
94 Undersampling
95 Undersampling
96 Undersampling
97 Undersampling
98 Undersampling
99 Undersampling
100 Undersampling
101 Undersampling
102 Undersampling
103 Undersampling
104 Undersampling
105 Undersampling
106 Undersampling
107 Undersampling
108 Undersampling
109 Undersampling
Model Unique Code
0 Logistic Regression_Undersampling_Zero Imputat...
1 Logistic Regression_Undersampling_Zero Imputat...
2 Logistic Regression_Undersampling_Zero Imputat...
3 Logistic Regression_Undersampling_Zero Imputat...
4 Logistic Regression_Undersampling_Zero Imputat...
5 Logistic Regression_Undersampling_Zero Imputat...
6 Logistic Regression_Undersampling_Zero Imputat...
7 Logistic Regression_Undersampling_Zero Imputat...
8 Logistic Regression_Undersampling_Zero Imputat...
9 Logistic Regression_Undersampling_Zero Imputat...
10 Logistic Regression_Undersampling_Zero Imputat...
11 Logistic Regression_Undersampling_Zero Imputat...
12 Logistic Regression_Undersampling_Zero Imputat...
13 Logistic Regression_Undersampling_Zero Imputat...
14 Logistic Regression_Undersampling_Zero Imputat...
15 Logistic Regression_Undersampling_Zero Imputat...
16 Logistic Regression_Undersampling_Zero Imputat...
17 Logistic Regression_Undersampling_Zero Imputat...
18 Logistic Regression_Undersampling_Zero Imputat...
19 Logistic Regression_Undersampling_Zero Imputat...
20 Logistic Regression_Undersampling_Zero Imputat...
21 Logistic Regression_Undersampling_Zero Imputat...
22 Logistic Regression_Undersampling_Zero Imputat...
23 Logistic Regression_Undersampling_Zero Imputat...
24 Logistic Regression_Undersampling_Zero Imputat...
25 Logistic Regression_Undersampling_Zero Imputat...
26 Logistic Regression_Undersampling_Zero Imputat...
27 Logistic Regression_Undersampling_Zero Imputat...
28 Logistic Regression_Undersampling_Zero Imputat...
29 Logistic Regression_Undersampling_Zero Imputat...
30 Logistic Regression_Undersampling_Zero Imputat...
31 Logistic Regression_Undersampling_Zero Imputat...
32 Logistic Regression_Undersampling_Zero Imputat...
33 Logistic Regression_Undersampling_Zero Imputat...
34 Logistic Regression_Undersampling_Zero Imputat...
35 Logistic Regression_Undersampling_Zero Imputat...
36 Logistic Regression_Undersampling_Zero Imputat...
37 Logistic Regression_Undersampling_Zero Imputat...
38 Logistic Regression_Undersampling_Zero Imputat...
39 Logistic Regression_Undersampling_Zero Imputat...
40 Logistic Regression_Undersampling_Zero Imputat...
41 Logistic Regression_Undersampling_Zero Imputat...
42 Logistic Regression_Undersampling_Zero Imputat...
43 Logistic Regression_Undersampling_Zero Imputat...
44 Logistic Regression_Undersampling_Zero Imputat...
45 Logistic Regression_Undersampling_Zero Imputat...
46 Logistic Regression_Undersampling_Zero Imputat...
47 Logistic Regression_Undersampling_Zero Imputat...
48 Logistic Regression_Undersampling_Zero Imputat...
49 Logistic Regression_Undersampling_Zero Imputat...
50 Logistic Regression_Undersampling_Zero Imputat...
51 Logistic Regression_Undersampling_Zero Imputat...
52 Logistic Regression_Undersampling_Zero Imputat...
53 Logistic Regression_Undersampling_Zero Imputat...
54 Logistic Regression_Undersampling_Zero Imputat...
55 Logistic Regression_Undersampling_Zero Imputat...
56 Logistic Regression_Undersampling_Zero Imputat...
57 Logistic Regression_Undersampling_Zero Imputat...
58 Logistic Regression_Undersampling_Zero Imputat...
59 Logistic Regression_Undersampling_Zero Imputat...
60 Logistic Regression_Undersampling_Zero Imputat...
61 Logistic Regression_Undersampling_Zero Imputat...
62 Logistic Regression_Undersampling_Zero Imputat...
63 Logistic Regression_Undersampling_Zero Imputat...
64 Logistic Regression_Undersampling_Zero Imputat...
65 Logistic Regression_Undersampling_Zero Imputat...
66 Logistic Regression_Undersampling_Zero Imputat...
67 Logistic Regression_Undersampling_Zero Imputat...
68 Logistic Regression_Undersampling_Zero Imputat...
69 Logistic Regression_Undersampling_Zero Imputat...
70 Logistic Regression_Undersampling_Zero Imputat...
71 Logistic Regression_Undersampling_Zero Imputat...
72 Logistic Regression_Undersampling_Zero Imputat...
73 Logistic Regression_Undersampling_Zero Imputat...
74 Logistic Regression_Undersampling_Zero Imputat...
75 Logistic Regression_Undersampling_Zero Imputat...
76 Logistic Regression_Undersampling_Zero Imputat...
77 Logistic Regression_Undersampling_Zero Imputat...
78 Logistic Regression_Undersampling_Zero Imputat...
79 Logistic Regression_Undersampling_Zero Imputat...
80 Logistic Regression_Undersampling_Zero Imputat...
81 Logistic Regression_Undersampling_Zero Imputat...
82 Logistic Regression_Undersampling_Zero Imputat...
83 Logistic Regression_Undersampling_Zero Imputat...
84 Logistic Regression_Undersampling_Zero Imputat...
85 Logistic Regression_Undersampling_Zero Imputat...
86 Logistic Regression_Undersampling_Zero Imputat...
87 Logistic Regression_Undersampling_Zero Imputat...
88 Logistic Regression_Undersampling_Zero Imputat...
89 Logistic Regression_Undersampling_Zero Imputat...
90 Logistic Regression_Undersampling_Zero Imputat...
91 Logistic Regression_Undersampling_Zero Imputat...
92 Logistic Regression_Undersampling_Zero Imputat...
93 Logistic Regression_Undersampling_Zero Imputat...
94 Logistic Regression_Undersampling_Zero Imputat...
95 Logistic Regression_Undersampling_Zero Imputat...
96 Logistic Regression_Undersampling_Zero Imputat...
97 Logistic Regression_Undersampling_Zero Imputat...
98 Logistic Regression_Undersampling_Zero Imputat...
99 Logistic Regression_Undersampling_Zero Imputat...
100 Logistic Regression_Undersampling_Zero Imputat...
101 Logistic Regression_Undersampling_Zero Imputat...
102 Logistic Regression_Undersampling_Zero Imputat...
103 Logistic Regression_Undersampling_Zero Imputat...
104 Logistic Regression_Undersampling_Zero Imputat...
105 Logistic Regression_Undersampling_Zero Imputat...
106 Logistic Regression_Undersampling_Zero Imputat...
107 Logistic Regression_Undersampling_Zero Imputat...
108 Logistic Regression_Undersampling_Zero Imputat...
109 Logistic Regression_Undersampling_Zero Imputat... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Logistic Regression 0.430076 NaN Mean Imputation
1 Logistic Regression 0.083801 NaN Mean Imputation
2 Logistic Regression 0.818565 NaN Mean Imputation
3 Logistic Regression 0.152038 NaN Mean Imputation
4 Logistic Regression 0.615221 NaN Mean Imputation
5 Logistic Regression 0.254437 NaN Mean Imputation
6 Logistic Regression 0.230443 NaN Mean Imputation
7 Logistic Regression NaN 1.0 Mean Imputation
8 Logistic Regression NaN None Mean Imputation
9 Logistic Regression NaN False Mean Imputation
10 Logistic Regression NaN True Mean Imputation
11 Logistic Regression NaN 1 Mean Imputation
12 Logistic Regression NaN None Mean Imputation
13 Logistic Regression NaN 100 Mean Imputation
14 Logistic Regression NaN auto Mean Imputation
15 Logistic Regression NaN None Mean Imputation
16 Logistic Regression NaN l2 Mean Imputation
17 Logistic Regression NaN None Mean Imputation
18 Logistic Regression NaN lbfgs Mean Imputation
19 Logistic Regression NaN 0.0001 Mean Imputation
20 Logistic Regression NaN 0 Mean Imputation
21 Logistic Regression NaN False Mean Imputation
22 Logistic Regression 0.454306 NaN Mean Imputation
23 Logistic Regression 0.058742 NaN Mean Imputation
24 Logistic Regression 0.774390 NaN Mean Imputation
25 Logistic Regression 0.109200 NaN Mean Imputation
26 Logistic Regression 0.601274 NaN Mean Imputation
27 Logistic Regression 0.248276 NaN Mean Imputation
28 Logistic Regression 0.202549 NaN Mean Imputation
29 Logistic Regression NaN 1.0 Mean Imputation
30 Logistic Regression NaN None Mean Imputation
31 Logistic Regression NaN False Mean Imputation
32 Logistic Regression NaN True Mean Imputation
33 Logistic Regression NaN 1 Mean Imputation
34 Logistic Regression NaN None Mean Imputation
35 Logistic Regression NaN 100 Mean Imputation
36 Logistic Regression NaN auto Mean Imputation
37 Logistic Regression NaN None Mean Imputation
38 Logistic Regression NaN l2 Mean Imputation
39 Logistic Regression NaN None Mean Imputation
40 Logistic Regression NaN lbfgs Mean Imputation
41 Logistic Regression NaN 0.0001 Mean Imputation
42 Logistic Regression NaN 0 Mean Imputation
43 Logistic Regression NaN False Mean Imputation
44 Logistic Regression 0.451409 NaN Mean Imputation
45 Logistic Regression 0.065934 NaN Mean Imputation
46 Logistic Regression 0.770053 NaN Mean Imputation
47 Logistic Regression 0.121468 NaN Mean Imputation
48 Logistic Regression 0.632651 NaN Mean Imputation
49 Logistic Regression 0.264656 NaN Mean Imputation
50 Logistic Regression 0.265303 NaN Mean Imputation
51 Logistic Regression NaN 1.0 Mean Imputation
52 Logistic Regression NaN None Mean Imputation
53 Logistic Regression NaN False Mean Imputation
54 Logistic Regression NaN True Mean Imputation
55 Logistic Regression NaN 1 Mean Imputation
56 Logistic Regression NaN None Mean Imputation
57 Logistic Regression NaN 100 Mean Imputation
58 Logistic Regression NaN auto Mean Imputation
59 Logistic Regression NaN None Mean Imputation
60 Logistic Regression NaN l2 Mean Imputation
61 Logistic Regression NaN None Mean Imputation
62 Logistic Regression NaN lbfgs Mean Imputation
63 Logistic Regression NaN 0.0001 Mean Imputation
64 Logistic Regression NaN 0 Mean Imputation
65 Logistic Regression NaN False Mean Imputation
66 Logistic Regression 0.464963 NaN Mean Imputation
67 Logistic Regression 0.064960 NaN Mean Imputation
68 Logistic Regression 0.698980 NaN Mean Imputation
69 Logistic Regression 0.118872 NaN Mean Imputation
70 Logistic Regression 0.596278 NaN Mean Imputation
71 Logistic Regression 0.198617 NaN Mean Imputation
72 Logistic Regression 0.192555 NaN Mean Imputation
73 Logistic Regression NaN 1.0 Mean Imputation
74 Logistic Regression NaN None Mean Imputation
75 Logistic Regression NaN False Mean Imputation
76 Logistic Regression NaN True Mean Imputation
77 Logistic Regression NaN 1 Mean Imputation
78 Logistic Regression NaN None Mean Imputation
79 Logistic Regression NaN 100 Mean Imputation
80 Logistic Regression NaN auto Mean Imputation
81 Logistic Regression NaN None Mean Imputation
82 Logistic Regression NaN l2 Mean Imputation
83 Logistic Regression NaN None Mean Imputation
84 Logistic Regression NaN lbfgs Mean Imputation
85 Logistic Regression NaN 0.0001 Mean Imputation
86 Logistic Regression NaN 0 Mean Imputation
87 Logistic Regression NaN False Mean Imputation
88 Logistic Regression 0.485774 NaN Mean Imputation
89 Logistic Regression 0.071570 NaN Mean Imputation
90 Logistic Regression 0.671296 NaN Mean Imputation
91 Logistic Regression 0.129349 NaN Mean Imputation
92 Logistic Regression 0.567091 NaN Mean Imputation
93 Logistic Regression 0.167194 NaN Mean Imputation
94 Logistic Regression 0.134183 NaN Mean Imputation
95 Logistic Regression NaN 1.0 Mean Imputation
96 Logistic Regression NaN None Mean Imputation
97 Logistic Regression NaN False Mean Imputation
98 Logistic Regression NaN True Mean Imputation
99 Logistic Regression NaN 1 Mean Imputation
100 Logistic Regression NaN None Mean Imputation
101 Logistic Regression NaN 100 Mean Imputation
102 Logistic Regression NaN auto Mean Imputation
103 Logistic Regression NaN None Mean Imputation
104 Logistic Regression NaN l2 Mean Imputation
105 Logistic Regression NaN None Mean Imputation
106 Logistic Regression NaN lbfgs Mean Imputation
107 Logistic Regression NaN 0.0001 Mean Imputation
108 Logistic Regression NaN 0 Mean Imputation
109 Logistic Regression NaN False Mean Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
45 Undersampling
46 Undersampling
47 Undersampling
48 Undersampling
49 Undersampling
50 Undersampling
51 Undersampling
52 Undersampling
53 Undersampling
54 Undersampling
55 Undersampling
56 Undersampling
57 Undersampling
58 Undersampling
59 Undersampling
60 Undersampling
61 Undersampling
62 Undersampling
63 Undersampling
64 Undersampling
65 Undersampling
66 Undersampling
67 Undersampling
68 Undersampling
69 Undersampling
70 Undersampling
71 Undersampling
72 Undersampling
73 Undersampling
74 Undersampling
75 Undersampling
76 Undersampling
77 Undersampling
78 Undersampling
79 Undersampling
80 Undersampling
81 Undersampling
82 Undersampling
83 Undersampling
84 Undersampling
85 Undersampling
86 Undersampling
87 Undersampling
88 Undersampling
89 Undersampling
90 Undersampling
91 Undersampling
92 Undersampling
93 Undersampling
94 Undersampling
95 Undersampling
96 Undersampling
97 Undersampling
98 Undersampling
99 Undersampling
100 Undersampling
101 Undersampling
102 Undersampling
103 Undersampling
104 Undersampling
105 Undersampling
106 Undersampling
107 Undersampling
108 Undersampling
109 Undersampling
Model Unique Code
0 Logistic Regression_Undersampling_Mean Imputat...
1 Logistic Regression_Undersampling_Mean Imputat...
2 Logistic Regression_Undersampling_Mean Imputat...
3 Logistic Regression_Undersampling_Mean Imputat...
4 Logistic Regression_Undersampling_Mean Imputat...
5 Logistic Regression_Undersampling_Mean Imputat...
6 Logistic Regression_Undersampling_Mean Imputat...
7 Logistic Regression_Undersampling_Mean Imputat...
8 Logistic Regression_Undersampling_Mean Imputat...
9 Logistic Regression_Undersampling_Mean Imputat...
10 Logistic Regression_Undersampling_Mean Imputat...
11 Logistic Regression_Undersampling_Mean Imputat...
12 Logistic Regression_Undersampling_Mean Imputat...
13 Logistic Regression_Undersampling_Mean Imputat...
14 Logistic Regression_Undersampling_Mean Imputat...
15 Logistic Regression_Undersampling_Mean Imputat...
16 Logistic Regression_Undersampling_Mean Imputat...
17 Logistic Regression_Undersampling_Mean Imputat...
18 Logistic Regression_Undersampling_Mean Imputat...
19 Logistic Regression_Undersampling_Mean Imputat...
20 Logistic Regression_Undersampling_Mean Imputat...
21 Logistic Regression_Undersampling_Mean Imputat...
22 Logistic Regression_Undersampling_Mean Imputat...
23 Logistic Regression_Undersampling_Mean Imputat...
24 Logistic Regression_Undersampling_Mean Imputat...
25 Logistic Regression_Undersampling_Mean Imputat...
26 Logistic Regression_Undersampling_Mean Imputat...
27 Logistic Regression_Undersampling_Mean Imputat...
28 Logistic Regression_Undersampling_Mean Imputat...
29 Logistic Regression_Undersampling_Mean Imputat...
30 Logistic Regression_Undersampling_Mean Imputat...
31 Logistic Regression_Undersampling_Mean Imputat...
32 Logistic Regression_Undersampling_Mean Imputat...
33 Logistic Regression_Undersampling_Mean Imputat...
34 Logistic Regression_Undersampling_Mean Imputat...
35 Logistic Regression_Undersampling_Mean Imputat...
36 Logistic Regression_Undersampling_Mean Imputat...
37 Logistic Regression_Undersampling_Mean Imputat...
38 Logistic Regression_Undersampling_Mean Imputat...
39 Logistic Regression_Undersampling_Mean Imputat...
40 Logistic Regression_Undersampling_Mean Imputat...
41 Logistic Regression_Undersampling_Mean Imputat...
42 Logistic Regression_Undersampling_Mean Imputat...
43 Logistic Regression_Undersampling_Mean Imputat...
44 Logistic Regression_Undersampling_Mean Imputat...
45 Logistic Regression_Undersampling_Mean Imputat...
46 Logistic Regression_Undersampling_Mean Imputat...
47 Logistic Regression_Undersampling_Mean Imputat...
48 Logistic Regression_Undersampling_Mean Imputat...
49 Logistic Regression_Undersampling_Mean Imputat...
50 Logistic Regression_Undersampling_Mean Imputat...
51 Logistic Regression_Undersampling_Mean Imputat...
52 Logistic Regression_Undersampling_Mean Imputat...
53 Logistic Regression_Undersampling_Mean Imputat...
54 Logistic Regression_Undersampling_Mean Imputat...
55 Logistic Regression_Undersampling_Mean Imputat...
56 Logistic Regression_Undersampling_Mean Imputat...
57 Logistic Regression_Undersampling_Mean Imputat...
58 Logistic Regression_Undersampling_Mean Imputat...
59 Logistic Regression_Undersampling_Mean Imputat...
60 Logistic Regression_Undersampling_Mean Imputat...
61 Logistic Regression_Undersampling_Mean Imputat...
62 Logistic Regression_Undersampling_Mean Imputat...
63 Logistic Regression_Undersampling_Mean Imputat...
64 Logistic Regression_Undersampling_Mean Imputat...
65 Logistic Regression_Undersampling_Mean Imputat...
66 Logistic Regression_Undersampling_Mean Imputat...
67 Logistic Regression_Undersampling_Mean Imputat...
68 Logistic Regression_Undersampling_Mean Imputat...
69 Logistic Regression_Undersampling_Mean Imputat...
70 Logistic Regression_Undersampling_Mean Imputat...
71 Logistic Regression_Undersampling_Mean Imputat...
72 Logistic Regression_Undersampling_Mean Imputat...
73 Logistic Regression_Undersampling_Mean Imputat...
74 Logistic Regression_Undersampling_Mean Imputat...
75 Logistic Regression_Undersampling_Mean Imputat...
76 Logistic Regression_Undersampling_Mean Imputat...
77 Logistic Regression_Undersampling_Mean Imputat...
78 Logistic Regression_Undersampling_Mean Imputat...
79 Logistic Regression_Undersampling_Mean Imputat...
80 Logistic Regression_Undersampling_Mean Imputat...
81 Logistic Regression_Undersampling_Mean Imputat...
82 Logistic Regression_Undersampling_Mean Imputat...
83 Logistic Regression_Undersampling_Mean Imputat...
84 Logistic Regression_Undersampling_Mean Imputat...
85 Logistic Regression_Undersampling_Mean Imputat...
86 Logistic Regression_Undersampling_Mean Imputat...
87 Logistic Regression_Undersampling_Mean Imputat...
88 Logistic Regression_Undersampling_Mean Imputat...
89 Logistic Regression_Undersampling_Mean Imputat...
90 Logistic Regression_Undersampling_Mean Imputat...
91 Logistic Regression_Undersampling_Mean Imputat...
92 Logistic Regression_Undersampling_Mean Imputat...
93 Logistic Regression_Undersampling_Mean Imputat...
94 Logistic Regression_Undersampling_Mean Imputat...
95 Logistic Regression_Undersampling_Mean Imputat...
96 Logistic Regression_Undersampling_Mean Imputat...
97 Logistic Regression_Undersampling_Mean Imputat...
98 Logistic Regression_Undersampling_Mean Imputat...
99 Logistic Regression_Undersampling_Mean Imputat...
100 Logistic Regression_Undersampling_Mean Imputat...
101 Logistic Regression_Undersampling_Mean Imputat...
102 Logistic Regression_Undersampling_Mean Imputat...
103 Logistic Regression_Undersampling_Mean Imputat...
104 Logistic Regression_Undersampling_Mean Imputat...
105 Logistic Regression_Undersampling_Mean Imputat...
106 Logistic Regression_Undersampling_Mean Imputat...
107 Logistic Regression_Undersampling_Mean Imputat...
108 Logistic Regression_Undersampling_Mean Imputat...
109 Logistic Regression_Undersampling_Mean Imputat... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Logistic Regression 0.486700 NaN Median Imputation
1 Logistic Regression 0.085271 NaN Median Imputation
2 Logistic Regression 0.742616 NaN Median Imputation
3 Logistic Regression 0.152977 NaN Median Imputation
4 Logistic Regression 0.623277 NaN Median Imputation
5 Logistic Regression 0.234272 NaN Median Imputation
6 Logistic Regression 0.246555 NaN Median Imputation
7 Logistic Regression NaN 1.0 Median Imputation
8 Logistic Regression NaN None Median Imputation
9 Logistic Regression NaN False Median Imputation
10 Logistic Regression NaN True Median Imputation
11 Logistic Regression NaN 1 Median Imputation
12 Logistic Regression NaN None Median Imputation
13 Logistic Regression NaN 100 Median Imputation
14 Logistic Regression NaN auto Median Imputation
15 Logistic Regression NaN None Median Imputation
16 Logistic Regression NaN l2 Median Imputation
17 Logistic Regression NaN None Median Imputation
18 Logistic Regression NaN lbfgs Median Imputation
19 Logistic Regression NaN 0.0001 Median Imputation
20 Logistic Regression NaN 0 Median Imputation
21 Logistic Regression NaN False Median Imputation
22 Logistic Regression 0.561233 NaN Median Imputation
23 Logistic Regression 0.056671 NaN Median Imputation
24 Logistic Regression 0.585366 NaN Median Imputation
25 Logistic Regression 0.103337 NaN Median Imputation
26 Logistic Regression 0.600737 NaN Median Imputation
27 Logistic Regression 0.196864 NaN Median Imputation
28 Logistic Regression 0.201475 NaN Median Imputation
29 Logistic Regression NaN 1.0 Median Imputation
30 Logistic Regression NaN None Median Imputation
31 Logistic Regression NaN False Median Imputation
32 Logistic Regression NaN True Median Imputation
33 Logistic Regression NaN 1 Median Imputation
34 Logistic Regression NaN None Median Imputation
35 Logistic Regression NaN 100 Median Imputation
36 Logistic Regression NaN auto Median Imputation
37 Logistic Regression NaN None Median Imputation
38 Logistic Regression NaN l2 Median Imputation
39 Logistic Regression NaN None Median Imputation
40 Logistic Regression NaN lbfgs Median Imputation
41 Logistic Regression NaN 0.0001 Median Imputation
42 Logistic Regression NaN 0 Median Imputation
43 Logistic Regression NaN False Median Imputation
44 Logistic Regression 0.568080 NaN Median Imputation
45 Logistic Regression 0.072396 NaN Median Imputation
46 Logistic Regression 0.657754 NaN Median Imputation
47 Logistic Regression 0.130435 NaN Median Imputation
48 Logistic Regression 0.630588 NaN Median Imputation
49 Logistic Regression 0.223405 NaN Median Imputation
50 Logistic Regression 0.261176 NaN Median Imputation
51 Logistic Regression NaN 1.0 Median Imputation
52 Logistic Regression NaN None Median Imputation
53 Logistic Regression NaN False Median Imputation
54 Logistic Regression NaN True Median Imputation
55 Logistic Regression NaN 1 Median Imputation
56 Logistic Regression NaN None Median Imputation
57 Logistic Regression NaN 100 Median Imputation
58 Logistic Regression NaN auto Median Imputation
59 Logistic Regression NaN None Median Imputation
60 Logistic Regression NaN l2 Median Imputation
61 Logistic Regression NaN None Median Imputation
62 Logistic Regression NaN lbfgs Median Imputation
63 Logistic Regression NaN 0.0001 Median Imputation
64 Logistic Regression NaN 0 Median Imputation
65 Logistic Regression NaN False Median Imputation
66 Logistic Regression 0.490253 NaN Median Imputation
67 Logistic Regression 0.068486 NaN Median Imputation
68 Logistic Regression 0.704082 NaN Median Imputation
69 Logistic Regression 0.124830 NaN Median Imputation
70 Logistic Regression 0.620117 NaN Median Imputation
71 Logistic Regression 0.218464 NaN Median Imputation
72 Logistic Regression 0.240234 NaN Median Imputation
73 Logistic Regression NaN 1.0 Median Imputation
74 Logistic Regression NaN None Median Imputation
75 Logistic Regression NaN False Median Imputation
76 Logistic Regression NaN True Median Imputation
77 Logistic Regression NaN 1 Median Imputation
78 Logistic Regression NaN None Median Imputation
79 Logistic Regression NaN 100 Median Imputation
80 Logistic Regression NaN auto Median Imputation
81 Logistic Regression NaN None Median Imputation
82 Logistic Regression NaN l2 Median Imputation
83 Logistic Regression NaN None Median Imputation
84 Logistic Regression NaN lbfgs Median Imputation
85 Logistic Regression NaN 0.0001 Median Imputation
86 Logistic Regression NaN 0 Median Imputation
87 Logistic Regression NaN False Median Imputation
88 Logistic Regression 0.532139 NaN Median Imputation
89 Logistic Regression 0.077922 NaN Median Imputation
90 Logistic Regression 0.666667 NaN Median Imputation
91 Logistic Regression 0.139535 NaN Median Imputation
92 Logistic Regression 0.600089 NaN Median Imputation
93 Logistic Regression 0.190689 NaN Median Imputation
94 Logistic Regression 0.200178 NaN Median Imputation
95 Logistic Regression NaN 1.0 Median Imputation
96 Logistic Regression NaN None Median Imputation
97 Logistic Regression NaN False Median Imputation
98 Logistic Regression NaN True Median Imputation
99 Logistic Regression NaN 1 Median Imputation
100 Logistic Regression NaN None Median Imputation
101 Logistic Regression NaN 100 Median Imputation
102 Logistic Regression NaN auto Median Imputation
103 Logistic Regression NaN None Median Imputation
104 Logistic Regression NaN l2 Median Imputation
105 Logistic Regression NaN None Median Imputation
106 Logistic Regression NaN lbfgs Median Imputation
107 Logistic Regression NaN 0.0001 Median Imputation
108 Logistic Regression NaN 0 Median Imputation
109 Logistic Regression NaN False Median Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
45 Undersampling
46 Undersampling
47 Undersampling
48 Undersampling
49 Undersampling
50 Undersampling
51 Undersampling
52 Undersampling
53 Undersampling
54 Undersampling
55 Undersampling
56 Undersampling
57 Undersampling
58 Undersampling
59 Undersampling
60 Undersampling
61 Undersampling
62 Undersampling
63 Undersampling
64 Undersampling
65 Undersampling
66 Undersampling
67 Undersampling
68 Undersampling
69 Undersampling
70 Undersampling
71 Undersampling
72 Undersampling
73 Undersampling
74 Undersampling
75 Undersampling
76 Undersampling
77 Undersampling
78 Undersampling
79 Undersampling
80 Undersampling
81 Undersampling
82 Undersampling
83 Undersampling
84 Undersampling
85 Undersampling
86 Undersampling
87 Undersampling
88 Undersampling
89 Undersampling
90 Undersampling
91 Undersampling
92 Undersampling
93 Undersampling
94 Undersampling
95 Undersampling
96 Undersampling
97 Undersampling
98 Undersampling
99 Undersampling
100 Undersampling
101 Undersampling
102 Undersampling
103 Undersampling
104 Undersampling
105 Undersampling
106 Undersampling
107 Undersampling
108 Undersampling
109 Undersampling
Model Unique Code
0 Logistic Regression_Undersampling_Median Imput...
1 Logistic Regression_Undersampling_Median Imput...
2 Logistic Regression_Undersampling_Median Imput...
3 Logistic Regression_Undersampling_Median Imput...
4 Logistic Regression_Undersampling_Median Imput...
5 Logistic Regression_Undersampling_Median Imput...
6 Logistic Regression_Undersampling_Median Imput...
7 Logistic Regression_Undersampling_Median Imput...
8 Logistic Regression_Undersampling_Median Imput...
9 Logistic Regression_Undersampling_Median Imput...
10 Logistic Regression_Undersampling_Median Imput...
11 Logistic Regression_Undersampling_Median Imput...
12 Logistic Regression_Undersampling_Median Imput...
13 Logistic Regression_Undersampling_Median Imput...
14 Logistic Regression_Undersampling_Median Imput...
15 Logistic Regression_Undersampling_Median Imput...
16 Logistic Regression_Undersampling_Median Imput...
17 Logistic Regression_Undersampling_Median Imput...
18 Logistic Regression_Undersampling_Median Imput...
19 Logistic Regression_Undersampling_Median Imput...
20 Logistic Regression_Undersampling_Median Imput...
21 Logistic Regression_Undersampling_Median Imput...
22 Logistic Regression_Undersampling_Median Imput...
23 Logistic Regression_Undersampling_Median Imput...
24 Logistic Regression_Undersampling_Median Imput...
25 Logistic Regression_Undersampling_Median Imput...
26 Logistic Regression_Undersampling_Median Imput...
27 Logistic Regression_Undersampling_Median Imput...
28 Logistic Regression_Undersampling_Median Imput...
29 Logistic Regression_Undersampling_Median Imput...
30 Logistic Regression_Undersampling_Median Imput...
31 Logistic Regression_Undersampling_Median Imput...
32 Logistic Regression_Undersampling_Median Imput...
33 Logistic Regression_Undersampling_Median Imput...
34 Logistic Regression_Undersampling_Median Imput...
35 Logistic Regression_Undersampling_Median Imput...
36 Logistic Regression_Undersampling_Median Imput...
37 Logistic Regression_Undersampling_Median Imput...
38 Logistic Regression_Undersampling_Median Imput...
39 Logistic Regression_Undersampling_Median Imput...
40 Logistic Regression_Undersampling_Median Imput...
41 Logistic Regression_Undersampling_Median Imput...
42 Logistic Regression_Undersampling_Median Imput...
43 Logistic Regression_Undersampling_Median Imput...
44 Logistic Regression_Undersampling_Median Imput...
45 Logistic Regression_Undersampling_Median Imput...
46 Logistic Regression_Undersampling_Median Imput...
47 Logistic Regression_Undersampling_Median Imput...
48 Logistic Regression_Undersampling_Median Imput...
49 Logistic Regression_Undersampling_Median Imput...
50 Logistic Regression_Undersampling_Median Imput...
51 Logistic Regression_Undersampling_Median Imput...
52 Logistic Regression_Undersampling_Median Imput...
53 Logistic Regression_Undersampling_Median Imput...
54 Logistic Regression_Undersampling_Median Imput...
55 Logistic Regression_Undersampling_Median Imput...
56 Logistic Regression_Undersampling_Median Imput...
57 Logistic Regression_Undersampling_Median Imput...
58 Logistic Regression_Undersampling_Median Imput...
59 Logistic Regression_Undersampling_Median Imput...
60 Logistic Regression_Undersampling_Median Imput...
61 Logistic Regression_Undersampling_Median Imput...
62 Logistic Regression_Undersampling_Median Imput...
63 Logistic Regression_Undersampling_Median Imput...
64 Logistic Regression_Undersampling_Median Imput...
65 Logistic Regression_Undersampling_Median Imput...
66 Logistic Regression_Undersampling_Median Imput...
67 Logistic Regression_Undersampling_Median Imput...
68 Logistic Regression_Undersampling_Median Imput...
69 Logistic Regression_Undersampling_Median Imput...
70 Logistic Regression_Undersampling_Median Imput...
71 Logistic Regression_Undersampling_Median Imput...
72 Logistic Regression_Undersampling_Median Imput...
73 Logistic Regression_Undersampling_Median Imput...
74 Logistic Regression_Undersampling_Median Imput...
75 Logistic Regression_Undersampling_Median Imput...
76 Logistic Regression_Undersampling_Median Imput...
77 Logistic Regression_Undersampling_Median Imput...
78 Logistic Regression_Undersampling_Median Imput...
79 Logistic Regression_Undersampling_Median Imput...
80 Logistic Regression_Undersampling_Median Imput...
81 Logistic Regression_Undersampling_Median Imput...
82 Logistic Regression_Undersampling_Median Imput...
83 Logistic Regression_Undersampling_Median Imput...
84 Logistic Regression_Undersampling_Median Imput...
85 Logistic Regression_Undersampling_Median Imput...
86 Logistic Regression_Undersampling_Median Imput...
87 Logistic Regression_Undersampling_Median Imput...
88 Logistic Regression_Undersampling_Median Imput...
89 Logistic Regression_Undersampling_Median Imput...
90 Logistic Regression_Undersampling_Median Imput...
91 Logistic Regression_Undersampling_Median Imput...
92 Logistic Regression_Undersampling_Median Imput...
93 Logistic Regression_Undersampling_Median Imput...
94 Logistic Regression_Undersampling_Median Imput...
95 Logistic Regression_Undersampling_Median Imput...
96 Logistic Regression_Undersampling_Median Imput...
97 Logistic Regression_Undersampling_Median Imput...
98 Logistic Regression_Undersampling_Median Imput...
99 Logistic Regression_Undersampling_Median Imput...
100 Logistic Regression_Undersampling_Median Imput...
101 Logistic Regression_Undersampling_Median Imput...
102 Logistic Regression_Undersampling_Median Imput...
103 Logistic Regression_Undersampling_Median Imput...
104 Logistic Regression_Undersampling_Median Imput...
105 Logistic Regression_Undersampling_Median Imput...
106 Logistic Regression_Undersampling_Median Imput...
107 Logistic Regression_Undersampling_Median Imput...
108 Logistic Regression_Undersampling_Median Imput...
109 Logistic Regression_Undersampling_Median Imput... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Logistic Regression 0.390308 NaN Zero Imputation
1 Logistic Regression 0.081386 NaN Zero Imputation
2 Logistic Regression 0.852321 NaN Zero Imputation
3 Logistic Regression 0.148584 NaN Zero Imputation
4 Logistic Regression 0.636137 NaN Zero Imputation
5 Logistic Regression 0.249106 NaN Zero Imputation
6 Logistic Regression 0.272274 NaN Zero Imputation
7 Logistic Regression NaN 1.0 Zero Imputation
8 Logistic Regression NaN None Zero Imputation
9 Logistic Regression NaN False Zero Imputation
10 Logistic Regression NaN True Zero Imputation
11 Logistic Regression NaN 1 Zero Imputation
12 Logistic Regression NaN None Zero Imputation
13 Logistic Regression NaN 100 Zero Imputation
14 Logistic Regression NaN auto Zero Imputation
15 Logistic Regression NaN None Zero Imputation
16 Logistic Regression NaN l2 Zero Imputation
17 Logistic Regression NaN None Zero Imputation
18 Logistic Regression NaN lbfgs Zero Imputation
19 Logistic Regression NaN 0.0001 Zero Imputation
20 Logistic Regression NaN 0 Zero Imputation
21 Logistic Regression NaN False Zero Imputation
22 Logistic Regression 0.360284 NaN Zero Imputation
23 Logistic Regression 0.054660 NaN Zero Imputation
24 Logistic Regression 0.847561 NaN Zero Imputation
25 Logistic Regression 0.102697 NaN Zero Imputation
26 Logistic Regression 0.648449 NaN Zero Imputation
27 Logistic Regression 0.250274 NaN Zero Imputation
28 Logistic Regression 0.296897 NaN Zero Imputation
29 Logistic Regression NaN 1.0 Zero Imputation
30 Logistic Regression NaN None Zero Imputation
31 Logistic Regression NaN False Zero Imputation
32 Logistic Regression NaN True Zero Imputation
33 Logistic Regression NaN 1 Zero Imputation
34 Logistic Regression NaN None Zero Imputation
35 Logistic Regression NaN 100 Zero Imputation
36 Logistic Regression NaN auto Zero Imputation
37 Logistic Regression NaN None Zero Imputation
38 Logistic Regression NaN l2 Zero Imputation
39 Logistic Regression NaN None Zero Imputation
40 Logistic Regression NaN lbfgs Zero Imputation
41 Logistic Regression NaN 0.0001 Zero Imputation
42 Logistic Regression NaN 0 Zero Imputation
43 Logistic Regression NaN False Zero Imputation
44 Logistic Regression 0.363972 NaN Zero Imputation
45 Logistic Regression 0.060726 NaN Zero Imputation
46 Logistic Regression 0.823529 NaN Zero Imputation
47 Logistic Regression 0.113111 NaN Zero Imputation
48 Logistic Regression 0.646102 NaN Zero Imputation
49 Logistic Regression 0.209116 NaN Zero Imputation
50 Logistic Regression 0.292204 NaN Zero Imputation
51 Logistic Regression NaN 1.0 Zero Imputation
52 Logistic Regression NaN None Zero Imputation
53 Logistic Regression NaN False Zero Imputation
54 Logistic Regression NaN True Zero Imputation
55 Logistic Regression NaN 1 Zero Imputation
56 Logistic Regression NaN None Zero Imputation
57 Logistic Regression NaN 100 Zero Imputation
58 Logistic Regression NaN auto Zero Imputation
59 Logistic Regression NaN None Zero Imputation
60 Logistic Regression NaN l2 Zero Imputation
61 Logistic Regression NaN None Zero Imputation
62 Logistic Regression NaN lbfgs Zero Imputation
63 Logistic Regression NaN 0.0001 Zero Imputation
64 Logistic Regression NaN 0 Zero Imputation
65 Logistic Regression NaN False Zero Imputation
66 Logistic Regression 0.345627 NaN Zero Imputation
67 Logistic Regression 0.063359 NaN Zero Imputation
68 Logistic Regression 0.846939 NaN Zero Imputation
69 Logistic Regression 0.117898 NaN Zero Imputation
70 Logistic Regression 0.658314 NaN Zero Imputation
71 Logistic Regression 0.252976 NaN Zero Imputation
72 Logistic Regression 0.316628 NaN Zero Imputation
73 Logistic Regression NaN 1.0 Zero Imputation
74 Logistic Regression NaN None Zero Imputation
75 Logistic Regression NaN False Zero Imputation
76 Logistic Regression NaN True Zero Imputation
77 Logistic Regression NaN 1 Zero Imputation
78 Logistic Regression NaN None Zero Imputation
79 Logistic Regression NaN 100 Zero Imputation
80 Logistic Regression NaN auto Zero Imputation
81 Logistic Regression NaN None Zero Imputation
82 Logistic Regression NaN l2 Zero Imputation
83 Logistic Regression NaN None Zero Imputation
84 Logistic Regression NaN lbfgs Zero Imputation
85 Logistic Regression NaN 0.0001 Zero Imputation
86 Logistic Regression NaN 0 Zero Imputation
87 Logistic Regression NaN False Zero Imputation
88 Logistic Regression 0.380664 NaN Zero Imputation
89 Logistic Regression 0.068687 NaN Zero Imputation
90 Logistic Regression 0.787037 NaN Zero Imputation
91 Logistic Regression 0.126347 NaN Zero Imputation
92 Logistic Regression 0.643175 NaN Zero Imputation
93 Logistic Regression 0.249581 NaN Zero Imputation
94 Logistic Regression 0.286349 NaN Zero Imputation
95 Logistic Regression NaN 1.0 Zero Imputation
96 Logistic Regression NaN None Zero Imputation
97 Logistic Regression NaN False Zero Imputation
98 Logistic Regression NaN True Zero Imputation
99 Logistic Regression NaN 1 Zero Imputation
100 Logistic Regression NaN None Zero Imputation
101 Logistic Regression NaN 100 Zero Imputation
102 Logistic Regression NaN auto Zero Imputation
103 Logistic Regression NaN None Zero Imputation
104 Logistic Regression NaN l2 Zero Imputation
105 Logistic Regression NaN None Zero Imputation
106 Logistic Regression NaN lbfgs Zero Imputation
107 Logistic Regression NaN 0.0001 Zero Imputation
108 Logistic Regression NaN 0 Zero Imputation
109 Logistic Regression NaN False Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
95 Hybrid Sampling (SMOTEENN)
96 Hybrid Sampling (SMOTEENN)
97 Hybrid Sampling (SMOTEENN)
98 Hybrid Sampling (SMOTEENN)
99 Hybrid Sampling (SMOTEENN)
100 Hybrid Sampling (SMOTEENN)
101 Hybrid Sampling (SMOTEENN)
102 Hybrid Sampling (SMOTEENN)
103 Hybrid Sampling (SMOTEENN)
104 Hybrid Sampling (SMOTEENN)
105 Hybrid Sampling (SMOTEENN)
106 Hybrid Sampling (SMOTEENN)
107 Hybrid Sampling (SMOTEENN)
108 Hybrid Sampling (SMOTEENN)
109 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Logistic Regression_Hybrid Sampling (SMOTEENN)...
1 Logistic Regression_Hybrid Sampling (SMOTEENN)...
2 Logistic Regression_Hybrid Sampling (SMOTEENN)...
3 Logistic Regression_Hybrid Sampling (SMOTEENN)...
4 Logistic Regression_Hybrid Sampling (SMOTEENN)...
5 Logistic Regression_Hybrid Sampling (SMOTEENN)...
6 Logistic Regression_Hybrid Sampling (SMOTEENN)...
7 Logistic Regression_Hybrid Sampling (SMOTEENN)...
8 Logistic Regression_Hybrid Sampling (SMOTEENN)...
9 Logistic Regression_Hybrid Sampling (SMOTEENN)...
10 Logistic Regression_Hybrid Sampling (SMOTEENN)...
11 Logistic Regression_Hybrid Sampling (SMOTEENN)...
12 Logistic Regression_Hybrid Sampling (SMOTEENN)...
13 Logistic Regression_Hybrid Sampling (SMOTEENN)...
14 Logistic Regression_Hybrid Sampling (SMOTEENN)...
15 Logistic Regression_Hybrid Sampling (SMOTEENN)...
16 Logistic Regression_Hybrid Sampling (SMOTEENN)...
17 Logistic Regression_Hybrid Sampling (SMOTEENN)...
18 Logistic Regression_Hybrid Sampling (SMOTEENN)...
19 Logistic Regression_Hybrid Sampling (SMOTEENN)...
20 Logistic Regression_Hybrid Sampling (SMOTEENN)...
21 Logistic Regression_Hybrid Sampling (SMOTEENN)...
22 Logistic Regression_Hybrid Sampling (SMOTEENN)...
23 Logistic Regression_Hybrid Sampling (SMOTEENN)...
24 Logistic Regression_Hybrid Sampling (SMOTEENN)...
25 Logistic Regression_Hybrid Sampling (SMOTEENN)...
26 Logistic Regression_Hybrid Sampling (SMOTEENN)...
27 Logistic Regression_Hybrid Sampling (SMOTEENN)...
28 Logistic Regression_Hybrid Sampling (SMOTEENN)...
29 Logistic Regression_Hybrid Sampling (SMOTEENN)...
30 Logistic Regression_Hybrid Sampling (SMOTEENN)...
31 Logistic Regression_Hybrid Sampling (SMOTEENN)...
32 Logistic Regression_Hybrid Sampling (SMOTEENN)...
33 Logistic Regression_Hybrid Sampling (SMOTEENN)...
34 Logistic Regression_Hybrid Sampling (SMOTEENN)...
35 Logistic Regression_Hybrid Sampling (SMOTEENN)...
36 Logistic Regression_Hybrid Sampling (SMOTEENN)...
37 Logistic Regression_Hybrid Sampling (SMOTEENN)...
38 Logistic Regression_Hybrid Sampling (SMOTEENN)...
39 Logistic Regression_Hybrid Sampling (SMOTEENN)...
40 Logistic Regression_Hybrid Sampling (SMOTEENN)...
41 Logistic Regression_Hybrid Sampling (SMOTEENN)...
42 Logistic Regression_Hybrid Sampling (SMOTEENN)...
43 Logistic Regression_Hybrid Sampling (SMOTEENN)...
44 Logistic Regression_Hybrid Sampling (SMOTEENN)...
45 Logistic Regression_Hybrid Sampling (SMOTEENN)...
46 Logistic Regression_Hybrid Sampling (SMOTEENN)...
47 Logistic Regression_Hybrid Sampling (SMOTEENN)...
48 Logistic Regression_Hybrid Sampling (SMOTEENN)...
49 Logistic Regression_Hybrid Sampling (SMOTEENN)...
50 Logistic Regression_Hybrid Sampling (SMOTEENN)...
51 Logistic Regression_Hybrid Sampling (SMOTEENN)...
52 Logistic Regression_Hybrid Sampling (SMOTEENN)...
53 Logistic Regression_Hybrid Sampling (SMOTEENN)...
54 Logistic Regression_Hybrid Sampling (SMOTEENN)...
55 Logistic Regression_Hybrid Sampling (SMOTEENN)...
56 Logistic Regression_Hybrid Sampling (SMOTEENN)...
57 Logistic Regression_Hybrid Sampling (SMOTEENN)...
58 Logistic Regression_Hybrid Sampling (SMOTEENN)...
59 Logistic Regression_Hybrid Sampling (SMOTEENN)...
60 Logistic Regression_Hybrid Sampling (SMOTEENN)...
61 Logistic Regression_Hybrid Sampling (SMOTEENN)...
62 Logistic Regression_Hybrid Sampling (SMOTEENN)...
63 Logistic Regression_Hybrid Sampling (SMOTEENN)...
64 Logistic Regression_Hybrid Sampling (SMOTEENN)...
65 Logistic Regression_Hybrid Sampling (SMOTEENN)...
66 Logistic Regression_Hybrid Sampling (SMOTEENN)...
67 Logistic Regression_Hybrid Sampling (SMOTEENN)...
68 Logistic Regression_Hybrid Sampling (SMOTEENN)...
69 Logistic Regression_Hybrid Sampling (SMOTEENN)...
70 Logistic Regression_Hybrid Sampling (SMOTEENN)...
71 Logistic Regression_Hybrid Sampling (SMOTEENN)...
72 Logistic Regression_Hybrid Sampling (SMOTEENN)...
73 Logistic Regression_Hybrid Sampling (SMOTEENN)...
74 Logistic Regression_Hybrid Sampling (SMOTEENN)...
75 Logistic Regression_Hybrid Sampling (SMOTEENN)...
76 Logistic Regression_Hybrid Sampling (SMOTEENN)...
77 Logistic Regression_Hybrid Sampling (SMOTEENN)...
78 Logistic Regression_Hybrid Sampling (SMOTEENN)...
79 Logistic Regression_Hybrid Sampling (SMOTEENN)...
80 Logistic Regression_Hybrid Sampling (SMOTEENN)...
81 Logistic Regression_Hybrid Sampling (SMOTEENN)...
82 Logistic Regression_Hybrid Sampling (SMOTEENN)...
83 Logistic Regression_Hybrid Sampling (SMOTEENN)...
84 Logistic Regression_Hybrid Sampling (SMOTEENN)...
85 Logistic Regression_Hybrid Sampling (SMOTEENN)...
86 Logistic Regression_Hybrid Sampling (SMOTEENN)...
87 Logistic Regression_Hybrid Sampling (SMOTEENN)...
88 Logistic Regression_Hybrid Sampling (SMOTEENN)...
89 Logistic Regression_Hybrid Sampling (SMOTEENN)...
90 Logistic Regression_Hybrid Sampling (SMOTEENN)...
91 Logistic Regression_Hybrid Sampling (SMOTEENN)...
92 Logistic Regression_Hybrid Sampling (SMOTEENN)...
93 Logistic Regression_Hybrid Sampling (SMOTEENN)...
94 Logistic Regression_Hybrid Sampling (SMOTEENN)...
95 Logistic Regression_Hybrid Sampling (SMOTEENN)...
96 Logistic Regression_Hybrid Sampling (SMOTEENN)...
97 Logistic Regression_Hybrid Sampling (SMOTEENN)...
98 Logistic Regression_Hybrid Sampling (SMOTEENN)...
99 Logistic Regression_Hybrid Sampling (SMOTEENN)...
100 Logistic Regression_Hybrid Sampling (SMOTEENN)...
101 Logistic Regression_Hybrid Sampling (SMOTEENN)...
102 Logistic Regression_Hybrid Sampling (SMOTEENN)...
103 Logistic Regression_Hybrid Sampling (SMOTEENN)...
104 Logistic Regression_Hybrid Sampling (SMOTEENN)...
105 Logistic Regression_Hybrid Sampling (SMOTEENN)...
106 Logistic Regression_Hybrid Sampling (SMOTEENN)...
107 Logistic Regression_Hybrid Sampling (SMOTEENN)...
108 Logistic Regression_Hybrid Sampling (SMOTEENN)...
109 Logistic Regression_Hybrid Sampling (SMOTEENN)... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Logistic Regression 0.366869 NaN Mean Imputation
1 Logistic Regression 0.078569 NaN Mean Imputation
2 Logistic Regression 0.852321 NaN Mean Imputation
3 Logistic Regression 0.143875 NaN Mean Imputation
4 Logistic Regression 0.638731 NaN Mean Imputation
5 Logistic Regression 0.265836 NaN Mean Imputation
6 Logistic Regression 0.277463 NaN Mean Imputation
7 Logistic Regression NaN 1.0 Mean Imputation
8 Logistic Regression NaN None Mean Imputation
9 Logistic Regression NaN False Mean Imputation
10 Logistic Regression NaN True Mean Imputation
11 Logistic Regression NaN 1 Mean Imputation
12 Logistic Regression NaN None Mean Imputation
13 Logistic Regression NaN 100 Mean Imputation
14 Logistic Regression NaN auto Mean Imputation
15 Logistic Regression NaN None Mean Imputation
16 Logistic Regression NaN l2 Mean Imputation
17 Logistic Regression NaN None Mean Imputation
18 Logistic Regression NaN lbfgs Mean Imputation
19 Logistic Regression NaN 0.0001 Mean Imputation
20 Logistic Regression NaN 0 Mean Imputation
21 Logistic Regression NaN False Mean Imputation
22 Logistic Regression 0.366605 NaN Mean Imputation
23 Logistic Regression 0.054119 NaN Mean Imputation
24 Logistic Regression 0.829268 NaN Mean Imputation
25 Logistic Regression 0.101606 NaN Mean Imputation
26 Logistic Regression 0.621086 NaN Mean Imputation
27 Logistic Regression 0.253003 NaN Mean Imputation
28 Logistic Regression 0.242172 NaN Mean Imputation
29 Logistic Regression NaN 1.0 Mean Imputation
30 Logistic Regression NaN None Mean Imputation
31 Logistic Regression NaN False Mean Imputation
32 Logistic Regression NaN True Mean Imputation
33 Logistic Regression NaN 1 Mean Imputation
34 Logistic Regression NaN None Mean Imputation
35 Logistic Regression NaN 100 Mean Imputation
36 Logistic Regression NaN auto Mean Imputation
37 Logistic Regression NaN None Mean Imputation
38 Logistic Regression NaN l2 Mean Imputation
39 Logistic Regression NaN None Mean Imputation
40 Logistic Regression NaN lbfgs Mean Imputation
41 Logistic Regression NaN 0.0001 Mean Imputation
42 Logistic Regression NaN 0 Mean Imputation
43 Logistic Regression NaN False Mean Imputation
44 Logistic Regression 0.357651 NaN Mean Imputation
45 Logistic Regression 0.061526 NaN Mean Imputation
46 Logistic Regression 0.844920 NaN Mean Imputation
47 Logistic Regression 0.114701 NaN Mean Imputation
48 Logistic Regression 0.629918 NaN Mean Imputation
49 Logistic Regression 0.263764 NaN Mean Imputation
50 Logistic Regression 0.259837 NaN Mean Imputation
51 Logistic Regression NaN 1.0 Mean Imputation
52 Logistic Regression NaN None Mean Imputation
53 Logistic Regression NaN False Mean Imputation
54 Logistic Regression NaN True Mean Imputation
55 Logistic Regression NaN 1 Mean Imputation
56 Logistic Regression NaN None Mean Imputation
57 Logistic Regression NaN 100 Mean Imputation
58 Logistic Regression NaN auto Mean Imputation
59 Logistic Regression NaN None Mean Imputation
60 Logistic Regression NaN l2 Mean Imputation
61 Logistic Regression NaN None Mean Imputation
62 Logistic Regression NaN lbfgs Mean Imputation
63 Logistic Regression NaN 0.0001 Mean Imputation
64 Logistic Regression NaN 0 Mean Imputation
65 Logistic Regression NaN False Mean Imputation
66 Logistic Regression 0.362750 NaN Mean Imputation
67 Logistic Regression 0.063604 NaN Mean Imputation
68 Logistic Regression 0.826531 NaN Mean Imputation
69 Logistic Regression 0.118119 NaN Mean Imputation
70 Logistic Regression 0.605573 NaN Mean Imputation
71 Logistic Regression 0.217251 NaN Mean Imputation
72 Logistic Regression 0.211147 NaN Mean Imputation
73 Logistic Regression NaN 1.0 Mean Imputation
74 Logistic Regression NaN None Mean Imputation
75 Logistic Regression NaN False Mean Imputation
76 Logistic Regression NaN True Mean Imputation
77 Logistic Regression NaN 1 Mean Imputation
78 Logistic Regression NaN None Mean Imputation
79 Logistic Regression NaN 100 Mean Imputation
80 Logistic Regression NaN auto Mean Imputation
81 Logistic Regression NaN None Mean Imputation
82 Logistic Regression NaN l2 Mean Imputation
83 Logistic Regression NaN None Mean Imputation
84 Logistic Regression NaN lbfgs Mean Imputation
85 Logistic Regression NaN 0.0001 Mean Imputation
86 Logistic Regression NaN 0 Mean Imputation
87 Logistic Regression NaN False Mean Imputation
88 Logistic Regression 0.388830 NaN Mean Imputation
89 Logistic Regression 0.070962 NaN Mean Imputation
90 Logistic Regression 0.805556 NaN Mean Imputation
91 Logistic Regression 0.130435 NaN Mean Imputation
92 Logistic Regression 0.612938 NaN Mean Imputation
93 Logistic Regression 0.218606 NaN Mean Imputation
94 Logistic Regression 0.225875 NaN Mean Imputation
95 Logistic Regression NaN 1.0 Mean Imputation
96 Logistic Regression NaN None Mean Imputation
97 Logistic Regression NaN False Mean Imputation
98 Logistic Regression NaN True Mean Imputation
99 Logistic Regression NaN 1 Mean Imputation
100 Logistic Regression NaN None Mean Imputation
101 Logistic Regression NaN 100 Mean Imputation
102 Logistic Regression NaN auto Mean Imputation
103 Logistic Regression NaN None Mean Imputation
104 Logistic Regression NaN l2 Mean Imputation
105 Logistic Regression NaN None Mean Imputation
106 Logistic Regression NaN lbfgs Mean Imputation
107 Logistic Regression NaN 0.0001 Mean Imputation
108 Logistic Regression NaN 0 Mean Imputation
109 Logistic Regression NaN False Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
95 Hybrid Sampling (SMOTEENN)
96 Hybrid Sampling (SMOTEENN)
97 Hybrid Sampling (SMOTEENN)
98 Hybrid Sampling (SMOTEENN)
99 Hybrid Sampling (SMOTEENN)
100 Hybrid Sampling (SMOTEENN)
101 Hybrid Sampling (SMOTEENN)
102 Hybrid Sampling (SMOTEENN)
103 Hybrid Sampling (SMOTEENN)
104 Hybrid Sampling (SMOTEENN)
105 Hybrid Sampling (SMOTEENN)
106 Hybrid Sampling (SMOTEENN)
107 Hybrid Sampling (SMOTEENN)
108 Hybrid Sampling (SMOTEENN)
109 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Logistic Regression_Hybrid Sampling (SMOTEENN)...
1 Logistic Regression_Hybrid Sampling (SMOTEENN)...
2 Logistic Regression_Hybrid Sampling (SMOTEENN)...
3 Logistic Regression_Hybrid Sampling (SMOTEENN)...
4 Logistic Regression_Hybrid Sampling (SMOTEENN)...
5 Logistic Regression_Hybrid Sampling (SMOTEENN)...
6 Logistic Regression_Hybrid Sampling (SMOTEENN)...
7 Logistic Regression_Hybrid Sampling (SMOTEENN)...
8 Logistic Regression_Hybrid Sampling (SMOTEENN)...
9 Logistic Regression_Hybrid Sampling (SMOTEENN)...
10 Logistic Regression_Hybrid Sampling (SMOTEENN)...
11 Logistic Regression_Hybrid Sampling (SMOTEENN)...
12 Logistic Regression_Hybrid Sampling (SMOTEENN)...
13 Logistic Regression_Hybrid Sampling (SMOTEENN)...
14 Logistic Regression_Hybrid Sampling (SMOTEENN)...
15 Logistic Regression_Hybrid Sampling (SMOTEENN)...
16 Logistic Regression_Hybrid Sampling (SMOTEENN)...
17 Logistic Regression_Hybrid Sampling (SMOTEENN)...
18 Logistic Regression_Hybrid Sampling (SMOTEENN)...
19 Logistic Regression_Hybrid Sampling (SMOTEENN)...
20 Logistic Regression_Hybrid Sampling (SMOTEENN)...
21 Logistic Regression_Hybrid Sampling (SMOTEENN)...
22 Logistic Regression_Hybrid Sampling (SMOTEENN)...
23 Logistic Regression_Hybrid Sampling (SMOTEENN)...
24 Logistic Regression_Hybrid Sampling (SMOTEENN)...
25 Logistic Regression_Hybrid Sampling (SMOTEENN)...
26 Logistic Regression_Hybrid Sampling (SMOTEENN)...
27 Logistic Regression_Hybrid Sampling (SMOTEENN)...
28 Logistic Regression_Hybrid Sampling (SMOTEENN)...
29 Logistic Regression_Hybrid Sampling (SMOTEENN)...
30 Logistic Regression_Hybrid Sampling (SMOTEENN)...
31 Logistic Regression_Hybrid Sampling (SMOTEENN)...
32 Logistic Regression_Hybrid Sampling (SMOTEENN)...
33 Logistic Regression_Hybrid Sampling (SMOTEENN)...
34 Logistic Regression_Hybrid Sampling (SMOTEENN)...
35 Logistic Regression_Hybrid Sampling (SMOTEENN)...
36 Logistic Regression_Hybrid Sampling (SMOTEENN)...
37 Logistic Regression_Hybrid Sampling (SMOTEENN)...
38 Logistic Regression_Hybrid Sampling (SMOTEENN)...
39 Logistic Regression_Hybrid Sampling (SMOTEENN)...
40 Logistic Regression_Hybrid Sampling (SMOTEENN)...
41 Logistic Regression_Hybrid Sampling (SMOTEENN)...
42 Logistic Regression_Hybrid Sampling (SMOTEENN)...
43 Logistic Regression_Hybrid Sampling (SMOTEENN)...
44 Logistic Regression_Hybrid Sampling (SMOTEENN)...
45 Logistic Regression_Hybrid Sampling (SMOTEENN)...
46 Logistic Regression_Hybrid Sampling (SMOTEENN)...
47 Logistic Regression_Hybrid Sampling (SMOTEENN)...
48 Logistic Regression_Hybrid Sampling (SMOTEENN)...
49 Logistic Regression_Hybrid Sampling (SMOTEENN)...
50 Logistic Regression_Hybrid Sampling (SMOTEENN)...
51 Logistic Regression_Hybrid Sampling (SMOTEENN)...
52 Logistic Regression_Hybrid Sampling (SMOTEENN)...
53 Logistic Regression_Hybrid Sampling (SMOTEENN)...
54 Logistic Regression_Hybrid Sampling (SMOTEENN)...
55 Logistic Regression_Hybrid Sampling (SMOTEENN)...
56 Logistic Regression_Hybrid Sampling (SMOTEENN)...
57 Logistic Regression_Hybrid Sampling (SMOTEENN)...
58 Logistic Regression_Hybrid Sampling (SMOTEENN)...
59 Logistic Regression_Hybrid Sampling (SMOTEENN)...
60 Logistic Regression_Hybrid Sampling (SMOTEENN)...
61 Logistic Regression_Hybrid Sampling (SMOTEENN)...
62 Logistic Regression_Hybrid Sampling (SMOTEENN)...
63 Logistic Regression_Hybrid Sampling (SMOTEENN)...
64 Logistic Regression_Hybrid Sampling (SMOTEENN)...
65 Logistic Regression_Hybrid Sampling (SMOTEENN)...
66 Logistic Regression_Hybrid Sampling (SMOTEENN)...
67 Logistic Regression_Hybrid Sampling (SMOTEENN)...
68 Logistic Regression_Hybrid Sampling (SMOTEENN)...
69 Logistic Regression_Hybrid Sampling (SMOTEENN)...
70 Logistic Regression_Hybrid Sampling (SMOTEENN)...
71 Logistic Regression_Hybrid Sampling (SMOTEENN)...
72 Logistic Regression_Hybrid Sampling (SMOTEENN)...
73 Logistic Regression_Hybrid Sampling (SMOTEENN)...
74 Logistic Regression_Hybrid Sampling (SMOTEENN)...
75 Logistic Regression_Hybrid Sampling (SMOTEENN)...
76 Logistic Regression_Hybrid Sampling (SMOTEENN)...
77 Logistic Regression_Hybrid Sampling (SMOTEENN)...
78 Logistic Regression_Hybrid Sampling (SMOTEENN)...
79 Logistic Regression_Hybrid Sampling (SMOTEENN)...
80 Logistic Regression_Hybrid Sampling (SMOTEENN)...
81 Logistic Regression_Hybrid Sampling (SMOTEENN)...
82 Logistic Regression_Hybrid Sampling (SMOTEENN)...
83 Logistic Regression_Hybrid Sampling (SMOTEENN)...
84 Logistic Regression_Hybrid Sampling (SMOTEENN)...
85 Logistic Regression_Hybrid Sampling (SMOTEENN)...
86 Logistic Regression_Hybrid Sampling (SMOTEENN)...
87 Logistic Regression_Hybrid Sampling (SMOTEENN)...
88 Logistic Regression_Hybrid Sampling (SMOTEENN)...
89 Logistic Regression_Hybrid Sampling (SMOTEENN)...
90 Logistic Regression_Hybrid Sampling (SMOTEENN)...
91 Logistic Regression_Hybrid Sampling (SMOTEENN)...
92 Logistic Regression_Hybrid Sampling (SMOTEENN)...
93 Logistic Regression_Hybrid Sampling (SMOTEENN)...
94 Logistic Regression_Hybrid Sampling (SMOTEENN)...
95 Logistic Regression_Hybrid Sampling (SMOTEENN)...
96 Logistic Regression_Hybrid Sampling (SMOTEENN)...
97 Logistic Regression_Hybrid Sampling (SMOTEENN)...
98 Logistic Regression_Hybrid Sampling (SMOTEENN)...
99 Logistic Regression_Hybrid Sampling (SMOTEENN)...
100 Logistic Regression_Hybrid Sampling (SMOTEENN)...
101 Logistic Regression_Hybrid Sampling (SMOTEENN)...
102 Logistic Regression_Hybrid Sampling (SMOTEENN)...
103 Logistic Regression_Hybrid Sampling (SMOTEENN)...
104 Logistic Regression_Hybrid Sampling (SMOTEENN)...
105 Logistic Regression_Hybrid Sampling (SMOTEENN)...
106 Logistic Regression_Hybrid Sampling (SMOTEENN)...
107 Logistic Regression_Hybrid Sampling (SMOTEENN)...
108 Logistic Regression_Hybrid Sampling (SMOTEENN)...
109 Logistic Regression_Hybrid Sampling (SMOTEENN)... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Logistic Regression 0.411114 NaN Median Imputation
1 Logistic Regression 0.082323 NaN Median Imputation
2 Logistic Regression 0.831224 NaN Median Imputation
3 Logistic Regression 0.149810 NaN Median Imputation
4 Logistic Regression 0.633390 NaN Median Imputation
5 Logistic Regression 0.238683 NaN Median Imputation
6 Logistic Regression 0.266779 NaN Median Imputation
7 Logistic Regression NaN 1.0 Median Imputation
8 Logistic Regression NaN None Median Imputation
9 Logistic Regression NaN False Median Imputation
10 Logistic Regression NaN True Median Imputation
11 Logistic Regression NaN 1 Median Imputation
12 Logistic Regression NaN None Median Imputation
13 Logistic Regression NaN 100 Median Imputation
14 Logistic Regression NaN auto Median Imputation
15 Logistic Regression NaN None Median Imputation
16 Logistic Regression NaN l2 Median Imputation
17 Logistic Regression NaN None Median Imputation
18 Logistic Regression NaN lbfgs Median Imputation
19 Logistic Regression NaN 0.0001 Median Imputation
20 Logistic Regression NaN 0 Median Imputation
21 Logistic Regression NaN False Median Imputation
22 Logistic Regression 0.384251 NaN Median Imputation
23 Logistic Regression 0.057050 NaN Median Imputation
24 Logistic Regression 0.853659 NaN Median Imputation
25 Logistic Regression 0.106952 NaN Median Imputation
26 Logistic Regression 0.642791 NaN Median Imputation
27 Logistic Regression 0.247553 NaN Median Imputation
28 Logistic Regression 0.285582 NaN Median Imputation
29 Logistic Regression NaN 1.0 Median Imputation
30 Logistic Regression NaN None Median Imputation
31 Logistic Regression NaN False Median Imputation
32 Logistic Regression NaN True Median Imputation
33 Logistic Regression NaN 1 Median Imputation
34 Logistic Regression NaN None Median Imputation
35 Logistic Regression NaN 100 Median Imputation
36 Logistic Regression NaN auto Median Imputation
37 Logistic Regression NaN None Median Imputation
38 Logistic Regression NaN l2 Median Imputation
39 Logistic Regression NaN None Median Imputation
40 Logistic Regression NaN lbfgs Median Imputation
41 Logistic Regression NaN 0.0001 Median Imputation
42 Logistic Regression NaN 0 Median Imputation
43 Logistic Regression NaN False Median Imputation
44 Logistic Regression 0.373189 NaN Median Imputation
45 Logistic Regression 0.061926 NaN Median Imputation
46 Logistic Regression 0.828877 NaN Median Imputation
47 Logistic Regression 0.115242 NaN Median Imputation
48 Logistic Regression 0.648276 NaN Median Imputation
49 Logistic Regression 0.271847 NaN Median Imputation
50 Logistic Regression 0.296553 NaN Median Imputation
51 Logistic Regression NaN 1.0 Median Imputation
52 Logistic Regression NaN None Median Imputation
53 Logistic Regression NaN False Median Imputation
54 Logistic Regression NaN True Median Imputation
55 Logistic Regression NaN 1 Median Imputation
56 Logistic Regression NaN None Median Imputation
57 Logistic Regression NaN 100 Median Imputation
58 Logistic Regression NaN auto Median Imputation
59 Logistic Regression NaN None Median Imputation
60 Logistic Regression NaN l2 Median Imputation
61 Logistic Regression NaN None Median Imputation
62 Logistic Regression NaN lbfgs Median Imputation
63 Logistic Regression NaN 0.0001 Median Imputation
64 Logistic Regression NaN 0 Median Imputation
65 Logistic Regression NaN False Median Imputation
66 Logistic Regression 0.372234 NaN Median Imputation
67 Logistic Regression 0.065209 NaN Median Imputation
68 Logistic Regression 0.836735 NaN Median Imputation
69 Logistic Regression 0.120989 NaN Median Imputation
70 Logistic Regression 0.651721 NaN Median Imputation
71 Logistic Regression 0.253787 NaN Median Imputation
72 Logistic Regression 0.303442 NaN Median Imputation
73 Logistic Regression NaN 1.0 Median Imputation
74 Logistic Regression NaN None Median Imputation
75 Logistic Regression NaN False Median Imputation
76 Logistic Regression NaN True Median Imputation
77 Logistic Regression NaN 1 Median Imputation
78 Logistic Regression NaN None Median Imputation
79 Logistic Regression NaN 100 Median Imputation
80 Logistic Regression NaN auto Median Imputation
81 Logistic Regression NaN None Median Imputation
82 Logistic Regression NaN l2 Median Imputation
83 Logistic Regression NaN None Median Imputation
84 Logistic Regression NaN lbfgs Median Imputation
85 Logistic Regression NaN 0.0001 Median Imputation
86 Logistic Regression NaN 0 Median Imputation
87 Logistic Regression NaN False Median Imputation
88 Logistic Regression 0.397524 NaN Median Imputation
89 Logistic Regression 0.072637 NaN Median Imputation
90 Logistic Regression 0.814815 NaN Median Imputation
91 Logistic Regression 0.133384 NaN Median Imputation
92 Logistic Regression 0.632641 NaN Median Imputation
93 Logistic Regression 0.254252 NaN Median Imputation
94 Logistic Regression 0.265283 NaN Median Imputation
95 Logistic Regression NaN 1.0 Median Imputation
96 Logistic Regression NaN None Median Imputation
97 Logistic Regression NaN False Median Imputation
98 Logistic Regression NaN True Median Imputation
99 Logistic Regression NaN 1 Median Imputation
100 Logistic Regression NaN None Median Imputation
101 Logistic Regression NaN 100 Median Imputation
102 Logistic Regression NaN auto Median Imputation
103 Logistic Regression NaN None Median Imputation
104 Logistic Regression NaN l2 Median Imputation
105 Logistic Regression NaN None Median Imputation
106 Logistic Regression NaN lbfgs Median Imputation
107 Logistic Regression NaN 0.0001 Median Imputation
108 Logistic Regression NaN 0 Median Imputation
109 Logistic Regression NaN False Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
95 Hybrid Sampling (SMOTEENN)
96 Hybrid Sampling (SMOTEENN)
97 Hybrid Sampling (SMOTEENN)
98 Hybrid Sampling (SMOTEENN)
99 Hybrid Sampling (SMOTEENN)
100 Hybrid Sampling (SMOTEENN)
101 Hybrid Sampling (SMOTEENN)
102 Hybrid Sampling (SMOTEENN)
103 Hybrid Sampling (SMOTEENN)
104 Hybrid Sampling (SMOTEENN)
105 Hybrid Sampling (SMOTEENN)
106 Hybrid Sampling (SMOTEENN)
107 Hybrid Sampling (SMOTEENN)
108 Hybrid Sampling (SMOTEENN)
109 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Logistic Regression_Hybrid Sampling (SMOTEENN)...
1 Logistic Regression_Hybrid Sampling (SMOTEENN)...
2 Logistic Regression_Hybrid Sampling (SMOTEENN)...
3 Logistic Regression_Hybrid Sampling (SMOTEENN)...
4 Logistic Regression_Hybrid Sampling (SMOTEENN)...
5 Logistic Regression_Hybrid Sampling (SMOTEENN)...
6 Logistic Regression_Hybrid Sampling (SMOTEENN)...
7 Logistic Regression_Hybrid Sampling (SMOTEENN)...
8 Logistic Regression_Hybrid Sampling (SMOTEENN)...
9 Logistic Regression_Hybrid Sampling (SMOTEENN)...
10 Logistic Regression_Hybrid Sampling (SMOTEENN)...
11 Logistic Regression_Hybrid Sampling (SMOTEENN)...
12 Logistic Regression_Hybrid Sampling (SMOTEENN)...
13 Logistic Regression_Hybrid Sampling (SMOTEENN)...
14 Logistic Regression_Hybrid Sampling (SMOTEENN)...
15 Logistic Regression_Hybrid Sampling (SMOTEENN)...
16 Logistic Regression_Hybrid Sampling (SMOTEENN)...
17 Logistic Regression_Hybrid Sampling (SMOTEENN)...
18 Logistic Regression_Hybrid Sampling (SMOTEENN)...
19 Logistic Regression_Hybrid Sampling (SMOTEENN)...
20 Logistic Regression_Hybrid Sampling (SMOTEENN)...
21 Logistic Regression_Hybrid Sampling (SMOTEENN)...
22 Logistic Regression_Hybrid Sampling (SMOTEENN)...
23 Logistic Regression_Hybrid Sampling (SMOTEENN)...
24 Logistic Regression_Hybrid Sampling (SMOTEENN)...
25 Logistic Regression_Hybrid Sampling (SMOTEENN)...
26 Logistic Regression_Hybrid Sampling (SMOTEENN)...
27 Logistic Regression_Hybrid Sampling (SMOTEENN)...
28 Logistic Regression_Hybrid Sampling (SMOTEENN)...
29 Logistic Regression_Hybrid Sampling (SMOTEENN)...
30 Logistic Regression_Hybrid Sampling (SMOTEENN)...
31 Logistic Regression_Hybrid Sampling (SMOTEENN)...
32 Logistic Regression_Hybrid Sampling (SMOTEENN)...
33 Logistic Regression_Hybrid Sampling (SMOTEENN)...
34 Logistic Regression_Hybrid Sampling (SMOTEENN)...
35 Logistic Regression_Hybrid Sampling (SMOTEENN)...
36 Logistic Regression_Hybrid Sampling (SMOTEENN)...
37 Logistic Regression_Hybrid Sampling (SMOTEENN)...
38 Logistic Regression_Hybrid Sampling (SMOTEENN)...
39 Logistic Regression_Hybrid Sampling (SMOTEENN)...
40 Logistic Regression_Hybrid Sampling (SMOTEENN)...
41 Logistic Regression_Hybrid Sampling (SMOTEENN)...
42 Logistic Regression_Hybrid Sampling (SMOTEENN)...
43 Logistic Regression_Hybrid Sampling (SMOTEENN)...
44 Logistic Regression_Hybrid Sampling (SMOTEENN)...
45 Logistic Regression_Hybrid Sampling (SMOTEENN)...
46 Logistic Regression_Hybrid Sampling (SMOTEENN)...
47 Logistic Regression_Hybrid Sampling (SMOTEENN)...
48 Logistic Regression_Hybrid Sampling (SMOTEENN)...
49 Logistic Regression_Hybrid Sampling (SMOTEENN)...
50 Logistic Regression_Hybrid Sampling (SMOTEENN)...
51 Logistic Regression_Hybrid Sampling (SMOTEENN)...
52 Logistic Regression_Hybrid Sampling (SMOTEENN)...
53 Logistic Regression_Hybrid Sampling (SMOTEENN)...
54 Logistic Regression_Hybrid Sampling (SMOTEENN)...
55 Logistic Regression_Hybrid Sampling (SMOTEENN)...
56 Logistic Regression_Hybrid Sampling (SMOTEENN)...
57 Logistic Regression_Hybrid Sampling (SMOTEENN)...
58 Logistic Regression_Hybrid Sampling (SMOTEENN)...
59 Logistic Regression_Hybrid Sampling (SMOTEENN)...
60 Logistic Regression_Hybrid Sampling (SMOTEENN)...
61 Logistic Regression_Hybrid Sampling (SMOTEENN)...
62 Logistic Regression_Hybrid Sampling (SMOTEENN)...
63 Logistic Regression_Hybrid Sampling (SMOTEENN)...
64 Logistic Regression_Hybrid Sampling (SMOTEENN)...
65 Logistic Regression_Hybrid Sampling (SMOTEENN)...
66 Logistic Regression_Hybrid Sampling (SMOTEENN)...
67 Logistic Regression_Hybrid Sampling (SMOTEENN)...
68 Logistic Regression_Hybrid Sampling (SMOTEENN)...
69 Logistic Regression_Hybrid Sampling (SMOTEENN)...
70 Logistic Regression_Hybrid Sampling (SMOTEENN)...
71 Logistic Regression_Hybrid Sampling (SMOTEENN)...
72 Logistic Regression_Hybrid Sampling (SMOTEENN)...
73 Logistic Regression_Hybrid Sampling (SMOTEENN)...
74 Logistic Regression_Hybrid Sampling (SMOTEENN)...
75 Logistic Regression_Hybrid Sampling (SMOTEENN)...
76 Logistic Regression_Hybrid Sampling (SMOTEENN)...
77 Logistic Regression_Hybrid Sampling (SMOTEENN)...
78 Logistic Regression_Hybrid Sampling (SMOTEENN)...
79 Logistic Regression_Hybrid Sampling (SMOTEENN)...
80 Logistic Regression_Hybrid Sampling (SMOTEENN)...
81 Logistic Regression_Hybrid Sampling (SMOTEENN)...
82 Logistic Regression_Hybrid Sampling (SMOTEENN)...
83 Logistic Regression_Hybrid Sampling (SMOTEENN)...
84 Logistic Regression_Hybrid Sampling (SMOTEENN)...
85 Logistic Regression_Hybrid Sampling (SMOTEENN)...
86 Logistic Regression_Hybrid Sampling (SMOTEENN)...
87 Logistic Regression_Hybrid Sampling (SMOTEENN)...
88 Logistic Regression_Hybrid Sampling (SMOTEENN)...
89 Logistic Regression_Hybrid Sampling (SMOTEENN)...
90 Logistic Regression_Hybrid Sampling (SMOTEENN)...
91 Logistic Regression_Hybrid Sampling (SMOTEENN)...
92 Logistic Regression_Hybrid Sampling (SMOTEENN)...
93 Logistic Regression_Hybrid Sampling (SMOTEENN)...
94 Logistic Regression_Hybrid Sampling (SMOTEENN)...
95 Logistic Regression_Hybrid Sampling (SMOTEENN)...
96 Logistic Regression_Hybrid Sampling (SMOTEENN)...
97 Logistic Regression_Hybrid Sampling (SMOTEENN)...
98 Logistic Regression_Hybrid Sampling (SMOTEENN)...
99 Logistic Regression_Hybrid Sampling (SMOTEENN)...
100 Logistic Regression_Hybrid Sampling (SMOTEENN)...
101 Logistic Regression_Hybrid Sampling (SMOTEENN)...
102 Logistic Regression_Hybrid Sampling (SMOTEENN)...
103 Logistic Regression_Hybrid Sampling (SMOTEENN)...
104 Logistic Regression_Hybrid Sampling (SMOTEENN)...
105 Logistic Regression_Hybrid Sampling (SMOTEENN)...
106 Logistic Regression_Hybrid Sampling (SMOTEENN)...
107 Logistic Regression_Hybrid Sampling (SMOTEENN)...
108 Logistic Regression_Hybrid Sampling (SMOTEENN)...
109 Logistic Regression_Hybrid Sampling (SMOTEENN)... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Logistic Regression 0.472742 NaN Zero Imputation
1 Logistic Regression 0.083923 NaN Zero Imputation
2 Logistic Regression 0.751055 NaN Zero Imputation
3 Logistic Regression 0.150975 NaN Zero Imputation
4 Logistic Regression 0.626554 NaN Zero Imputation
5 Logistic Regression 0.247362 NaN Zero Imputation
6 Logistic Regression 0.253109 NaN Zero Imputation
7 Logistic Regression NaN 1.0 Zero Imputation
8 Logistic Regression NaN None Zero Imputation
9 Logistic Regression NaN False Zero Imputation
10 Logistic Regression NaN True Zero Imputation
11 Logistic Regression NaN 1 Zero Imputation
12 Logistic Regression NaN None Zero Imputation
13 Logistic Regression NaN 100 Zero Imputation
14 Logistic Regression NaN auto Zero Imputation
15 Logistic Regression NaN None Zero Imputation
16 Logistic Regression NaN l2 Zero Imputation
17 Logistic Regression NaN None Zero Imputation
18 Logistic Regression NaN lbfgs Zero Imputation
19 Logistic Regression NaN 0.0001 Zero Imputation
20 Logistic Regression NaN 0 Zero Imputation
21 Logistic Regression NaN False Zero Imputation
22 Logistic Regression 0.470635 NaN Zero Imputation
23 Logistic Regression 0.057950 NaN Zero Imputation
24 Logistic Regression 0.737805 NaN Zero Imputation
25 Logistic Regression 0.107460 NaN Zero Imputation
26 Logistic Regression 0.643828 NaN Zero Imputation
27 Logistic Regression 0.232872 NaN Zero Imputation
28 Logistic Regression 0.287656 NaN Zero Imputation
29 Logistic Regression NaN 1.0 Zero Imputation
30 Logistic Regression NaN None Zero Imputation
31 Logistic Regression NaN False Zero Imputation
32 Logistic Regression NaN True Zero Imputation
33 Logistic Regression NaN 1 Zero Imputation
34 Logistic Regression NaN None Zero Imputation
35 Logistic Regression NaN 100 Zero Imputation
36 Logistic Regression NaN auto Zero Imputation
37 Logistic Regression NaN None Zero Imputation
38 Logistic Regression NaN l2 Zero Imputation
39 Logistic Regression NaN None Zero Imputation
40 Logistic Regression NaN lbfgs Zero Imputation
41 Logistic Regression NaN 0.0001 Zero Imputation
42 Logistic Regression NaN 0 Zero Imputation
43 Logistic Regression NaN False Zero Imputation
44 Logistic Regression 0.468791 NaN Zero Imputation
45 Logistic Regression 0.061361 NaN Zero Imputation
46 Logistic Regression 0.684492 NaN Zero Imputation
47 Logistic Regression 0.112626 NaN Zero Imputation
48 Logistic Regression 0.640194 NaN Zero Imputation
49 Logistic Regression 0.209657 NaN Zero Imputation
50 Logistic Regression 0.280389 NaN Zero Imputation
51 Logistic Regression NaN 1.0 Zero Imputation
52 Logistic Regression NaN None Zero Imputation
53 Logistic Regression NaN False Zero Imputation
54 Logistic Regression NaN True Zero Imputation
55 Logistic Regression NaN 1 Zero Imputation
56 Logistic Regression NaN None Zero Imputation
57 Logistic Regression NaN 100 Zero Imputation
58 Logistic Regression NaN auto Zero Imputation
59 Logistic Regression NaN None Zero Imputation
60 Logistic Regression NaN l2 Zero Imputation
61 Logistic Regression NaN None Zero Imputation
62 Logistic Regression NaN lbfgs Zero Imputation
63 Logistic Regression NaN 0.0001 Zero Imputation
64 Logistic Regression NaN 0 Zero Imputation
65 Logistic Regression NaN False Zero Imputation
66 Logistic Regression 0.450738 NaN Zero Imputation
67 Logistic Regression 0.067735 NaN Zero Imputation
68 Logistic Regression 0.755102 NaN Zero Imputation
69 Logistic Regression 0.124318 NaN Zero Imputation
70 Logistic Regression 0.660009 NaN Zero Imputation
71 Logistic Regression 0.246412 NaN Zero Imputation
72 Logistic Regression 0.320018 NaN Zero Imputation
73 Logistic Regression NaN 1.0 Zero Imputation
74 Logistic Regression NaN None Zero Imputation
75 Logistic Regression NaN False Zero Imputation
76 Logistic Regression NaN True Zero Imputation
77 Logistic Regression NaN 1 Zero Imputation
78 Logistic Regression NaN None Zero Imputation
79 Logistic Regression NaN 100 Zero Imputation
80 Logistic Regression NaN auto Zero Imputation
81 Logistic Regression NaN None Zero Imputation
82 Logistic Regression NaN l2 Zero Imputation
83 Logistic Regression NaN None Zero Imputation
84 Logistic Regression NaN lbfgs Zero Imputation
85 Logistic Regression NaN 0.0001 Zero Imputation
86 Logistic Regression NaN 0 Zero Imputation
87 Logistic Regression NaN False Zero Imputation
88 Logistic Regression 0.494731 NaN Zero Imputation
89 Logistic Regression 0.075773 NaN Zero Imputation
90 Logistic Regression 0.703704 NaN Zero Imputation
91 Logistic Regression 0.136814 NaN Zero Imputation
92 Logistic Regression 0.647318 NaN Zero Imputation
93 Logistic Regression 0.237746 NaN Zero Imputation
94 Logistic Regression 0.294636 NaN Zero Imputation
95 Logistic Regression NaN 1.0 Zero Imputation
96 Logistic Regression NaN None Zero Imputation
97 Logistic Regression NaN False Zero Imputation
98 Logistic Regression NaN True Zero Imputation
99 Logistic Regression NaN 1 Zero Imputation
100 Logistic Regression NaN None Zero Imputation
101 Logistic Regression NaN 100 Zero Imputation
102 Logistic Regression NaN auto Zero Imputation
103 Logistic Regression NaN None Zero Imputation
104 Logistic Regression NaN l2 Zero Imputation
105 Logistic Regression NaN None Zero Imputation
106 Logistic Regression NaN lbfgs Zero Imputation
107 Logistic Regression NaN 0.0001 Zero Imputation
108 Logistic Regression NaN 0 Zero Imputation
109 Logistic Regression NaN False Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
95 Hybrid Sampling (SMOTETomek)
96 Hybrid Sampling (SMOTETomek)
97 Hybrid Sampling (SMOTETomek)
98 Hybrid Sampling (SMOTETomek)
99 Hybrid Sampling (SMOTETomek)
100 Hybrid Sampling (SMOTETomek)
101 Hybrid Sampling (SMOTETomek)
102 Hybrid Sampling (SMOTETomek)
103 Hybrid Sampling (SMOTETomek)
104 Hybrid Sampling (SMOTETomek)
105 Hybrid Sampling (SMOTETomek)
106 Hybrid Sampling (SMOTETomek)
107 Hybrid Sampling (SMOTETomek)
108 Hybrid Sampling (SMOTETomek)
109 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Logistic Regression_Hybrid Sampling (SMOTETome...
1 Logistic Regression_Hybrid Sampling (SMOTETome...
2 Logistic Regression_Hybrid Sampling (SMOTETome...
3 Logistic Regression_Hybrid Sampling (SMOTETome...
4 Logistic Regression_Hybrid Sampling (SMOTETome...
5 Logistic Regression_Hybrid Sampling (SMOTETome...
6 Logistic Regression_Hybrid Sampling (SMOTETome...
7 Logistic Regression_Hybrid Sampling (SMOTETome...
8 Logistic Regression_Hybrid Sampling (SMOTETome...
9 Logistic Regression_Hybrid Sampling (SMOTETome...
10 Logistic Regression_Hybrid Sampling (SMOTETome...
11 Logistic Regression_Hybrid Sampling (SMOTETome...
12 Logistic Regression_Hybrid Sampling (SMOTETome...
13 Logistic Regression_Hybrid Sampling (SMOTETome...
14 Logistic Regression_Hybrid Sampling (SMOTETome...
15 Logistic Regression_Hybrid Sampling (SMOTETome...
16 Logistic Regression_Hybrid Sampling (SMOTETome...
17 Logistic Regression_Hybrid Sampling (SMOTETome...
18 Logistic Regression_Hybrid Sampling (SMOTETome...
19 Logistic Regression_Hybrid Sampling (SMOTETome...
20 Logistic Regression_Hybrid Sampling (SMOTETome...
21 Logistic Regression_Hybrid Sampling (SMOTETome...
22 Logistic Regression_Hybrid Sampling (SMOTETome...
23 Logistic Regression_Hybrid Sampling (SMOTETome...
24 Logistic Regression_Hybrid Sampling (SMOTETome...
25 Logistic Regression_Hybrid Sampling (SMOTETome...
26 Logistic Regression_Hybrid Sampling (SMOTETome...
27 Logistic Regression_Hybrid Sampling (SMOTETome...
28 Logistic Regression_Hybrid Sampling (SMOTETome...
29 Logistic Regression_Hybrid Sampling (SMOTETome...
30 Logistic Regression_Hybrid Sampling (SMOTETome...
31 Logistic Regression_Hybrid Sampling (SMOTETome...
32 Logistic Regression_Hybrid Sampling (SMOTETome...
33 Logistic Regression_Hybrid Sampling (SMOTETome...
34 Logistic Regression_Hybrid Sampling (SMOTETome...
35 Logistic Regression_Hybrid Sampling (SMOTETome...
36 Logistic Regression_Hybrid Sampling (SMOTETome...
37 Logistic Regression_Hybrid Sampling (SMOTETome...
38 Logistic Regression_Hybrid Sampling (SMOTETome...
39 Logistic Regression_Hybrid Sampling (SMOTETome...
40 Logistic Regression_Hybrid Sampling (SMOTETome...
41 Logistic Regression_Hybrid Sampling (SMOTETome...
42 Logistic Regression_Hybrid Sampling (SMOTETome...
43 Logistic Regression_Hybrid Sampling (SMOTETome...
44 Logistic Regression_Hybrid Sampling (SMOTETome...
45 Logistic Regression_Hybrid Sampling (SMOTETome...
46 Logistic Regression_Hybrid Sampling (SMOTETome...
47 Logistic Regression_Hybrid Sampling (SMOTETome...
48 Logistic Regression_Hybrid Sampling (SMOTETome...
49 Logistic Regression_Hybrid Sampling (SMOTETome...
50 Logistic Regression_Hybrid Sampling (SMOTETome...
51 Logistic Regression_Hybrid Sampling (SMOTETome...
52 Logistic Regression_Hybrid Sampling (SMOTETome...
53 Logistic Regression_Hybrid Sampling (SMOTETome...
54 Logistic Regression_Hybrid Sampling (SMOTETome...
55 Logistic Regression_Hybrid Sampling (SMOTETome...
56 Logistic Regression_Hybrid Sampling (SMOTETome...
57 Logistic Regression_Hybrid Sampling (SMOTETome...
58 Logistic Regression_Hybrid Sampling (SMOTETome...
59 Logistic Regression_Hybrid Sampling (SMOTETome...
60 Logistic Regression_Hybrid Sampling (SMOTETome...
61 Logistic Regression_Hybrid Sampling (SMOTETome...
62 Logistic Regression_Hybrid Sampling (SMOTETome...
63 Logistic Regression_Hybrid Sampling (SMOTETome...
64 Logistic Regression_Hybrid Sampling (SMOTETome...
65 Logistic Regression_Hybrid Sampling (SMOTETome...
66 Logistic Regression_Hybrid Sampling (SMOTETome...
67 Logistic Regression_Hybrid Sampling (SMOTETome...
68 Logistic Regression_Hybrid Sampling (SMOTETome...
69 Logistic Regression_Hybrid Sampling (SMOTETome...
70 Logistic Regression_Hybrid Sampling (SMOTETome...
71 Logistic Regression_Hybrid Sampling (SMOTETome...
72 Logistic Regression_Hybrid Sampling (SMOTETome...
73 Logistic Regression_Hybrid Sampling (SMOTETome...
74 Logistic Regression_Hybrid Sampling (SMOTETome...
75 Logistic Regression_Hybrid Sampling (SMOTETome...
76 Logistic Regression_Hybrid Sampling (SMOTETome...
77 Logistic Regression_Hybrid Sampling (SMOTETome...
78 Logistic Regression_Hybrid Sampling (SMOTETome...
79 Logistic Regression_Hybrid Sampling (SMOTETome...
80 Logistic Regression_Hybrid Sampling (SMOTETome...
81 Logistic Regression_Hybrid Sampling (SMOTETome...
82 Logistic Regression_Hybrid Sampling (SMOTETome...
83 Logistic Regression_Hybrid Sampling (SMOTETome...
84 Logistic Regression_Hybrid Sampling (SMOTETome...
85 Logistic Regression_Hybrid Sampling (SMOTETome...
86 Logistic Regression_Hybrid Sampling (SMOTETome...
87 Logistic Regression_Hybrid Sampling (SMOTETome...
88 Logistic Regression_Hybrid Sampling (SMOTETome...
89 Logistic Regression_Hybrid Sampling (SMOTETome...
90 Logistic Regression_Hybrid Sampling (SMOTETome...
91 Logistic Regression_Hybrid Sampling (SMOTETome...
92 Logistic Regression_Hybrid Sampling (SMOTETome...
93 Logistic Regression_Hybrid Sampling (SMOTETome...
94 Logistic Regression_Hybrid Sampling (SMOTETome...
95 Logistic Regression_Hybrid Sampling (SMOTETome...
96 Logistic Regression_Hybrid Sampling (SMOTETome...
97 Logistic Regression_Hybrid Sampling (SMOTETome...
98 Logistic Regression_Hybrid Sampling (SMOTETome...
99 Logistic Regression_Hybrid Sampling (SMOTETome...
100 Logistic Regression_Hybrid Sampling (SMOTETome...
101 Logistic Regression_Hybrid Sampling (SMOTETome...
102 Logistic Regression_Hybrid Sampling (SMOTETome...
103 Logistic Regression_Hybrid Sampling (SMOTETome...
104 Logistic Regression_Hybrid Sampling (SMOTETome...
105 Logistic Regression_Hybrid Sampling (SMOTETome...
106 Logistic Regression_Hybrid Sampling (SMOTETome...
107 Logistic Regression_Hybrid Sampling (SMOTETome...
108 Logistic Regression_Hybrid Sampling (SMOTETome...
109 Logistic Regression_Hybrid Sampling (SMOTETome... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Logistic Regression 0.449302 NaN Mean Imputation
1 Logistic Regression 0.083183 NaN Mean Imputation
2 Logistic Regression 0.780591 NaN Mean Imputation
3 Logistic Regression 0.150345 NaN Mean Imputation
4 Logistic Regression 0.623066 NaN Mean Imputation
5 Logistic Regression 0.249615 NaN Mean Imputation
6 Logistic Regression 0.246133 NaN Mean Imputation
7 Logistic Regression NaN 1.0 Mean Imputation
8 Logistic Regression NaN None Mean Imputation
9 Logistic Regression NaN False Mean Imputation
10 Logistic Regression NaN True Mean Imputation
11 Logistic Regression NaN 1 Mean Imputation
12 Logistic Regression NaN None Mean Imputation
13 Logistic Regression NaN 100 Mean Imputation
14 Logistic Regression NaN auto Mean Imputation
15 Logistic Regression NaN None Mean Imputation
16 Logistic Regression NaN l2 Mean Imputation
17 Logistic Regression NaN None Mean Imputation
18 Logistic Regression NaN lbfgs Mean Imputation
19 Logistic Regression NaN 0.0001 Mean Imputation
20 Logistic Regression NaN 0 Mean Imputation
21 Logistic Regression NaN False Mean Imputation
22 Logistic Regression 0.476165 NaN Mean Imputation
23 Logistic Regression 0.056824 NaN Mean Imputation
24 Logistic Regression 0.713415 NaN Mean Imputation
25 Logistic Regression 0.105263 NaN Mean Imputation
26 Logistic Regression 0.621783 NaN Mean Imputation
27 Logistic Regression 0.227297 NaN Mean Imputation
28 Logistic Regression 0.243565 NaN Mean Imputation
29 Logistic Regression NaN 1.0 Mean Imputation
30 Logistic Regression NaN None Mean Imputation
31 Logistic Regression NaN False Mean Imputation
32 Logistic Regression NaN True Mean Imputation
33 Logistic Regression NaN 1 Mean Imputation
34 Logistic Regression NaN None Mean Imputation
35 Logistic Regression NaN 100 Mean Imputation
36 Logistic Regression NaN auto Mean Imputation
37 Logistic Regression NaN None Mean Imputation
38 Logistic Regression NaN l2 Mean Imputation
39 Logistic Regression NaN None Mean Imputation
40 Logistic Regression NaN lbfgs Mean Imputation
41 Logistic Regression NaN 0.0001 Mean Imputation
42 Logistic Regression NaN 0 Mean Imputation
43 Logistic Regression NaN False Mean Imputation
44 Logistic Regression 0.466948 NaN Mean Imputation
45 Logistic Regression 0.062827 NaN Mean Imputation
46 Logistic Regression 0.705882 NaN Mean Imputation
47 Logistic Regression 0.115385 NaN Mean Imputation
48 Logistic Regression 0.628059 NaN Mean Imputation
49 Logistic Regression 0.218875 NaN Mean Imputation
50 Logistic Regression 0.256119 NaN Mean Imputation
51 Logistic Regression NaN 1.0 Mean Imputation
52 Logistic Regression NaN None Mean Imputation
53 Logistic Regression NaN False Mean Imputation
54 Logistic Regression NaN True Mean Imputation
55 Logistic Regression NaN 1 Mean Imputation
56 Logistic Regression NaN None Mean Imputation
57 Logistic Regression NaN 100 Mean Imputation
58 Logistic Regression NaN auto Mean Imputation
59 Logistic Regression NaN None Mean Imputation
60 Logistic Regression NaN l2 Mean Imputation
61 Logistic Regression NaN None Mean Imputation
62 Logistic Regression NaN lbfgs Mean Imputation
63 Logistic Regression NaN 0.0001 Mean Imputation
64 Logistic Regression NaN 0 Mean Imputation
65 Logistic Regression NaN False Mean Imputation
66 Logistic Regression 0.512118 NaN Mean Imputation
67 Logistic Regression 0.071872 NaN Mean Imputation
68 Logistic Regression 0.709184 NaN Mean Imputation
69 Logistic Regression 0.130516 NaN Mean Imputation
70 Logistic Regression 0.634318 NaN Mean Imputation
71 Logistic Regression 0.226638 NaN Mean Imputation
72 Logistic Regression 0.268635 NaN Mean Imputation
73 Logistic Regression NaN 1.0 Mean Imputation
74 Logistic Regression NaN None Mean Imputation
75 Logistic Regression NaN False Mean Imputation
76 Logistic Regression NaN True Mean Imputation
77 Logistic Regression NaN 1 Mean Imputation
78 Logistic Regression NaN None Mean Imputation
79 Logistic Regression NaN 100 Mean Imputation
80 Logistic Regression NaN auto Mean Imputation
81 Logistic Regression NaN None Mean Imputation
82 Logistic Regression NaN l2 Mean Imputation
83 Logistic Regression NaN None Mean Imputation
84 Logistic Regression NaN lbfgs Mean Imputation
85 Logistic Regression NaN 0.0001 Mean Imputation
86 Logistic Regression NaN 0 Mean Imputation
87 Logistic Regression NaN False Mean Imputation
88 Logistic Regression 0.458114 NaN Mean Imputation
89 Logistic Regression 0.075219 NaN Mean Imputation
90 Logistic Regression 0.754630 NaN Mean Imputation
91 Logistic Regression 0.136802 NaN Mean Imputation
92 Logistic Regression 0.632065 NaN Mean Imputation
93 Logistic Regression 0.232583 NaN Mean Imputation
94 Logistic Regression 0.264131 NaN Mean Imputation
95 Logistic Regression NaN 1.0 Mean Imputation
96 Logistic Regression NaN None Mean Imputation
97 Logistic Regression NaN False Mean Imputation
98 Logistic Regression NaN True Mean Imputation
99 Logistic Regression NaN 1 Mean Imputation
100 Logistic Regression NaN None Mean Imputation
101 Logistic Regression NaN 100 Mean Imputation
102 Logistic Regression NaN auto Mean Imputation
103 Logistic Regression NaN None Mean Imputation
104 Logistic Regression NaN l2 Mean Imputation
105 Logistic Regression NaN None Mean Imputation
106 Logistic Regression NaN lbfgs Mean Imputation
107 Logistic Regression NaN 0.0001 Mean Imputation
108 Logistic Regression NaN 0 Mean Imputation
109 Logistic Regression NaN False Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
95 Hybrid Sampling (SMOTETomek)
96 Hybrid Sampling (SMOTETomek)
97 Hybrid Sampling (SMOTETomek)
98 Hybrid Sampling (SMOTETomek)
99 Hybrid Sampling (SMOTETomek)
100 Hybrid Sampling (SMOTETomek)
101 Hybrid Sampling (SMOTETomek)
102 Hybrid Sampling (SMOTETomek)
103 Hybrid Sampling (SMOTETomek)
104 Hybrid Sampling (SMOTETomek)
105 Hybrid Sampling (SMOTETomek)
106 Hybrid Sampling (SMOTETomek)
107 Hybrid Sampling (SMOTETomek)
108 Hybrid Sampling (SMOTETomek)
109 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Logistic Regression_Hybrid Sampling (SMOTETome...
1 Logistic Regression_Hybrid Sampling (SMOTETome...
2 Logistic Regression_Hybrid Sampling (SMOTETome...
3 Logistic Regression_Hybrid Sampling (SMOTETome...
4 Logistic Regression_Hybrid Sampling (SMOTETome...
5 Logistic Regression_Hybrid Sampling (SMOTETome...
6 Logistic Regression_Hybrid Sampling (SMOTETome...
7 Logistic Regression_Hybrid Sampling (SMOTETome...
8 Logistic Regression_Hybrid Sampling (SMOTETome...
9 Logistic Regression_Hybrid Sampling (SMOTETome...
10 Logistic Regression_Hybrid Sampling (SMOTETome...
11 Logistic Regression_Hybrid Sampling (SMOTETome...
12 Logistic Regression_Hybrid Sampling (SMOTETome...
13 Logistic Regression_Hybrid Sampling (SMOTETome...
14 Logistic Regression_Hybrid Sampling (SMOTETome...
15 Logistic Regression_Hybrid Sampling (SMOTETome...
16 Logistic Regression_Hybrid Sampling (SMOTETome...
17 Logistic Regression_Hybrid Sampling (SMOTETome...
18 Logistic Regression_Hybrid Sampling (SMOTETome...
19 Logistic Regression_Hybrid Sampling (SMOTETome...
20 Logistic Regression_Hybrid Sampling (SMOTETome...
21 Logistic Regression_Hybrid Sampling (SMOTETome...
22 Logistic Regression_Hybrid Sampling (SMOTETome...
23 Logistic Regression_Hybrid Sampling (SMOTETome...
24 Logistic Regression_Hybrid Sampling (SMOTETome...
25 Logistic Regression_Hybrid Sampling (SMOTETome...
26 Logistic Regression_Hybrid Sampling (SMOTETome...
27 Logistic Regression_Hybrid Sampling (SMOTETome...
28 Logistic Regression_Hybrid Sampling (SMOTETome...
29 Logistic Regression_Hybrid Sampling (SMOTETome...
30 Logistic Regression_Hybrid Sampling (SMOTETome...
31 Logistic Regression_Hybrid Sampling (SMOTETome...
32 Logistic Regression_Hybrid Sampling (SMOTETome...
33 Logistic Regression_Hybrid Sampling (SMOTETome...
34 Logistic Regression_Hybrid Sampling (SMOTETome...
35 Logistic Regression_Hybrid Sampling (SMOTETome...
36 Logistic Regression_Hybrid Sampling (SMOTETome...
37 Logistic Regression_Hybrid Sampling (SMOTETome...
38 Logistic Regression_Hybrid Sampling (SMOTETome...
39 Logistic Regression_Hybrid Sampling (SMOTETome...
40 Logistic Regression_Hybrid Sampling (SMOTETome...
41 Logistic Regression_Hybrid Sampling (SMOTETome...
42 Logistic Regression_Hybrid Sampling (SMOTETome...
43 Logistic Regression_Hybrid Sampling (SMOTETome...
44 Logistic Regression_Hybrid Sampling (SMOTETome...
45 Logistic Regression_Hybrid Sampling (SMOTETome...
46 Logistic Regression_Hybrid Sampling (SMOTETome...
47 Logistic Regression_Hybrid Sampling (SMOTETome...
48 Logistic Regression_Hybrid Sampling (SMOTETome...
49 Logistic Regression_Hybrid Sampling (SMOTETome...
50 Logistic Regression_Hybrid Sampling (SMOTETome...
51 Logistic Regression_Hybrid Sampling (SMOTETome...
52 Logistic Regression_Hybrid Sampling (SMOTETome...
53 Logistic Regression_Hybrid Sampling (SMOTETome...
54 Logistic Regression_Hybrid Sampling (SMOTETome...
55 Logistic Regression_Hybrid Sampling (SMOTETome...
56 Logistic Regression_Hybrid Sampling (SMOTETome...
57 Logistic Regression_Hybrid Sampling (SMOTETome...
58 Logistic Regression_Hybrid Sampling (SMOTETome...
59 Logistic Regression_Hybrid Sampling (SMOTETome...
60 Logistic Regression_Hybrid Sampling (SMOTETome...
61 Logistic Regression_Hybrid Sampling (SMOTETome...
62 Logistic Regression_Hybrid Sampling (SMOTETome...
63 Logistic Regression_Hybrid Sampling (SMOTETome...
64 Logistic Regression_Hybrid Sampling (SMOTETome...
65 Logistic Regression_Hybrid Sampling (SMOTETome...
66 Logistic Regression_Hybrid Sampling (SMOTETome...
67 Logistic Regression_Hybrid Sampling (SMOTETome...
68 Logistic Regression_Hybrid Sampling (SMOTETome...
69 Logistic Regression_Hybrid Sampling (SMOTETome...
70 Logistic Regression_Hybrid Sampling (SMOTETome...
71 Logistic Regression_Hybrid Sampling (SMOTETome...
72 Logistic Regression_Hybrid Sampling (SMOTETome...
73 Logistic Regression_Hybrid Sampling (SMOTETome...
74 Logistic Regression_Hybrid Sampling (SMOTETome...
75 Logistic Regression_Hybrid Sampling (SMOTETome...
76 Logistic Regression_Hybrid Sampling (SMOTETome...
77 Logistic Regression_Hybrid Sampling (SMOTETome...
78 Logistic Regression_Hybrid Sampling (SMOTETome...
79 Logistic Regression_Hybrid Sampling (SMOTETome...
80 Logistic Regression_Hybrid Sampling (SMOTETome...
81 Logistic Regression_Hybrid Sampling (SMOTETome...
82 Logistic Regression_Hybrid Sampling (SMOTETome...
83 Logistic Regression_Hybrid Sampling (SMOTETome...
84 Logistic Regression_Hybrid Sampling (SMOTETome...
85 Logistic Regression_Hybrid Sampling (SMOTETome...
86 Logistic Regression_Hybrid Sampling (SMOTETome...
87 Logistic Regression_Hybrid Sampling (SMOTETome...
88 Logistic Regression_Hybrid Sampling (SMOTETome...
89 Logistic Regression_Hybrid Sampling (SMOTETome...
90 Logistic Regression_Hybrid Sampling (SMOTETome...
91 Logistic Regression_Hybrid Sampling (SMOTETome...
92 Logistic Regression_Hybrid Sampling (SMOTETome...
93 Logistic Regression_Hybrid Sampling (SMOTETome...
94 Logistic Regression_Hybrid Sampling (SMOTETome...
95 Logistic Regression_Hybrid Sampling (SMOTETome...
96 Logistic Regression_Hybrid Sampling (SMOTETome...
97 Logistic Regression_Hybrid Sampling (SMOTETome...
98 Logistic Regression_Hybrid Sampling (SMOTETome...
99 Logistic Regression_Hybrid Sampling (SMOTETome...
100 Logistic Regression_Hybrid Sampling (SMOTETome...
101 Logistic Regression_Hybrid Sampling (SMOTETome...
102 Logistic Regression_Hybrid Sampling (SMOTETome...
103 Logistic Regression_Hybrid Sampling (SMOTETome...
104 Logistic Regression_Hybrid Sampling (SMOTETome...
105 Logistic Regression_Hybrid Sampling (SMOTETome...
106 Logistic Regression_Hybrid Sampling (SMOTETome...
107 Logistic Regression_Hybrid Sampling (SMOTETome...
108 Logistic Regression_Hybrid Sampling (SMOTETome...
109 Logistic Regression_Hybrid Sampling (SMOTETome... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Logistic Regression 0.508559 NaN Median Imputation
1 Logistic Regression 0.087177 NaN Median Imputation
2 Logistic Regression 0.725738 NaN Median Imputation
3 Logistic Regression 0.155656 NaN Median Imputation
4 Logistic Regression 0.622130 NaN Median Imputation
5 Logistic Regression 0.222654 NaN Median Imputation
6 Logistic Regression 0.244260 NaN Median Imputation
7 Logistic Regression NaN 1.0 Median Imputation
8 Logistic Regression NaN None Median Imputation
9 Logistic Regression NaN False Median Imputation
10 Logistic Regression NaN True Median Imputation
11 Logistic Regression NaN 1 Median Imputation
12 Logistic Regression NaN None Median Imputation
13 Logistic Regression NaN 100 Median Imputation
14 Logistic Regression NaN auto Median Imputation
15 Logistic Regression NaN None Median Imputation
16 Logistic Regression NaN l2 Median Imputation
17 Logistic Regression NaN None Median Imputation
18 Logistic Regression NaN lbfgs Median Imputation
19 Logistic Regression NaN 0.0001 Median Imputation
20 Logistic Regression NaN 0 Median Imputation
21 Logistic Regression NaN False Median Imputation
22 Logistic Regression 0.518041 NaN Median Imputation
23 Logistic Regression 0.059259 NaN Median Imputation
24 Logistic Regression 0.682927 NaN Median Imputation
25 Logistic Regression 0.109056 NaN Median Imputation
26 Logistic Regression 0.635181 NaN Median Imputation
27 Logistic Regression 0.222528 NaN Median Imputation
28 Logistic Regression 0.270362 NaN Median Imputation
29 Logistic Regression NaN 1.0 Median Imputation
30 Logistic Regression NaN None Median Imputation
31 Logistic Regression NaN False Median Imputation
32 Logistic Regression NaN True Median Imputation
33 Logistic Regression NaN 1 Median Imputation
34 Logistic Regression NaN None Median Imputation
35 Logistic Regression NaN 100 Median Imputation
36 Logistic Regression NaN auto Median Imputation
37 Logistic Regression NaN None Median Imputation
38 Logistic Regression NaN l2 Median Imputation
39 Logistic Regression NaN None Median Imputation
40 Logistic Regression NaN lbfgs Median Imputation
41 Logistic Regression NaN 0.0001 Median Imputation
42 Logistic Regression NaN 0 Median Imputation
43 Logistic Regression NaN False Median Imputation
44 Logistic Regression 0.516460 NaN Median Imputation
45 Logistic Regression 0.067192 NaN Median Imputation
46 Logistic Regression 0.684492 NaN Median Imputation
47 Logistic Regression 0.122371 NaN Median Imputation
48 Logistic Regression 0.636101 NaN Median Imputation
49 Logistic Regression 0.209483 NaN Median Imputation
50 Logistic Regression 0.272203 NaN Median Imputation
51 Logistic Regression NaN 1.0 Median Imputation
52 Logistic Regression NaN None Median Imputation
53 Logistic Regression NaN False Median Imputation
54 Logistic Regression NaN True Median Imputation
55 Logistic Regression NaN 1 Median Imputation
56 Logistic Regression NaN None Median Imputation
57 Logistic Regression NaN 100 Median Imputation
58 Logistic Regression NaN auto Median Imputation
59 Logistic Regression NaN None Median Imputation
60 Logistic Regression NaN l2 Median Imputation
61 Logistic Regression NaN None Median Imputation
62 Logistic Regression NaN lbfgs Median Imputation
63 Logistic Regression NaN 0.0001 Median Imputation
64 Logistic Regression NaN 0 Median Imputation
65 Logistic Regression NaN False Median Imputation
66 Logistic Regression 0.527924 NaN Median Imputation
67 Logistic Regression 0.076433 NaN Median Imputation
68 Logistic Regression 0.734694 NaN Median Imputation
69 Logistic Regression 0.138462 NaN Median Imputation
70 Logistic Regression 0.661445 NaN Median Imputation
71 Logistic Regression 0.266763 NaN Median Imputation
72 Logistic Regression 0.322890 NaN Median Imputation
73 Logistic Regression NaN 1.0 Median Imputation
74 Logistic Regression NaN None Median Imputation
75 Logistic Regression NaN False Median Imputation
76 Logistic Regression NaN True Median Imputation
77 Logistic Regression NaN 1 Median Imputation
78 Logistic Regression NaN None Median Imputation
79 Logistic Regression NaN 100 Median Imputation
80 Logistic Regression NaN auto Median Imputation
81 Logistic Regression NaN None Median Imputation
82 Logistic Regression NaN l2 Median Imputation
83 Logistic Regression NaN None Median Imputation
84 Logistic Regression NaN lbfgs Median Imputation
85 Logistic Regression NaN 0.0001 Median Imputation
86 Logistic Regression NaN 0 Median Imputation
87 Logistic Regression NaN False Median Imputation
88 Logistic Regression 0.557165 NaN Median Imputation
89 Logistic Regression 0.079748 NaN Median Imputation
90 Logistic Regression 0.643519 NaN Median Imputation
91 Logistic Regression 0.141909 NaN Median Imputation
92 Logistic Regression 0.627634 NaN Median Imputation
93 Logistic Regression 0.211080 NaN Median Imputation
94 Logistic Regression 0.255267 NaN Median Imputation
95 Logistic Regression NaN 1.0 Median Imputation
96 Logistic Regression NaN None Median Imputation
97 Logistic Regression NaN False Median Imputation
98 Logistic Regression NaN True Median Imputation
99 Logistic Regression NaN 1 Median Imputation
100 Logistic Regression NaN None Median Imputation
101 Logistic Regression NaN 100 Median Imputation
102 Logistic Regression NaN auto Median Imputation
103 Logistic Regression NaN None Median Imputation
104 Logistic Regression NaN l2 Median Imputation
105 Logistic Regression NaN None Median Imputation
106 Logistic Regression NaN lbfgs Median Imputation
107 Logistic Regression NaN 0.0001 Median Imputation
108 Logistic Regression NaN 0 Median Imputation
109 Logistic Regression NaN False Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
95 Hybrid Sampling (SMOTETomek)
96 Hybrid Sampling (SMOTETomek)
97 Hybrid Sampling (SMOTETomek)
98 Hybrid Sampling (SMOTETomek)
99 Hybrid Sampling (SMOTETomek)
100 Hybrid Sampling (SMOTETomek)
101 Hybrid Sampling (SMOTETomek)
102 Hybrid Sampling (SMOTETomek)
103 Hybrid Sampling (SMOTETomek)
104 Hybrid Sampling (SMOTETomek)
105 Hybrid Sampling (SMOTETomek)
106 Hybrid Sampling (SMOTETomek)
107 Hybrid Sampling (SMOTETomek)
108 Hybrid Sampling (SMOTETomek)
109 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Logistic Regression_Hybrid Sampling (SMOTETome...
1 Logistic Regression_Hybrid Sampling (SMOTETome...
2 Logistic Regression_Hybrid Sampling (SMOTETome...
3 Logistic Regression_Hybrid Sampling (SMOTETome...
4 Logistic Regression_Hybrid Sampling (SMOTETome...
5 Logistic Regression_Hybrid Sampling (SMOTETome...
6 Logistic Regression_Hybrid Sampling (SMOTETome...
7 Logistic Regression_Hybrid Sampling (SMOTETome...
8 Logistic Regression_Hybrid Sampling (SMOTETome...
9 Logistic Regression_Hybrid Sampling (SMOTETome...
10 Logistic Regression_Hybrid Sampling (SMOTETome...
11 Logistic Regression_Hybrid Sampling (SMOTETome...
12 Logistic Regression_Hybrid Sampling (SMOTETome...
13 Logistic Regression_Hybrid Sampling (SMOTETome...
14 Logistic Regression_Hybrid Sampling (SMOTETome...
15 Logistic Regression_Hybrid Sampling (SMOTETome...
16 Logistic Regression_Hybrid Sampling (SMOTETome...
17 Logistic Regression_Hybrid Sampling (SMOTETome...
18 Logistic Regression_Hybrid Sampling (SMOTETome...
19 Logistic Regression_Hybrid Sampling (SMOTETome...
20 Logistic Regression_Hybrid Sampling (SMOTETome...
21 Logistic Regression_Hybrid Sampling (SMOTETome...
22 Logistic Regression_Hybrid Sampling (SMOTETome...
23 Logistic Regression_Hybrid Sampling (SMOTETome...
24 Logistic Regression_Hybrid Sampling (SMOTETome...
25 Logistic Regression_Hybrid Sampling (SMOTETome...
26 Logistic Regression_Hybrid Sampling (SMOTETome...
27 Logistic Regression_Hybrid Sampling (SMOTETome...
28 Logistic Regression_Hybrid Sampling (SMOTETome...
29 Logistic Regression_Hybrid Sampling (SMOTETome...
30 Logistic Regression_Hybrid Sampling (SMOTETome...
31 Logistic Regression_Hybrid Sampling (SMOTETome...
32 Logistic Regression_Hybrid Sampling (SMOTETome...
33 Logistic Regression_Hybrid Sampling (SMOTETome...
34 Logistic Regression_Hybrid Sampling (SMOTETome...
35 Logistic Regression_Hybrid Sampling (SMOTETome...
36 Logistic Regression_Hybrid Sampling (SMOTETome...
37 Logistic Regression_Hybrid Sampling (SMOTETome...
38 Logistic Regression_Hybrid Sampling (SMOTETome...
39 Logistic Regression_Hybrid Sampling (SMOTETome...
40 Logistic Regression_Hybrid Sampling (SMOTETome...
41 Logistic Regression_Hybrid Sampling (SMOTETome...
42 Logistic Regression_Hybrid Sampling (SMOTETome...
43 Logistic Regression_Hybrid Sampling (SMOTETome...
44 Logistic Regression_Hybrid Sampling (SMOTETome...
45 Logistic Regression_Hybrid Sampling (SMOTETome...
46 Logistic Regression_Hybrid Sampling (SMOTETome...
47 Logistic Regression_Hybrid Sampling (SMOTETome...
48 Logistic Regression_Hybrid Sampling (SMOTETome...
49 Logistic Regression_Hybrid Sampling (SMOTETome...
50 Logistic Regression_Hybrid Sampling (SMOTETome...
51 Logistic Regression_Hybrid Sampling (SMOTETome...
52 Logistic Regression_Hybrid Sampling (SMOTETome...
53 Logistic Regression_Hybrid Sampling (SMOTETome...
54 Logistic Regression_Hybrid Sampling (SMOTETome...
55 Logistic Regression_Hybrid Sampling (SMOTETome...
56 Logistic Regression_Hybrid Sampling (SMOTETome...
57 Logistic Regression_Hybrid Sampling (SMOTETome...
58 Logistic Regression_Hybrid Sampling (SMOTETome...
59 Logistic Regression_Hybrid Sampling (SMOTETome...
60 Logistic Regression_Hybrid Sampling (SMOTETome...
61 Logistic Regression_Hybrid Sampling (SMOTETome...
62 Logistic Regression_Hybrid Sampling (SMOTETome...
63 Logistic Regression_Hybrid Sampling (SMOTETome...
64 Logistic Regression_Hybrid Sampling (SMOTETome...
65 Logistic Regression_Hybrid Sampling (SMOTETome...
66 Logistic Regression_Hybrid Sampling (SMOTETome...
67 Logistic Regression_Hybrid Sampling (SMOTETome...
68 Logistic Regression_Hybrid Sampling (SMOTETome...
69 Logistic Regression_Hybrid Sampling (SMOTETome...
70 Logistic Regression_Hybrid Sampling (SMOTETome...
71 Logistic Regression_Hybrid Sampling (SMOTETome...
72 Logistic Regression_Hybrid Sampling (SMOTETome...
73 Logistic Regression_Hybrid Sampling (SMOTETome...
74 Logistic Regression_Hybrid Sampling (SMOTETome...
75 Logistic Regression_Hybrid Sampling (SMOTETome...
76 Logistic Regression_Hybrid Sampling (SMOTETome...
77 Logistic Regression_Hybrid Sampling (SMOTETome...
78 Logistic Regression_Hybrid Sampling (SMOTETome...
79 Logistic Regression_Hybrid Sampling (SMOTETome...
80 Logistic Regression_Hybrid Sampling (SMOTETome...
81 Logistic Regression_Hybrid Sampling (SMOTETome...
82 Logistic Regression_Hybrid Sampling (SMOTETome...
83 Logistic Regression_Hybrid Sampling (SMOTETome...
84 Logistic Regression_Hybrid Sampling (SMOTETome...
85 Logistic Regression_Hybrid Sampling (SMOTETome...
86 Logistic Regression_Hybrid Sampling (SMOTETome...
87 Logistic Regression_Hybrid Sampling (SMOTETome...
88 Logistic Regression_Hybrid Sampling (SMOTETome...
89 Logistic Regression_Hybrid Sampling (SMOTETome...
90 Logistic Regression_Hybrid Sampling (SMOTETome...
91 Logistic Regression_Hybrid Sampling (SMOTETome...
92 Logistic Regression_Hybrid Sampling (SMOTETome...
93 Logistic Regression_Hybrid Sampling (SMOTETome...
94 Logistic Regression_Hybrid Sampling (SMOTETome...
95 Logistic Regression_Hybrid Sampling (SMOTETome...
96 Logistic Regression_Hybrid Sampling (SMOTETome...
97 Logistic Regression_Hybrid Sampling (SMOTETome...
98 Logistic Regression_Hybrid Sampling (SMOTETome...
99 Logistic Regression_Hybrid Sampling (SMOTETome...
100 Logistic Regression_Hybrid Sampling (SMOTETome...
101 Logistic Regression_Hybrid Sampling (SMOTETome...
102 Logistic Regression_Hybrid Sampling (SMOTETome...
103 Logistic Regression_Hybrid Sampling (SMOTETome...
104 Logistic Regression_Hybrid Sampling (SMOTETome...
105 Logistic Regression_Hybrid Sampling (SMOTETome...
106 Logistic Regression_Hybrid Sampling (SMOTETome...
107 Logistic Regression_Hybrid Sampling (SMOTETome...
108 Logistic Regression_Hybrid Sampling (SMOTETome...
109 Logistic Regression_Hybrid Sampling (SMOTETome... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 k-Nearest Neighbors (k-NN) 0.840927 NaN Zero Imputation
1 k-Nearest Neighbors (k-NN) 0.096703 NaN Zero Imputation
2 k-Nearest Neighbors (k-NN) 0.185654 NaN Zero Imputation
3 k-Nearest Neighbors (k-NN) 0.127168 NaN Zero Imputation
4 k-Nearest Neighbors (k-NN) 0.559796 NaN Zero Imputation
5 k-Nearest Neighbors (k-NN) 0.119271 NaN Zero Imputation
6 k-Nearest Neighbors (k-NN) 0.119593 NaN Zero Imputation
7 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
8 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
9 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
10 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
11 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
12 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
13 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
14 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
15 k-Nearest Neighbors (k-NN) 0.841454 NaN Zero Imputation
16 k-Nearest Neighbors (k-NN) 0.060241 NaN Zero Imputation
17 k-Nearest Neighbors (k-NN) 0.182927 NaN Zero Imputation
18 k-Nearest Neighbors (k-NN) 0.090634 NaN Zero Imputation
19 k-Nearest Neighbors (k-NN) 0.545939 NaN Zero Imputation
20 k-Nearest Neighbors (k-NN) 0.093566 NaN Zero Imputation
21 k-Nearest Neighbors (k-NN) 0.091878 NaN Zero Imputation
22 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
23 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
24 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
25 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
26 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
27 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
28 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
29 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
30 k-Nearest Neighbors (k-NN) 0.838820 NaN Zero Imputation
31 k-Nearest Neighbors (k-NN) 0.048832 NaN Zero Imputation
32 k-Nearest Neighbors (k-NN) 0.122995 NaN Zero Imputation
33 k-Nearest Neighbors (k-NN) 0.069909 NaN Zero Imputation
34 k-Nearest Neighbors (k-NN) 0.530319 NaN Zero Imputation
35 k-Nearest Neighbors (k-NN) 0.071788 NaN Zero Imputation
36 k-Nearest Neighbors (k-NN) 0.060638 NaN Zero Imputation
37 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
38 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
39 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
40 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
41 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
42 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
43 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
44 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
45 k-Nearest Neighbors (k-NN) 0.841412 NaN Zero Imputation
46 k-Nearest Neighbors (k-NN) 0.062500 NaN Zero Imputation
47 k-Nearest Neighbors (k-NN) 0.147959 NaN Zero Imputation
48 k-Nearest Neighbors (k-NN) 0.087879 NaN Zero Imputation
49 k-Nearest Neighbors (k-NN) 0.533114 NaN Zero Imputation
50 k-Nearest Neighbors (k-NN) 0.068946 NaN Zero Imputation
51 k-Nearest Neighbors (k-NN) 0.066229 NaN Zero Imputation
52 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
53 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
54 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
55 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
56 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
57 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
58 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
59 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
60 k-Nearest Neighbors (k-NN) 0.840095 NaN Zero Imputation
61 k-Nearest Neighbors (k-NN) 0.090147 NaN Zero Imputation
62 k-Nearest Neighbors (k-NN) 0.199074 NaN Zero Imputation
63 k-Nearest Neighbors (k-NN) 0.124098 NaN Zero Imputation
64 k-Nearest Neighbors (k-NN) 0.565178 NaN Zero Imputation
65 k-Nearest Neighbors (k-NN) 0.131378 NaN Zero Imputation
66 k-Nearest Neighbors (k-NN) 0.130355 NaN Zero Imputation
67 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
68 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
69 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
70 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
71 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
72 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
73 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
74 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
45 Oversampling
46 Oversampling
47 Oversampling
48 Oversampling
49 Oversampling
50 Oversampling
51 Oversampling
52 Oversampling
53 Oversampling
54 Oversampling
55 Oversampling
56 Oversampling
57 Oversampling
58 Oversampling
59 Oversampling
60 Oversampling
61 Oversampling
62 Oversampling
63 Oversampling
64 Oversampling
65 Oversampling
66 Oversampling
67 Oversampling
68 Oversampling
69 Oversampling
70 Oversampling
71 Oversampling
72 Oversampling
73 Oversampling
74 Oversampling
Model Unique Code
0 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
1 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
2 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
3 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
4 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
5 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
6 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
7 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
8 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
9 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
10 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
11 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
12 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
13 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
14 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
15 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
16 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
17 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
18 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
19 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
20 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
21 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
22 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
23 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
24 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
25 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
26 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
27 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
28 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
29 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
30 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
31 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
32 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
33 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
34 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
35 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
36 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
37 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
38 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
39 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
40 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
41 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
42 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
43 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
44 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
45 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
46 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
47 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
48 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
49 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
50 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
51 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
52 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
53 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
54 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
55 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
56 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
57 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
58 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
59 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
60 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
61 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
62 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
63 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
64 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
65 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
66 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
67 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
68 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
69 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
70 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
71 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
72 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
73 k-Nearest Neighbors (k-NN)_Oversampling_Zero I...
74 k-Nearest Neighbors (k-NN)_Oversampling_Zero I... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 k-Nearest Neighbors (k-NN) 0.839083 NaN Mean Imputation
1 k-Nearest Neighbors (k-NN) 0.069124 NaN Mean Imputation
2 k-Nearest Neighbors (k-NN) 0.126582 NaN Mean Imputation
3 k-Nearest Neighbors (k-NN) 0.089419 NaN Mean Imputation
4 k-Nearest Neighbors (k-NN) 0.545102 NaN Mean Imputation
5 k-Nearest Neighbors (k-NN) 0.099853 NaN Mean Imputation
6 k-Nearest Neighbors (k-NN) 0.090204 NaN Mean Imputation
7 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
8 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
9 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
10 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
11 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
12 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
13 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
14 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
15 k-Nearest Neighbors (k-NN) 0.848038 NaN Mean Imputation
16 k-Nearest Neighbors (k-NN) 0.067086 NaN Mean Imputation
17 k-Nearest Neighbors (k-NN) 0.195122 NaN Mean Imputation
18 k-Nearest Neighbors (k-NN) 0.099844 NaN Mean Imputation
19 k-Nearest Neighbors (k-NN) 0.552346 NaN Mean Imputation
20 k-Nearest Neighbors (k-NN) 0.104619 NaN Mean Imputation
21 k-Nearest Neighbors (k-NN) 0.104692 NaN Mean Imputation
22 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
23 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
24 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
25 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
26 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
27 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
28 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
29 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
30 k-Nearest Neighbors (k-NN) 0.836450 NaN Mean Imputation
31 k-Nearest Neighbors (k-NN) 0.062500 NaN Mean Imputation
32 k-Nearest Neighbors (k-NN) 0.165775 NaN Mean Imputation
33 k-Nearest Neighbors (k-NN) 0.090776 NaN Mean Imputation
34 k-Nearest Neighbors (k-NN) 0.558970 NaN Mean Imputation
35 k-Nearest Neighbors (k-NN) 0.127480 NaN Mean Imputation
36 k-Nearest Neighbors (k-NN) 0.117940 NaN Mean Imputation
37 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
38 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
39 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
40 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
41 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
42 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
43 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
44 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
45 k-Nearest Neighbors (k-NN) 0.841939 NaN Mean Imputation
46 k-Nearest Neighbors (k-NN) 0.062771 NaN Mean Imputation
47 k-Nearest Neighbors (k-NN) 0.147959 NaN Mean Imputation
48 k-Nearest Neighbors (k-NN) 0.088146 NaN Mean Imputation
49 k-Nearest Neighbors (k-NN) 0.539427 NaN Mean Imputation
50 k-Nearest Neighbors (k-NN) 0.085261 NaN Mean Imputation
51 k-Nearest Neighbors (k-NN) 0.078853 NaN Mean Imputation
52 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
53 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
54 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
55 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
56 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
57 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
58 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
59 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
60 k-Nearest Neighbors (k-NN) 0.840095 NaN Mean Imputation
61 k-Nearest Neighbors (k-NN) 0.088421 NaN Mean Imputation
62 k-Nearest Neighbors (k-NN) 0.194444 NaN Mean Imputation
63 k-Nearest Neighbors (k-NN) 0.121563 NaN Mean Imputation
64 k-Nearest Neighbors (k-NN) 0.576130 NaN Mean Imputation
65 k-Nearest Neighbors (k-NN) 0.158199 NaN Mean Imputation
66 k-Nearest Neighbors (k-NN) 0.152259 NaN Mean Imputation
67 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
68 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
69 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
70 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
71 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
72 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
73 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
74 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
45 Oversampling
46 Oversampling
47 Oversampling
48 Oversampling
49 Oversampling
50 Oversampling
51 Oversampling
52 Oversampling
53 Oversampling
54 Oversampling
55 Oversampling
56 Oversampling
57 Oversampling
58 Oversampling
59 Oversampling
60 Oversampling
61 Oversampling
62 Oversampling
63 Oversampling
64 Oversampling
65 Oversampling
66 Oversampling
67 Oversampling
68 Oversampling
69 Oversampling
70 Oversampling
71 Oversampling
72 Oversampling
73 Oversampling
74 Oversampling
Model Unique Code
0 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
1 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
2 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
3 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
4 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
5 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
6 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
7 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
8 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
9 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
10 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
11 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
12 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
13 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
14 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
15 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
16 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
17 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
18 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
19 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
20 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
21 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
22 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
23 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
24 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
25 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
26 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
27 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
28 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
29 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
30 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
31 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
32 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
33 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
34 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
35 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
36 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
37 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
38 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
39 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
40 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
41 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
42 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
43 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
44 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
45 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
46 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
47 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
48 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
49 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
50 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
51 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
52 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
53 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
54 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
55 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
56 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
57 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
58 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
59 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
60 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
61 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
62 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
63 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
64 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
65 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
66 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
67 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
68 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
69 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
70 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
71 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
72 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
73 k-Nearest Neighbors (k-NN)_Oversampling_Mean I...
74 k-Nearest Neighbors (k-NN)_Oversampling_Mean I... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 k-Nearest Neighbors (k-NN) 0.841454 NaN Median Imputation
1 k-Nearest Neighbors (k-NN) 0.088036 NaN Median Imputation
2 k-Nearest Neighbors (k-NN) 0.164557 NaN Median Imputation
3 k-Nearest Neighbors (k-NN) 0.114706 NaN Median Imputation
4 k-Nearest Neighbors (k-NN) 0.554976 NaN Median Imputation
5 k-Nearest Neighbors (k-NN) 0.111955 NaN Median Imputation
6 k-Nearest Neighbors (k-NN) 0.109951 NaN Median Imputation
7 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
8 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
9 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
10 k-Nearest Neighbors (k-NN) NaN None Median Imputation
11 k-Nearest Neighbors (k-NN) NaN None Median Imputation
12 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
13 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
14 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
15 k-Nearest Neighbors (k-NN) 0.843034 NaN Median Imputation
16 k-Nearest Neighbors (k-NN) 0.066265 NaN Median Imputation
17 k-Nearest Neighbors (k-NN) 0.201220 NaN Median Imputation
18 k-Nearest Neighbors (k-NN) 0.099698 NaN Median Imputation
19 k-Nearest Neighbors (k-NN) 0.551658 NaN Median Imputation
20 k-Nearest Neighbors (k-NN) 0.107138 NaN Median Imputation
21 k-Nearest Neighbors (k-NN) 0.103316 NaN Median Imputation
22 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
23 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
24 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
25 k-Nearest Neighbors (k-NN) NaN None Median Imputation
26 k-Nearest Neighbors (k-NN) NaN None Median Imputation
27 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
28 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
29 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
30 k-Nearest Neighbors (k-NN) 0.839083 NaN Median Imputation
31 k-Nearest Neighbors (k-NN) 0.058333 NaN Median Imputation
32 k-Nearest Neighbors (k-NN) 0.149733 NaN Median Imputation
33 k-Nearest Neighbors (k-NN) 0.083958 NaN Median Imputation
34 k-Nearest Neighbors (k-NN) 0.541180 NaN Median Imputation
35 k-Nearest Neighbors (k-NN) 0.090324 NaN Median Imputation
36 k-Nearest Neighbors (k-NN) 0.082360 NaN Median Imputation
37 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
38 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
39 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
40 k-Nearest Neighbors (k-NN) NaN None Median Imputation
41 k-Nearest Neighbors (k-NN) NaN None Median Imputation
42 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
43 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
44 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
45 k-Nearest Neighbors (k-NN) 0.841939 NaN Median Imputation
46 k-Nearest Neighbors (k-NN) 0.053097 NaN Median Imputation
47 k-Nearest Neighbors (k-NN) 0.122449 NaN Median Imputation
48 k-Nearest Neighbors (k-NN) 0.074074 NaN Median Imputation
49 k-Nearest Neighbors (k-NN) 0.539729 NaN Median Imputation
50 k-Nearest Neighbors (k-NN) 0.088141 NaN Median Imputation
51 k-Nearest Neighbors (k-NN) 0.079459 NaN Median Imputation
52 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
53 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
54 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
55 k-Nearest Neighbors (k-NN) NaN None Median Imputation
56 k-Nearest Neighbors (k-NN) NaN None Median Imputation
57 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
58 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
59 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
60 k-Nearest Neighbors (k-NN) 0.844310 NaN Median Imputation
61 k-Nearest Neighbors (k-NN) 0.098501 NaN Median Imputation
62 k-Nearest Neighbors (k-NN) 0.212963 NaN Median Imputation
63 k-Nearest Neighbors (k-NN) 0.134700 NaN Median Imputation
64 k-Nearest Neighbors (k-NN) 0.576763 NaN Median Imputation
65 k-Nearest Neighbors (k-NN) 0.155364 NaN Median Imputation
66 k-Nearest Neighbors (k-NN) 0.153525 NaN Median Imputation
67 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
68 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
69 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
70 k-Nearest Neighbors (k-NN) NaN None Median Imputation
71 k-Nearest Neighbors (k-NN) NaN None Median Imputation
72 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
73 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
74 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
45 Oversampling
46 Oversampling
47 Oversampling
48 Oversampling
49 Oversampling
50 Oversampling
51 Oversampling
52 Oversampling
53 Oversampling
54 Oversampling
55 Oversampling
56 Oversampling
57 Oversampling
58 Oversampling
59 Oversampling
60 Oversampling
61 Oversampling
62 Oversampling
63 Oversampling
64 Oversampling
65 Oversampling
66 Oversampling
67 Oversampling
68 Oversampling
69 Oversampling
70 Oversampling
71 Oversampling
72 Oversampling
73 Oversampling
74 Oversampling
Model Unique Code
0 k-Nearest Neighbors (k-NN)_Oversampling_Median...
1 k-Nearest Neighbors (k-NN)_Oversampling_Median...
2 k-Nearest Neighbors (k-NN)_Oversampling_Median...
3 k-Nearest Neighbors (k-NN)_Oversampling_Median...
4 k-Nearest Neighbors (k-NN)_Oversampling_Median...
5 k-Nearest Neighbors (k-NN)_Oversampling_Median...
6 k-Nearest Neighbors (k-NN)_Oversampling_Median...
7 k-Nearest Neighbors (k-NN)_Oversampling_Median...
8 k-Nearest Neighbors (k-NN)_Oversampling_Median...
9 k-Nearest Neighbors (k-NN)_Oversampling_Median...
10 k-Nearest Neighbors (k-NN)_Oversampling_Median...
11 k-Nearest Neighbors (k-NN)_Oversampling_Median...
12 k-Nearest Neighbors (k-NN)_Oversampling_Median...
13 k-Nearest Neighbors (k-NN)_Oversampling_Median...
14 k-Nearest Neighbors (k-NN)_Oversampling_Median...
15 k-Nearest Neighbors (k-NN)_Oversampling_Median...
16 k-Nearest Neighbors (k-NN)_Oversampling_Median...
17 k-Nearest Neighbors (k-NN)_Oversampling_Median...
18 k-Nearest Neighbors (k-NN)_Oversampling_Median...
19 k-Nearest Neighbors (k-NN)_Oversampling_Median...
20 k-Nearest Neighbors (k-NN)_Oversampling_Median...
21 k-Nearest Neighbors (k-NN)_Oversampling_Median...
22 k-Nearest Neighbors (k-NN)_Oversampling_Median...
23 k-Nearest Neighbors (k-NN)_Oversampling_Median...
24 k-Nearest Neighbors (k-NN)_Oversampling_Median...
25 k-Nearest Neighbors (k-NN)_Oversampling_Median...
26 k-Nearest Neighbors (k-NN)_Oversampling_Median...
27 k-Nearest Neighbors (k-NN)_Oversampling_Median...
28 k-Nearest Neighbors (k-NN)_Oversampling_Median...
29 k-Nearest Neighbors (k-NN)_Oversampling_Median...
30 k-Nearest Neighbors (k-NN)_Oversampling_Median...
31 k-Nearest Neighbors (k-NN)_Oversampling_Median...
32 k-Nearest Neighbors (k-NN)_Oversampling_Median...
33 k-Nearest Neighbors (k-NN)_Oversampling_Median...
34 k-Nearest Neighbors (k-NN)_Oversampling_Median...
35 k-Nearest Neighbors (k-NN)_Oversampling_Median...
36 k-Nearest Neighbors (k-NN)_Oversampling_Median...
37 k-Nearest Neighbors (k-NN)_Oversampling_Median...
38 k-Nearest Neighbors (k-NN)_Oversampling_Median...
39 k-Nearest Neighbors (k-NN)_Oversampling_Median...
40 k-Nearest Neighbors (k-NN)_Oversampling_Median...
41 k-Nearest Neighbors (k-NN)_Oversampling_Median...
42 k-Nearest Neighbors (k-NN)_Oversampling_Median...
43 k-Nearest Neighbors (k-NN)_Oversampling_Median...
44 k-Nearest Neighbors (k-NN)_Oversampling_Median...
45 k-Nearest Neighbors (k-NN)_Oversampling_Median...
46 k-Nearest Neighbors (k-NN)_Oversampling_Median...
47 k-Nearest Neighbors (k-NN)_Oversampling_Median...
48 k-Nearest Neighbors (k-NN)_Oversampling_Median...
49 k-Nearest Neighbors (k-NN)_Oversampling_Median...
50 k-Nearest Neighbors (k-NN)_Oversampling_Median...
51 k-Nearest Neighbors (k-NN)_Oversampling_Median...
52 k-Nearest Neighbors (k-NN)_Oversampling_Median...
53 k-Nearest Neighbors (k-NN)_Oversampling_Median...
54 k-Nearest Neighbors (k-NN)_Oversampling_Median...
55 k-Nearest Neighbors (k-NN)_Oversampling_Median...
56 k-Nearest Neighbors (k-NN)_Oversampling_Median...
57 k-Nearest Neighbors (k-NN)_Oversampling_Median...
58 k-Nearest Neighbors (k-NN)_Oversampling_Median...
59 k-Nearest Neighbors (k-NN)_Oversampling_Median...
60 k-Nearest Neighbors (k-NN)_Oversampling_Median...
61 k-Nearest Neighbors (k-NN)_Oversampling_Median...
62 k-Nearest Neighbors (k-NN)_Oversampling_Median...
63 k-Nearest Neighbors (k-NN)_Oversampling_Median...
64 k-Nearest Neighbors (k-NN)_Oversampling_Median...
65 k-Nearest Neighbors (k-NN)_Oversampling_Median...
66 k-Nearest Neighbors (k-NN)_Oversampling_Median...
67 k-Nearest Neighbors (k-NN)_Oversampling_Median...
68 k-Nearest Neighbors (k-NN)_Oversampling_Median...
69 k-Nearest Neighbors (k-NN)_Oversampling_Median...
70 k-Nearest Neighbors (k-NN)_Oversampling_Median...
71 k-Nearest Neighbors (k-NN)_Oversampling_Median...
72 k-Nearest Neighbors (k-NN)_Oversampling_Median...
73 k-Nearest Neighbors (k-NN)_Oversampling_Median...
74 k-Nearest Neighbors (k-NN)_Oversampling_Median... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 k-Nearest Neighbors (k-NN) 0.621543 NaN Zero Imputation
1 k-Nearest Neighbors (k-NN) 0.088477 NaN Zero Imputation
2 k-Nearest Neighbors (k-NN) 0.544304 NaN Zero Imputation
3 k-Nearest Neighbors (k-NN) 0.152212 NaN Zero Imputation
4 k-Nearest Neighbors (k-NN) 0.617164 NaN Zero Imputation
5 k-Nearest Neighbors (k-NN) 0.170989 NaN Zero Imputation
6 k-Nearest Neighbors (k-NN) 0.234329 NaN Zero Imputation
7 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
8 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
9 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
10 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
11 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
12 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
13 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
14 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
15 k-Nearest Neighbors (k-NN) 0.616539 NaN Zero Imputation
16 k-Nearest Neighbors (k-NN) 0.071618 NaN Zero Imputation
17 k-Nearest Neighbors (k-NN) 0.658537 NaN Zero Imputation
18 k-Nearest Neighbors (k-NN) 0.129187 NaN Zero Imputation
19 k-Nearest Neighbors (k-NN) 0.658879 NaN Zero Imputation
20 k-Nearest Neighbors (k-NN) 0.273180 NaN Zero Imputation
21 k-Nearest Neighbors (k-NN) 0.317758 NaN Zero Imputation
22 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
23 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
24 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
25 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
26 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
27 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
28 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
29 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
30 k-Nearest Neighbors (k-NN) 0.618120 NaN Zero Imputation
31 k-Nearest Neighbors (k-NN) 0.066575 NaN Zero Imputation
32 k-Nearest Neighbors (k-NN) 0.518717 NaN Zero Imputation
33 k-Nearest Neighbors (k-NN) 0.118005 NaN Zero Imputation
34 k-Nearest Neighbors (k-NN) 0.601634 NaN Zero Imputation
35 k-Nearest Neighbors (k-NN) 0.171524 NaN Zero Imputation
36 k-Nearest Neighbors (k-NN) 0.203268 NaN Zero Imputation
37 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
38 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
39 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
40 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
41 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
42 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
43 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
44 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
45 k-Nearest Neighbors (k-NN) 0.599579 NaN Zero Imputation
46 k-Nearest Neighbors (k-NN) 0.067320 NaN Zero Imputation
47 k-Nearest Neighbors (k-NN) 0.525510 NaN Zero Imputation
48 k-Nearest Neighbors (k-NN) 0.119351 NaN Zero Imputation
49 k-Nearest Neighbors (k-NN) 0.586742 NaN Zero Imputation
50 k-Nearest Neighbors (k-NN) 0.129121 NaN Zero Imputation
51 k-Nearest Neighbors (k-NN) 0.173484 NaN Zero Imputation
52 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
53 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
54 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
55 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
56 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
57 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
58 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
59 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
60 k-Nearest Neighbors (k-NN) 0.608535 NaN Zero Imputation
61 k-Nearest Neighbors (k-NN) 0.081686 NaN Zero Imputation
62 k-Nearest Neighbors (k-NN) 0.574074 NaN Zero Imputation
63 k-Nearest Neighbors (k-NN) 0.143022 NaN Zero Imputation
64 k-Nearest Neighbors (k-NN) 0.619156 NaN Zero Imputation
65 k-Nearest Neighbors (k-NN) 0.184689 NaN Zero Imputation
66 k-Nearest Neighbors (k-NN) 0.238312 NaN Zero Imputation
67 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
68 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
69 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
70 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
71 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
72 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
73 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
74 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
45 Undersampling
46 Undersampling
47 Undersampling
48 Undersampling
49 Undersampling
50 Undersampling
51 Undersampling
52 Undersampling
53 Undersampling
54 Undersampling
55 Undersampling
56 Undersampling
57 Undersampling
58 Undersampling
59 Undersampling
60 Undersampling
61 Undersampling
62 Undersampling
63 Undersampling
64 Undersampling
65 Undersampling
66 Undersampling
67 Undersampling
68 Undersampling
69 Undersampling
70 Undersampling
71 Undersampling
72 Undersampling
73 Undersampling
74 Undersampling
Model Unique Code
0 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
1 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
2 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
3 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
4 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
5 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
6 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
7 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
8 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
9 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
10 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
11 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
12 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
13 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
14 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
15 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
16 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
17 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
18 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
19 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
20 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
21 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
22 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
23 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
24 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
25 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
26 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
27 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
28 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
29 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
30 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
31 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
32 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
33 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
34 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
35 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
36 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
37 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
38 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
39 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
40 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
41 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
42 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
43 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
44 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
45 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
46 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
47 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
48 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
49 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
50 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
51 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
52 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
53 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
54 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
55 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
56 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
57 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
58 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
59 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
60 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
61 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
62 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
63 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
64 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
65 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
66 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
67 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
68 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
69 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
70 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
71 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
72 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
73 k-Nearest Neighbors (k-NN)_Undersampling_Zero ...
74 k-Nearest Neighbors (k-NN)_Undersampling_Zero ... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 k-Nearest Neighbors (k-NN) 0.610482 NaN Mean Imputation
1 k-Nearest Neighbors (k-NN) 0.080972 NaN Mean Imputation
2 k-Nearest Neighbors (k-NN) 0.506329 NaN Mean Imputation
3 k-Nearest Neighbors (k-NN) 0.139616 NaN Mean Imputation
4 k-Nearest Neighbors (k-NN) 0.591465 NaN Mean Imputation
5 k-Nearest Neighbors (k-NN) 0.123745 NaN Mean Imputation
6 k-Nearest Neighbors (k-NN) 0.182929 NaN Mean Imputation
7 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
8 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
9 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
10 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
11 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
12 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
13 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
14 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
15 k-Nearest Neighbors (k-NN) 0.590730 NaN Mean Imputation
16 k-Nearest Neighbors (k-NN) 0.057888 NaN Mean Imputation
17 k-Nearest Neighbors (k-NN) 0.554878 NaN Mean Imputation
18 k-Nearest Neighbors (k-NN) 0.104839 NaN Mean Imputation
19 k-Nearest Neighbors (k-NN) 0.603360 NaN Mean Imputation
20 k-Nearest Neighbors (k-NN) 0.147226 NaN Mean Imputation
21 k-Nearest Neighbors (k-NN) 0.206720 NaN Mean Imputation
22 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
23 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
24 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
25 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
26 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
27 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
28 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
29 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
30 k-Nearest Neighbors (k-NN) 0.570714 NaN Mean Imputation
31 k-Nearest Neighbors (k-NN) 0.070280 NaN Mean Imputation
32 k-Nearest Neighbors (k-NN) 0.631016 NaN Mean Imputation
33 k-Nearest Neighbors (k-NN) 0.126474 NaN Mean Imputation
34 k-Nearest Neighbors (k-NN) 0.613856 NaN Mean Imputation
35 k-Nearest Neighbors (k-NN) 0.198606 NaN Mean Imputation
36 k-Nearest Neighbors (k-NN) 0.227711 NaN Mean Imputation
37 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
38 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
39 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
40 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
41 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
42 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
43 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
44 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
45 k-Nearest Neighbors (k-NN) 0.626185 NaN Mean Imputation
46 k-Nearest Neighbors (k-NN) 0.072676 NaN Mean Imputation
47 k-Nearest Neighbors (k-NN) 0.530612 NaN Mean Imputation
48 k-Nearest Neighbors (k-NN) 0.127843 NaN Mean Imputation
49 k-Nearest Neighbors (k-NN) 0.601657 NaN Mean Imputation
50 k-Nearest Neighbors (k-NN) 0.162001 NaN Mean Imputation
51 k-Nearest Neighbors (k-NN) 0.203313 NaN Mean Imputation
52 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
53 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
54 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
55 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
56 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
57 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
58 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
59 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
60 k-Nearest Neighbors (k-NN) 0.603003 NaN Mean Imputation
61 k-Nearest Neighbors (k-NN) 0.081116 NaN Mean Imputation
62 k-Nearest Neighbors (k-NN) 0.578704 NaN Mean Imputation
63 k-Nearest Neighbors (k-NN) 0.142288 NaN Mean Imputation
64 k-Nearest Neighbors (k-NN) 0.621237 NaN Mean Imputation
65 k-Nearest Neighbors (k-NN) 0.183173 NaN Mean Imputation
66 k-Nearest Neighbors (k-NN) 0.242474 NaN Mean Imputation
67 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
68 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
69 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
70 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
71 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
72 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
73 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
74 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
45 Undersampling
46 Undersampling
47 Undersampling
48 Undersampling
49 Undersampling
50 Undersampling
51 Undersampling
52 Undersampling
53 Undersampling
54 Undersampling
55 Undersampling
56 Undersampling
57 Undersampling
58 Undersampling
59 Undersampling
60 Undersampling
61 Undersampling
62 Undersampling
63 Undersampling
64 Undersampling
65 Undersampling
66 Undersampling
67 Undersampling
68 Undersampling
69 Undersampling
70 Undersampling
71 Undersampling
72 Undersampling
73 Undersampling
74 Undersampling
Model Unique Code
0 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
1 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
2 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
3 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
4 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
5 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
6 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
7 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
8 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
9 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
10 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
11 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
12 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
13 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
14 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
15 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
16 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
17 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
18 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
19 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
20 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
21 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
22 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
23 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
24 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
25 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
26 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
27 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
28 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
29 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
30 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
31 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
32 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
33 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
34 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
35 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
36 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
37 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
38 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
39 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
40 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
41 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
42 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
43 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
44 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
45 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
46 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
47 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
48 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
49 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
50 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
51 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
52 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
53 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
54 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
55 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
56 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
57 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
58 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
59 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
60 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
61 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
62 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
63 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
64 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
65 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
66 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
67 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
68 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
69 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
70 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
71 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
72 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
73 k-Nearest Neighbors (k-NN)_Undersampling_Mean ...
74 k-Nearest Neighbors (k-NN)_Undersampling_Mean ... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 k-Nearest Neighbors (k-NN) 0.611799 NaN Median Imputation
1 k-Nearest Neighbors (k-NN) 0.081812 NaN Median Imputation
2 k-Nearest Neighbors (k-NN) 0.510549 NaN Median Imputation
3 k-Nearest Neighbors (k-NN) 0.141026 NaN Median Imputation
4 k-Nearest Neighbors (k-NN) 0.586476 NaN Median Imputation
5 k-Nearest Neighbors (k-NN) 0.129088 NaN Median Imputation
6 k-Nearest Neighbors (k-NN) 0.172952 NaN Median Imputation
7 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
8 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
9 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
10 k-Nearest Neighbors (k-NN) NaN None Median Imputation
11 k-Nearest Neighbors (k-NN) NaN None Median Imputation
12 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
13 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
14 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
15 k-Nearest Neighbors (k-NN) 0.600474 NaN Median Imputation
16 k-Nearest Neighbors (k-NN) 0.057554 NaN Median Imputation
17 k-Nearest Neighbors (k-NN) 0.536585 NaN Median Imputation
18 k-Nearest Neighbors (k-NN) 0.103957 NaN Median Imputation
19 k-Nearest Neighbors (k-NN) 0.609024 NaN Median Imputation
20 k-Nearest Neighbors (k-NN) 0.139943 NaN Median Imputation
21 k-Nearest Neighbors (k-NN) 0.218049 NaN Median Imputation
22 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
23 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
24 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
25 k-Nearest Neighbors (k-NN) NaN None Median Imputation
26 k-Nearest Neighbors (k-NN) NaN None Median Imputation
27 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
28 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
29 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
30 k-Nearest Neighbors (k-NN) 0.595997 NaN Median Imputation
31 k-Nearest Neighbors (k-NN) 0.071837 NaN Median Imputation
32 k-Nearest Neighbors (k-NN) 0.604278 NaN Median Imputation
33 k-Nearest Neighbors (k-NN) 0.128409 NaN Median Imputation
34 k-Nearest Neighbors (k-NN) 0.624269 NaN Median Imputation
35 k-Nearest Neighbors (k-NN) 0.199846 NaN Median Imputation
36 k-Nearest Neighbors (k-NN) 0.248537 NaN Median Imputation
37 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
38 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
39 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
40 k-Nearest Neighbors (k-NN) NaN None Median Imputation
41 k-Nearest Neighbors (k-NN) NaN None Median Imputation
42 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
43 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
44 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
45 k-Nearest Neighbors (k-NN) 0.611433 NaN Median Imputation
46 k-Nearest Neighbors (k-NN) 0.074518 NaN Median Imputation
47 k-Nearest Neighbors (k-NN) 0.571429 NaN Median Imputation
48 k-Nearest Neighbors (k-NN) 0.131842 NaN Median Imputation
49 k-Nearest Neighbors (k-NN) 0.623647 NaN Median Imputation
50 k-Nearest Neighbors (k-NN) 0.191514 NaN Median Imputation
51 k-Nearest Neighbors (k-NN) 0.247293 NaN Median Imputation
52 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
53 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
54 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
55 k-Nearest Neighbors (k-NN) NaN None Median Imputation
56 k-Nearest Neighbors (k-NN) NaN None Median Imputation
57 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
58 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
59 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
60 k-Nearest Neighbors (k-NN) 0.610379 NaN Median Imputation
61 k-Nearest Neighbors (k-NN) 0.076459 NaN Median Imputation
62 k-Nearest Neighbors (k-NN) 0.527778 NaN Median Imputation
63 k-Nearest Neighbors (k-NN) 0.133568 NaN Median Imputation
64 k-Nearest Neighbors (k-NN) 0.584662 NaN Median Imputation
65 k-Nearest Neighbors (k-NN) 0.143141 NaN Median Imputation
66 k-Nearest Neighbors (k-NN) 0.169324 NaN Median Imputation
67 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
68 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
69 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
70 k-Nearest Neighbors (k-NN) NaN None Median Imputation
71 k-Nearest Neighbors (k-NN) NaN None Median Imputation
72 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
73 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
74 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
45 Undersampling
46 Undersampling
47 Undersampling
48 Undersampling
49 Undersampling
50 Undersampling
51 Undersampling
52 Undersampling
53 Undersampling
54 Undersampling
55 Undersampling
56 Undersampling
57 Undersampling
58 Undersampling
59 Undersampling
60 Undersampling
61 Undersampling
62 Undersampling
63 Undersampling
64 Undersampling
65 Undersampling
66 Undersampling
67 Undersampling
68 Undersampling
69 Undersampling
70 Undersampling
71 Undersampling
72 Undersampling
73 Undersampling
74 Undersampling
Model Unique Code
0 k-Nearest Neighbors (k-NN)_Undersampling_Media...
1 k-Nearest Neighbors (k-NN)_Undersampling_Media...
2 k-Nearest Neighbors (k-NN)_Undersampling_Media...
3 k-Nearest Neighbors (k-NN)_Undersampling_Media...
4 k-Nearest Neighbors (k-NN)_Undersampling_Media...
5 k-Nearest Neighbors (k-NN)_Undersampling_Media...
6 k-Nearest Neighbors (k-NN)_Undersampling_Media...
7 k-Nearest Neighbors (k-NN)_Undersampling_Media...
8 k-Nearest Neighbors (k-NN)_Undersampling_Media...
9 k-Nearest Neighbors (k-NN)_Undersampling_Media...
10 k-Nearest Neighbors (k-NN)_Undersampling_Media...
11 k-Nearest Neighbors (k-NN)_Undersampling_Media...
12 k-Nearest Neighbors (k-NN)_Undersampling_Media...
13 k-Nearest Neighbors (k-NN)_Undersampling_Media...
14 k-Nearest Neighbors (k-NN)_Undersampling_Media...
15 k-Nearest Neighbors (k-NN)_Undersampling_Media...
16 k-Nearest Neighbors (k-NN)_Undersampling_Media...
17 k-Nearest Neighbors (k-NN)_Undersampling_Media...
18 k-Nearest Neighbors (k-NN)_Undersampling_Media...
19 k-Nearest Neighbors (k-NN)_Undersampling_Media...
20 k-Nearest Neighbors (k-NN)_Undersampling_Media...
21 k-Nearest Neighbors (k-NN)_Undersampling_Media...
22 k-Nearest Neighbors (k-NN)_Undersampling_Media...
23 k-Nearest Neighbors (k-NN)_Undersampling_Media...
24 k-Nearest Neighbors (k-NN)_Undersampling_Media...
25 k-Nearest Neighbors (k-NN)_Undersampling_Media...
26 k-Nearest Neighbors (k-NN)_Undersampling_Media...
27 k-Nearest Neighbors (k-NN)_Undersampling_Media...
28 k-Nearest Neighbors (k-NN)_Undersampling_Media...
29 k-Nearest Neighbors (k-NN)_Undersampling_Media...
30 k-Nearest Neighbors (k-NN)_Undersampling_Media...
31 k-Nearest Neighbors (k-NN)_Undersampling_Media...
32 k-Nearest Neighbors (k-NN)_Undersampling_Media...
33 k-Nearest Neighbors (k-NN)_Undersampling_Media...
34 k-Nearest Neighbors (k-NN)_Undersampling_Media...
35 k-Nearest Neighbors (k-NN)_Undersampling_Media...
36 k-Nearest Neighbors (k-NN)_Undersampling_Media...
37 k-Nearest Neighbors (k-NN)_Undersampling_Media...
38 k-Nearest Neighbors (k-NN)_Undersampling_Media...
39 k-Nearest Neighbors (k-NN)_Undersampling_Media...
40 k-Nearest Neighbors (k-NN)_Undersampling_Media...
41 k-Nearest Neighbors (k-NN)_Undersampling_Media...
42 k-Nearest Neighbors (k-NN)_Undersampling_Media...
43 k-Nearest Neighbors (k-NN)_Undersampling_Media...
44 k-Nearest Neighbors (k-NN)_Undersampling_Media...
45 k-Nearest Neighbors (k-NN)_Undersampling_Media...
46 k-Nearest Neighbors (k-NN)_Undersampling_Media...
47 k-Nearest Neighbors (k-NN)_Undersampling_Media...
48 k-Nearest Neighbors (k-NN)_Undersampling_Media...
49 k-Nearest Neighbors (k-NN)_Undersampling_Media...
50 k-Nearest Neighbors (k-NN)_Undersampling_Media...
51 k-Nearest Neighbors (k-NN)_Undersampling_Media...
52 k-Nearest Neighbors (k-NN)_Undersampling_Media...
53 k-Nearest Neighbors (k-NN)_Undersampling_Media...
54 k-Nearest Neighbors (k-NN)_Undersampling_Media...
55 k-Nearest Neighbors (k-NN)_Undersampling_Media...
56 k-Nearest Neighbors (k-NN)_Undersampling_Media...
57 k-Nearest Neighbors (k-NN)_Undersampling_Media...
58 k-Nearest Neighbors (k-NN)_Undersampling_Media...
59 k-Nearest Neighbors (k-NN)_Undersampling_Media...
60 k-Nearest Neighbors (k-NN)_Undersampling_Media...
61 k-Nearest Neighbors (k-NN)_Undersampling_Media...
62 k-Nearest Neighbors (k-NN)_Undersampling_Media...
63 k-Nearest Neighbors (k-NN)_Undersampling_Media...
64 k-Nearest Neighbors (k-NN)_Undersampling_Media...
65 k-Nearest Neighbors (k-NN)_Undersampling_Media...
66 k-Nearest Neighbors (k-NN)_Undersampling_Media...
67 k-Nearest Neighbors (k-NN)_Undersampling_Media...
68 k-Nearest Neighbors (k-NN)_Undersampling_Media...
69 k-Nearest Neighbors (k-NN)_Undersampling_Media...
70 k-Nearest Neighbors (k-NN)_Undersampling_Media...
71 k-Nearest Neighbors (k-NN)_Undersampling_Media...
72 k-Nearest Neighbors (k-NN)_Undersampling_Media...
73 k-Nearest Neighbors (k-NN)_Undersampling_Media...
74 k-Nearest Neighbors (k-NN)_Undersampling_Media... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 k-Nearest Neighbors (k-NN) 0.672110 NaN Zero Imputation
1 k-Nearest Neighbors (k-NN) 0.081395 NaN Zero Imputation
2 k-Nearest Neighbors (k-NN) 0.413502 NaN Zero Imputation
3 k-Nearest Neighbors (k-NN) 0.136017 NaN Zero Imputation
4 k-Nearest Neighbors (k-NN) 0.576071 NaN Zero Imputation
5 k-Nearest Neighbors (k-NN) 0.122734 NaN Zero Imputation
6 k-Nearest Neighbors (k-NN) 0.152142 NaN Zero Imputation
7 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
8 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
9 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
10 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
11 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
12 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
13 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
14 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
15 k-Nearest Neighbors (k-NN) 0.648670 NaN Zero Imputation
16 k-Nearest Neighbors (k-NN) 0.056818 NaN Zero Imputation
17 k-Nearest Neighbors (k-NN) 0.457317 NaN Zero Imputation
18 k-Nearest Neighbors (k-NN) 0.101078 NaN Zero Imputation
19 k-Nearest Neighbors (k-NN) 0.572238 NaN Zero Imputation
20 k-Nearest Neighbors (k-NN) 0.122920 NaN Zero Imputation
21 k-Nearest Neighbors (k-NN) 0.144475 NaN Zero Imputation
22 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
23 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
24 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
25 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
26 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
27 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
28 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
29 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
30 k-Nearest Neighbors (k-NN) 0.652884 NaN Zero Imputation
31 k-Nearest Neighbors (k-NN) 0.057858 NaN Zero Imputation
32 k-Nearest Neighbors (k-NN) 0.395722 NaN Zero Imputation
33 k-Nearest Neighbors (k-NN) 0.100955 NaN Zero Imputation
34 k-Nearest Neighbors (k-NN) 0.537124 NaN Zero Imputation
35 k-Nearest Neighbors (k-NN) 0.089229 NaN Zero Imputation
36 k-Nearest Neighbors (k-NN) 0.074247 NaN Zero Imputation
37 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
38 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
39 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
40 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
41 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
42 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
43 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
44 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
45 k-Nearest Neighbors (k-NN) 0.653583 NaN Zero Imputation
46 k-Nearest Neighbors (k-NN) 0.061176 NaN Zero Imputation
47 k-Nearest Neighbors (k-NN) 0.397959 NaN Zero Imputation
48 k-Nearest Neighbors (k-NN) 0.106050 NaN Zero Imputation
49 k-Nearest Neighbors (k-NN) 0.527472 NaN Zero Imputation
50 k-Nearest Neighbors (k-NN) 0.065459 NaN Zero Imputation
51 k-Nearest Neighbors (k-NN) 0.054943 NaN Zero Imputation
52 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
53 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
54 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
55 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
56 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
57 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
58 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
59 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
60 k-Nearest Neighbors (k-NN) 0.655163 NaN Zero Imputation
61 k-Nearest Neighbors (k-NN) 0.077340 NaN Zero Imputation
62 k-Nearest Neighbors (k-NN) 0.462963 NaN Zero Imputation
63 k-Nearest Neighbors (k-NN) 0.132538 NaN Zero Imputation
64 k-Nearest Neighbors (k-NN) 0.574187 NaN Zero Imputation
65 k-Nearest Neighbors (k-NN) 0.134182 NaN Zero Imputation
66 k-Nearest Neighbors (k-NN) 0.148374 NaN Zero Imputation
67 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
68 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
69 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
70 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
71 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
72 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
73 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
74 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
1 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
2 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
3 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
4 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
5 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
6 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
7 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
8 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
9 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
10 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
11 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
12 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
13 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
14 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
15 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
16 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
17 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
18 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
19 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
20 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
21 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
22 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
23 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
24 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
25 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
26 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
27 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
28 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
29 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
30 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
31 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
32 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
33 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
34 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
35 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
36 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
37 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
38 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
39 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
40 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
41 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
42 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
43 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
44 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
45 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
46 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
47 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
48 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
49 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
50 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
51 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
52 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
53 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
54 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
55 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
56 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
57 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
58 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
59 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
60 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
61 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
62 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
63 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
64 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
65 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
66 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
67 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
68 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
69 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
70 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
71 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
72 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
73 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
74 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 k-Nearest Neighbors (k-NN) 0.687385 NaN Mean Imputation
1 k-Nearest Neighbors (k-NN) 0.078901 NaN Mean Imputation
2 k-Nearest Neighbors (k-NN) 0.375527 NaN Mean Imputation
3 k-Nearest Neighbors (k-NN) 0.130403 NaN Mean Imputation
4 k-Nearest Neighbors (k-NN) 0.541897 NaN Mean Imputation
5 k-Nearest Neighbors (k-NN) 0.083673 NaN Mean Imputation
6 k-Nearest Neighbors (k-NN) 0.083793 NaN Mean Imputation
7 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
8 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
9 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
10 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
11 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
12 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
13 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
14 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
15 k-Nearest Neighbors (k-NN) 0.666842 NaN Mean Imputation
16 k-Nearest Neighbors (k-NN) 0.059247 NaN Mean Imputation
17 k-Nearest Neighbors (k-NN) 0.451220 NaN Mean Imputation
18 k-Nearest Neighbors (k-NN) 0.104742 NaN Mean Imputation
19 k-Nearest Neighbors (k-NN) 0.559195 NaN Mean Imputation
20 k-Nearest Neighbors (k-NN) 0.128118 NaN Mean Imputation
21 k-Nearest Neighbors (k-NN) 0.118390 NaN Mean Imputation
22 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
23 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
24 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
25 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
26 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
27 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
28 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
29 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
30 k-Nearest Neighbors (k-NN) 0.667369 NaN Mean Imputation
31 k-Nearest Neighbors (k-NN) 0.068910 NaN Mean Imputation
32 k-Nearest Neighbors (k-NN) 0.459893 NaN Mean Imputation
33 k-Nearest Neighbors (k-NN) 0.119861 NaN Mean Imputation
34 k-Nearest Neighbors (k-NN) 0.570259 NaN Mean Imputation
35 k-Nearest Neighbors (k-NN) 0.143175 NaN Mean Imputation
36 k-Nearest Neighbors (k-NN) 0.140517 NaN Mean Imputation
37 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
38 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
39 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
40 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
41 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
42 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
43 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
44 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
45 k-Nearest Neighbors (k-NN) 0.687302 NaN Mean Imputation
46 k-Nearest Neighbors (k-NN) 0.066492 NaN Mean Imputation
47 k-Nearest Neighbors (k-NN) 0.387755 NaN Mean Imputation
48 k-Nearest Neighbors (k-NN) 0.113518 NaN Mean Imputation
49 k-Nearest Neighbors (k-NN) 0.552273 NaN Mean Imputation
50 k-Nearest Neighbors (k-NN) 0.091366 NaN Mean Imputation
51 k-Nearest Neighbors (k-NN) 0.104545 NaN Mean Imputation
52 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
53 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
54 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
55 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
56 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
57 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
58 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
59 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
60 k-Nearest Neighbors (k-NN) 0.666754 NaN Mean Imputation
61 k-Nearest Neighbors (k-NN) 0.072535 NaN Mean Imputation
62 k-Nearest Neighbors (k-NN) 0.412037 NaN Mean Imputation
63 k-Nearest Neighbors (k-NN) 0.123354 NaN Mean Imputation
64 k-Nearest Neighbors (k-NN) 0.568240 NaN Mean Imputation
65 k-Nearest Neighbors (k-NN) 0.110392 NaN Mean Imputation
66 k-Nearest Neighbors (k-NN) 0.136480 NaN Mean Imputation
67 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
68 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
69 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
70 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
71 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
72 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
73 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
74 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
1 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
2 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
3 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
4 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
5 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
6 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
7 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
8 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
9 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
10 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
11 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
12 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
13 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
14 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
15 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
16 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
17 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
18 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
19 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
20 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
21 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
22 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
23 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
24 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
25 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
26 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
27 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
28 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
29 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
30 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
31 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
32 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
33 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
34 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
35 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
36 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
37 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
38 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
39 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
40 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
41 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
42 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
43 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
44 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
45 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
46 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
47 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
48 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
49 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
50 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
51 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
52 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
53 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
54 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
55 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
56 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
57 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
58 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
59 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
60 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
61 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
62 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
63 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
64 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
65 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
66 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
67 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
68 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
69 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
70 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
71 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
72 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
73 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
74 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 k-Nearest Neighbors (k-NN) 0.671846 NaN Median Imputation
1 k-Nearest Neighbors (k-NN) 0.077117 NaN Median Imputation
2 k-Nearest Neighbors (k-NN) 0.388186 NaN Median Imputation
3 k-Nearest Neighbors (k-NN) 0.128671 NaN Median Imputation
4 k-Nearest Neighbors (k-NN) 0.544383 NaN Median Imputation
5 k-Nearest Neighbors (k-NN) 0.096267 NaN Median Imputation
6 k-Nearest Neighbors (k-NN) 0.088766 NaN Median Imputation
7 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
8 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
9 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
10 k-Nearest Neighbors (k-NN) NaN None Median Imputation
11 k-Nearest Neighbors (k-NN) NaN None Median Imputation
12 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
13 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
14 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
15 k-Nearest Neighbors (k-NN) 0.652620 NaN Median Imputation
16 k-Nearest Neighbors (k-NN) 0.054742 NaN Median Imputation
17 k-Nearest Neighbors (k-NN) 0.432927 NaN Median Imputation
18 k-Nearest Neighbors (k-NN) 0.097194 NaN Median Imputation
19 k-Nearest Neighbors (k-NN) 0.567693 NaN Median Imputation
20 k-Nearest Neighbors (k-NN) 0.122789 NaN Median Imputation
21 k-Nearest Neighbors (k-NN) 0.135387 NaN Median Imputation
22 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
23 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
24 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
25 k-Nearest Neighbors (k-NN) NaN None Median Imputation
26 k-Nearest Neighbors (k-NN) NaN None Median Imputation
27 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
28 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
29 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
30 k-Nearest Neighbors (k-NN) 0.661838 NaN Median Imputation
31 k-Nearest Neighbors (k-NN) 0.063644 NaN Median Imputation
32 k-Nearest Neighbors (k-NN) 0.427807 NaN Median Imputation
33 k-Nearest Neighbors (k-NN) 0.110803 NaN Median Imputation
34 k-Nearest Neighbors (k-NN) 0.561666 NaN Median Imputation
35 k-Nearest Neighbors (k-NN) 0.119676 NaN Median Imputation
36 k-Nearest Neighbors (k-NN) 0.123332 NaN Median Imputation
37 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
38 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
39 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
40 k-Nearest Neighbors (k-NN) NaN None Median Imputation
41 k-Nearest Neighbors (k-NN) NaN None Median Imputation
42 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
43 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
44 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
45 k-Nearest Neighbors (k-NN) 0.659905 NaN Median Imputation
46 k-Nearest Neighbors (k-NN) 0.065131 NaN Median Imputation
47 k-Nearest Neighbors (k-NN) 0.418367 NaN Median Imputation
48 k-Nearest Neighbors (k-NN) 0.112715 NaN Median Imputation
49 k-Nearest Neighbors (k-NN) 0.550923 NaN Median Imputation
50 k-Nearest Neighbors (k-NN) 0.097574 NaN Median Imputation
51 k-Nearest Neighbors (k-NN) 0.101845 NaN Median Imputation
52 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
53 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
54 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
55 k-Nearest Neighbors (k-NN) NaN None Median Imputation
56 k-Nearest Neighbors (k-NN) NaN None Median Imputation
57 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
58 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
59 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
60 k-Nearest Neighbors (k-NN) 0.670443 NaN Median Imputation
61 k-Nearest Neighbors (k-NN) 0.078240 NaN Median Imputation
62 k-Nearest Neighbors (k-NN) 0.444444 NaN Median Imputation
63 k-Nearest Neighbors (k-NN) 0.133056 NaN Median Imputation
64 k-Nearest Neighbors (k-NN) 0.576799 NaN Median Imputation
65 k-Nearest Neighbors (k-NN) 0.128523 NaN Median Imputation
66 k-Nearest Neighbors (k-NN) 0.153598 NaN Median Imputation
67 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
68 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
69 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
70 k-Nearest Neighbors (k-NN) NaN None Median Imputation
71 k-Nearest Neighbors (k-NN) NaN None Median Imputation
72 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
73 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
74 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
1 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
2 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
3 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
4 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
5 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
6 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
7 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
8 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
9 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
10 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
11 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
12 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
13 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
14 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
15 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
16 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
17 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
18 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
19 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
20 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
21 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
22 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
23 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
24 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
25 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
26 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
27 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
28 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
29 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
30 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
31 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
32 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
33 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
34 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
35 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
36 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
37 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
38 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
39 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
40 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
41 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
42 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
43 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
44 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
45 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
46 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
47 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
48 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
49 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
50 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
51 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
52 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
53 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
54 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
55 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
56 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
57 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
58 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
59 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
60 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
61 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
62 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
63 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
64 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
65 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
66 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
67 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
68 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
69 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
70 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
71 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
72 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
73 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
74 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 k-Nearest Neighbors (k-NN) 0.747169 NaN Zero Imputation
1 k-Nearest Neighbors (k-NN) 0.089671 NaN Zero Imputation
2 k-Nearest Neighbors (k-NN) 0.333333 NaN Zero Imputation
3 k-Nearest Neighbors (k-NN) 0.141324 NaN Zero Imputation
4 k-Nearest Neighbors (k-NN) 0.567490 NaN Zero Imputation
5 k-Nearest Neighbors (k-NN) 0.115785 NaN Zero Imputation
6 k-Nearest Neighbors (k-NN) 0.134980 NaN Zero Imputation
7 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
8 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
9 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
10 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
11 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
12 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
13 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
14 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
15 k-Nearest Neighbors (k-NN) 0.741638 NaN Zero Imputation
16 k-Nearest Neighbors (k-NN) 0.051592 NaN Zero Imputation
17 k-Nearest Neighbors (k-NN) 0.286585 NaN Zero Imputation
18 k-Nearest Neighbors (k-NN) 0.087442 NaN Zero Imputation
19 k-Nearest Neighbors (k-NN) 0.549695 NaN Zero Imputation
20 k-Nearest Neighbors (k-NN) 0.105746 NaN Zero Imputation
21 k-Nearest Neighbors (k-NN) 0.099390 NaN Zero Imputation
22 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
23 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
24 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
25 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
26 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
27 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
28 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
29 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
30 k-Nearest Neighbors (k-NN) 0.746379 NaN Zero Imputation
31 k-Nearest Neighbors (k-NN) 0.058087 NaN Zero Imputation
32 k-Nearest Neighbors (k-NN) 0.272727 NaN Zero Imputation
33 k-Nearest Neighbors (k-NN) 0.095775 NaN Zero Imputation
34 k-Nearest Neighbors (k-NN) 0.537049 NaN Zero Imputation
35 k-Nearest Neighbors (k-NN) 0.070071 NaN Zero Imputation
36 k-Nearest Neighbors (k-NN) 0.074099 NaN Zero Imputation
37 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
38 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
39 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
40 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
41 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
42 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
43 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
44 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
45 k-Nearest Neighbors (k-NN) 0.742097 NaN Zero Imputation
46 k-Nearest Neighbors (k-NN) 0.055619 NaN Zero Imputation
47 k-Nearest Neighbors (k-NN) 0.250000 NaN Zero Imputation
48 k-Nearest Neighbors (k-NN) 0.090994 NaN Zero Imputation
49 k-Nearest Neighbors (k-NN) 0.535064 NaN Zero Imputation
50 k-Nearest Neighbors (k-NN) 0.062880 NaN Zero Imputation
51 k-Nearest Neighbors (k-NN) 0.070129 NaN Zero Imputation
52 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
53 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
54 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
55 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
56 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
57 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
58 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
59 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
60 k-Nearest Neighbors (k-NN) 0.747629 NaN Zero Imputation
61 k-Nearest Neighbors (k-NN) 0.077449 NaN Zero Imputation
62 k-Nearest Neighbors (k-NN) 0.314815 NaN Zero Imputation
63 k-Nearest Neighbors (k-NN) 0.124314 NaN Zero Imputation
64 k-Nearest Neighbors (k-NN) 0.567306 NaN Zero Imputation
65 k-Nearest Neighbors (k-NN) 0.123831 NaN Zero Imputation
66 k-Nearest Neighbors (k-NN) 0.134611 NaN Zero Imputation
67 k-Nearest Neighbors (k-NN) NaN auto Zero Imputation
68 k-Nearest Neighbors (k-NN) NaN 30 Zero Imputation
69 k-Nearest Neighbors (k-NN) NaN minkowski Zero Imputation
70 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
71 k-Nearest Neighbors (k-NN) NaN None Zero Imputation
72 k-Nearest Neighbors (k-NN) NaN 5 Zero Imputation
73 k-Nearest Neighbors (k-NN) NaN 2 Zero Imputation
74 k-Nearest Neighbors (k-NN) NaN uniform Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
1 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
2 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
3 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
4 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
5 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
6 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
7 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
8 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
9 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
10 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
11 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
12 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
13 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
14 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
15 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
16 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
17 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
18 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
19 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
20 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
21 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
22 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
23 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
24 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
25 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
26 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
27 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
28 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
29 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
30 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
31 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
32 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
33 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
34 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
35 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
36 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
37 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
38 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
39 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
40 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
41 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
42 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
43 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
44 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
45 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
46 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
47 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
48 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
49 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
50 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
51 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
52 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
53 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
54 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
55 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
56 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
57 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
58 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
59 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
60 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
61 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
62 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
63 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
64 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
65 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
66 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
67 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
68 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
69 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
70 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
71 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
72 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
73 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
74 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 k-Nearest Neighbors (k-NN) 0.761917 NaN Mean Imputation
1 k-Nearest Neighbors (k-NN) 0.072983 NaN Mean Imputation
2 k-Nearest Neighbors (k-NN) 0.240506 NaN Mean Imputation
3 k-Nearest Neighbors (k-NN) 0.111984 NaN Mean Imputation
4 k-Nearest Neighbors (k-NN) 0.541348 NaN Mean Imputation
5 k-Nearest Neighbors (k-NN) 0.081288 NaN Mean Imputation
6 k-Nearest Neighbors (k-NN) 0.082696 NaN Mean Imputation
7 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
8 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
9 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
10 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
11 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
12 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
13 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
14 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
15 k-Nearest Neighbors (k-NN) 0.760600 NaN Mean Imputation
16 k-Nearest Neighbors (k-NN) 0.060213 NaN Mean Imputation
17 k-Nearest Neighbors (k-NN) 0.310976 NaN Mean Imputation
18 k-Nearest Neighbors (k-NN) 0.100890 NaN Mean Imputation
19 k-Nearest Neighbors (k-NN) 0.556115 NaN Mean Imputation
20 k-Nearest Neighbors (k-NN) 0.092544 NaN Mean Imputation
21 k-Nearest Neighbors (k-NN) 0.112230 NaN Mean Imputation
22 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
23 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
24 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
25 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
26 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
27 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
28 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
29 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
30 k-Nearest Neighbors (k-NN) 0.750329 NaN Mean Imputation
31 k-Nearest Neighbors (k-NN) 0.070056 NaN Mean Imputation
32 k-Nearest Neighbors (k-NN) 0.331551 NaN Mean Imputation
33 k-Nearest Neighbors (k-NN) 0.115672 NaN Mean Imputation
34 k-Nearest Neighbors (k-NN) 0.567855 NaN Mean Imputation
35 k-Nearest Neighbors (k-NN) 0.125149 NaN Mean Imputation
36 k-Nearest Neighbors (k-NN) 0.135710 NaN Mean Imputation
37 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
38 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
39 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
40 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
41 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
42 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
43 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
44 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
45 k-Nearest Neighbors (k-NN) 0.769231 NaN Mean Imputation
46 k-Nearest Neighbors (k-NN) 0.073935 NaN Mean Imputation
47 k-Nearest Neighbors (k-NN) 0.301020 NaN Mean Imputation
48 k-Nearest Neighbors (k-NN) 0.118712 NaN Mean Imputation
49 k-Nearest Neighbors (k-NN) 0.558555 NaN Mean Imputation
50 k-Nearest Neighbors (k-NN) 0.112432 NaN Mean Imputation
51 k-Nearest Neighbors (k-NN) 0.117110 NaN Mean Imputation
52 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
53 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
54 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
55 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
56 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
57 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
58 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
59 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
60 k-Nearest Neighbors (k-NN) 0.755005 NaN Mean Imputation
61 k-Nearest Neighbors (k-NN) 0.072967 NaN Mean Imputation
62 k-Nearest Neighbors (k-NN) 0.282407 NaN Mean Imputation
63 k-Nearest Neighbors (k-NN) 0.115970 NaN Mean Imputation
64 k-Nearest Neighbors (k-NN) 0.561117 NaN Mean Imputation
65 k-Nearest Neighbors (k-NN) 0.112813 NaN Mean Imputation
66 k-Nearest Neighbors (k-NN) 0.122234 NaN Mean Imputation
67 k-Nearest Neighbors (k-NN) NaN auto Mean Imputation
68 k-Nearest Neighbors (k-NN) NaN 30 Mean Imputation
69 k-Nearest Neighbors (k-NN) NaN minkowski Mean Imputation
70 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
71 k-Nearest Neighbors (k-NN) NaN None Mean Imputation
72 k-Nearest Neighbors (k-NN) NaN 5 Mean Imputation
73 k-Nearest Neighbors (k-NN) NaN 2 Mean Imputation
74 k-Nearest Neighbors (k-NN) NaN uniform Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
1 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
2 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
3 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
4 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
5 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
6 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
7 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
8 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
9 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
10 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
11 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
12 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
13 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
14 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
15 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
16 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
17 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
18 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
19 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
20 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
21 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
22 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
23 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
24 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
25 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
26 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
27 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
28 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
29 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
30 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
31 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
32 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
33 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
34 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
35 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
36 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
37 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
38 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
39 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
40 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
41 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
42 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
43 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
44 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
45 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
46 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
47 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
48 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
49 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
50 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
51 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
52 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
53 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
54 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
55 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
56 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
57 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
58 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
59 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
60 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
61 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
62 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
63 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
64 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
65 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
66 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
67 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
68 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
69 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
70 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
71 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
72 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
73 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
74 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 k-Nearest Neighbors (k-NN) 0.752436 NaN Median Imputation
1 k-Nearest Neighbors (k-NN) 0.080048 NaN Median Imputation
2 k-Nearest Neighbors (k-NN) 0.282700 NaN Median Imputation
3 k-Nearest Neighbors (k-NN) 0.124767 NaN Median Imputation
4 k-Nearest Neighbors (k-NN) 0.552447 NaN Median Imputation
5 k-Nearest Neighbors (k-NN) 0.093414 NaN Median Imputation
6 k-Nearest Neighbors (k-NN) 0.104894 NaN Median Imputation
7 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
8 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
9 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
10 k-Nearest Neighbors (k-NN) NaN None Median Imputation
11 k-Nearest Neighbors (k-NN) NaN None Median Imputation
12 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
13 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
14 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
15 k-Nearest Neighbors (k-NN) 0.746379 NaN Median Imputation
16 k-Nearest Neighbors (k-NN) 0.053631 NaN Median Imputation
17 k-Nearest Neighbors (k-NN) 0.292683 NaN Median Imputation
18 k-Nearest Neighbors (k-NN) 0.090652 NaN Median Imputation
19 k-Nearest Neighbors (k-NN) 0.540539 NaN Median Imputation
20 k-Nearest Neighbors (k-NN) 0.073481 NaN Median Imputation
21 k-Nearest Neighbors (k-NN) 0.081078 NaN Median Imputation
22 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
23 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
24 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
25 k-Nearest Neighbors (k-NN) NaN None Median Imputation
26 k-Nearest Neighbors (k-NN) NaN None Median Imputation
27 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
28 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
29 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
30 k-Nearest Neighbors (k-NN) 0.748222 NaN Median Imputation
31 k-Nearest Neighbors (k-NN) 0.061574 NaN Median Imputation
32 k-Nearest Neighbors (k-NN) 0.288770 NaN Median Imputation
33 k-Nearest Neighbors (k-NN) 0.101504 NaN Median Imputation
34 k-Nearest Neighbors (k-NN) 0.566274 NaN Median Imputation
35 k-Nearest Neighbors (k-NN) 0.131665 NaN Median Imputation
36 k-Nearest Neighbors (k-NN) 0.132548 NaN Median Imputation
37 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
38 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
39 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
40 k-Nearest Neighbors (k-NN) NaN None Median Imputation
41 k-Nearest Neighbors (k-NN) NaN None Median Imputation
42 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
43 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
44 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
45 k-Nearest Neighbors (k-NN) 0.750527 NaN Median Imputation
46 k-Nearest Neighbors (k-NN) 0.066897 NaN Median Imputation
47 k-Nearest Neighbors (k-NN) 0.295918 NaN Median Imputation
48 k-Nearest Neighbors (k-NN) 0.109125 NaN Median Imputation
49 k-Nearest Neighbors (k-NN) 0.562155 NaN Median Imputation
50 k-Nearest Neighbors (k-NN) 0.109603 NaN Median Imputation
51 k-Nearest Neighbors (k-NN) 0.124310 NaN Median Imputation
52 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
53 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
54 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
55 k-Nearest Neighbors (k-NN) NaN None Median Imputation
56 k-Nearest Neighbors (k-NN) NaN None Median Imputation
57 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
58 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
59 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
60 k-Nearest Neighbors (k-NN) 0.756322 NaN Median Imputation
61 k-Nearest Neighbors (k-NN) 0.080473 NaN Median Imputation
62 k-Nearest Neighbors (k-NN) 0.314815 NaN Median Imputation
63 k-Nearest Neighbors (k-NN) 0.128181 NaN Median Imputation
64 k-Nearest Neighbors (k-NN) 0.574015 NaN Median Imputation
65 k-Nearest Neighbors (k-NN) 0.132169 NaN Median Imputation
66 k-Nearest Neighbors (k-NN) 0.148030 NaN Median Imputation
67 k-Nearest Neighbors (k-NN) NaN auto Median Imputation
68 k-Nearest Neighbors (k-NN) NaN 30 Median Imputation
69 k-Nearest Neighbors (k-NN) NaN minkowski Median Imputation
70 k-Nearest Neighbors (k-NN) NaN None Median Imputation
71 k-Nearest Neighbors (k-NN) NaN None Median Imputation
72 k-Nearest Neighbors (k-NN) NaN 5 Median Imputation
73 k-Nearest Neighbors (k-NN) NaN 2 Median Imputation
74 k-Nearest Neighbors (k-NN) NaN uniform Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
1 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
2 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
3 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
4 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
5 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
6 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
7 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
8 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
9 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
10 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
11 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
12 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
13 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
14 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
15 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
16 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
17 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
18 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
19 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
20 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
21 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
22 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
23 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
24 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
25 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
26 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
27 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
28 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
29 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
30 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
31 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
32 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
33 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
34 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
35 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
36 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
37 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
38 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
39 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
40 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
41 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
42 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
43 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
44 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
45 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
46 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
47 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
48 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
49 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
50 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
51 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
52 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
53 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
54 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
55 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
56 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
57 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
58 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
59 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
60 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
61 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
62 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
63 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
64 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
65 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
66 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
67 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
68 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
69 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
70 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
71 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
72 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
73 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM...
74 k-Nearest Neighbors (k-NN)_Hybrid Sampling (SM... ,
Algorithm Metrics Hyperparameters \
0 Support Vector Machines (SVM) 0.491441 NaN
1 Support Vector Machines (SVM) 0.084396 NaN
2 Support Vector Machines (SVM) 0.725738 NaN
3 Support Vector Machines (SVM) 0.151209 NaN
4 Support Vector Machines (SVM) 0.639273 NaN
5 Support Vector Machines (SVM) 0.217580 NaN
6 Support Vector Machines (SVM) 0.278546 NaN
7 Support Vector Machines (SVM) NaN 1.0
8 Support Vector Machines (SVM) NaN False
9 Support Vector Machines (SVM) NaN 200
10 Support Vector Machines (SVM) NaN None
11 Support Vector Machines (SVM) NaN 0.0
12 Support Vector Machines (SVM) NaN ovr
13 Support Vector Machines (SVM) NaN 3
14 Support Vector Machines (SVM) NaN scale
15 Support Vector Machines (SVM) NaN rbf
16 Support Vector Machines (SVM) NaN -1
17 Support Vector Machines (SVM) NaN True
18 Support Vector Machines (SVM) NaN None
19 Support Vector Machines (SVM) NaN True
20 Support Vector Machines (SVM) NaN 0.001
21 Support Vector Machines (SVM) NaN False
22 Support Vector Machines (SVM) 0.480643 NaN
23 Support Vector Machines (SVM) 0.061591 NaN
24 Support Vector Machines (SVM) 0.774390 NaN
25 Support Vector Machines (SVM) 0.114106 NaN
26 Support Vector Machines (SVM) 0.681149 NaN
27 Support Vector Machines (SVM) 0.266733 NaN
28 Support Vector Machines (SVM) 0.362297 NaN
29 Support Vector Machines (SVM) NaN 1.0
30 Support Vector Machines (SVM) NaN False
31 Support Vector Machines (SVM) NaN 200
32 Support Vector Machines (SVM) NaN None
33 Support Vector Machines (SVM) NaN 0.0
34 Support Vector Machines (SVM) NaN ovr
35 Support Vector Machines (SVM) NaN 3
36 Support Vector Machines (SVM) NaN scale
37 Support Vector Machines (SVM) NaN rbf
38 Support Vector Machines (SVM) NaN -1
39 Support Vector Machines (SVM) NaN True
40 Support Vector Machines (SVM) NaN None
41 Support Vector Machines (SVM) NaN True
42 Support Vector Machines (SVM) NaN 0.001
43 Support Vector Machines (SVM) NaN False
44 Support Vector Machines (SVM) 0.470898 NaN
45 Support Vector Machines (SVM) 0.065777 NaN
46 Support Vector Machines (SVM) 0.737968 NaN
47 Support Vector Machines (SVM) 0.120788 NaN
48 Support Vector Machines (SVM) 0.656951 NaN
49 Support Vector Machines (SVM) 0.253474 NaN
50 Support Vector Machines (SVM) 0.313902 NaN
51 Support Vector Machines (SVM) NaN 1.0
52 Support Vector Machines (SVM) NaN False
53 Support Vector Machines (SVM) NaN 200
54 Support Vector Machines (SVM) NaN None
55 Support Vector Machines (SVM) NaN 0.0
56 Support Vector Machines (SVM) NaN ovr
57 Support Vector Machines (SVM) NaN 3
58 Support Vector Machines (SVM) NaN scale
59 Support Vector Machines (SVM) NaN rbf
60 Support Vector Machines (SVM) NaN -1
61 Support Vector Machines (SVM) NaN True
62 Support Vector Machines (SVM) NaN None
63 Support Vector Machines (SVM) NaN True
64 Support Vector Machines (SVM) NaN 0.001
65 Support Vector Machines (SVM) NaN False
66 Support Vector Machines (SVM) 0.467071 NaN
67 Support Vector Machines (SVM) 0.068085 NaN
68 Support Vector Machines (SVM) 0.734694 NaN
69 Support Vector Machines (SVM) 0.124621 NaN
70 Support Vector Machines (SVM) 0.638110 NaN
71 Support Vector Machines (SVM) 0.219008 NaN
72 Support Vector Machines (SVM) 0.276220 NaN
73 Support Vector Machines (SVM) NaN 1.0
74 Support Vector Machines (SVM) NaN False
75 Support Vector Machines (SVM) NaN 200
76 Support Vector Machines (SVM) NaN None
77 Support Vector Machines (SVM) NaN 0.0
78 Support Vector Machines (SVM) NaN ovr
79 Support Vector Machines (SVM) NaN 3
80 Support Vector Machines (SVM) NaN scale
81 Support Vector Machines (SVM) NaN rbf
82 Support Vector Machines (SVM) NaN -1
83 Support Vector Machines (SVM) NaN True
84 Support Vector Machines (SVM) NaN None
85 Support Vector Machines (SVM) NaN True
86 Support Vector Machines (SVM) NaN 0.001
87 Support Vector Machines (SVM) NaN False
88 Support Vector Machines (SVM) 0.494731 NaN
89 Support Vector Machines (SVM) 0.081201 NaN
90 Support Vector Machines (SVM) 0.763889 NaN
91 Support Vector Machines (SVM) 0.146797 NaN
92 Support Vector Machines (SVM) 0.670149 NaN
93 Support Vector Machines (SVM) 0.271586 NaN
94 Support Vector Machines (SVM) 0.340297 NaN
95 Support Vector Machines (SVM) NaN 1.0
96 Support Vector Machines (SVM) NaN False
97 Support Vector Machines (SVM) NaN 200
98 Support Vector Machines (SVM) NaN None
99 Support Vector Machines (SVM) NaN 0.0
100 Support Vector Machines (SVM) NaN ovr
101 Support Vector Machines (SVM) NaN 3
102 Support Vector Machines (SVM) NaN scale
103 Support Vector Machines (SVM) NaN rbf
104 Support Vector Machines (SVM) NaN -1
105 Support Vector Machines (SVM) NaN True
106 Support Vector Machines (SVM) NaN None
107 Support Vector Machines (SVM) NaN True
108 Support Vector Machines (SVM) NaN 0.001
109 Support Vector Machines (SVM) NaN False
Imputation Technique Imbalance Class Technique \
0 Zero Imputation Oversampling
1 Zero Imputation Oversampling
2 Zero Imputation Oversampling
3 Zero Imputation Oversampling
4 Zero Imputation Oversampling
5 Zero Imputation Oversampling
6 Zero Imputation Oversampling
7 Zero Imputation Oversampling
8 Zero Imputation Oversampling
9 Zero Imputation Oversampling
10 Zero Imputation Oversampling
11 Zero Imputation Oversampling
12 Zero Imputation Oversampling
13 Zero Imputation Oversampling
14 Zero Imputation Oversampling
15 Zero Imputation Oversampling
16 Zero Imputation Oversampling
17 Zero Imputation Oversampling
18 Zero Imputation Oversampling
19 Zero Imputation Oversampling
20 Zero Imputation Oversampling
21 Zero Imputation Oversampling
22 Zero Imputation Oversampling
23 Zero Imputation Oversampling
24 Zero Imputation Oversampling
25 Zero Imputation Oversampling
26 Zero Imputation Oversampling
27 Zero Imputation Oversampling
28 Zero Imputation Oversampling
29 Zero Imputation Oversampling
30 Zero Imputation Oversampling
31 Zero Imputation Oversampling
32 Zero Imputation Oversampling
33 Zero Imputation Oversampling
34 Zero Imputation Oversampling
35 Zero Imputation Oversampling
36 Zero Imputation Oversampling
37 Zero Imputation Oversampling
38 Zero Imputation Oversampling
39 Zero Imputation Oversampling
40 Zero Imputation Oversampling
41 Zero Imputation Oversampling
42 Zero Imputation Oversampling
43 Zero Imputation Oversampling
44 Zero Imputation Oversampling
45 Zero Imputation Oversampling
46 Zero Imputation Oversampling
47 Zero Imputation Oversampling
48 Zero Imputation Oversampling
49 Zero Imputation Oversampling
50 Zero Imputation Oversampling
51 Zero Imputation Oversampling
52 Zero Imputation Oversampling
53 Zero Imputation Oversampling
54 Zero Imputation Oversampling
55 Zero Imputation Oversampling
56 Zero Imputation Oversampling
57 Zero Imputation Oversampling
58 Zero Imputation Oversampling
59 Zero Imputation Oversampling
60 Zero Imputation Oversampling
61 Zero Imputation Oversampling
62 Zero Imputation Oversampling
63 Zero Imputation Oversampling
64 Zero Imputation Oversampling
65 Zero Imputation Oversampling
66 Zero Imputation Oversampling
67 Zero Imputation Oversampling
68 Zero Imputation Oversampling
69 Zero Imputation Oversampling
70 Zero Imputation Oversampling
71 Zero Imputation Oversampling
72 Zero Imputation Oversampling
73 Zero Imputation Oversampling
74 Zero Imputation Oversampling
75 Zero Imputation Oversampling
76 Zero Imputation Oversampling
77 Zero Imputation Oversampling
78 Zero Imputation Oversampling
79 Zero Imputation Oversampling
80 Zero Imputation Oversampling
81 Zero Imputation Oversampling
82 Zero Imputation Oversampling
83 Zero Imputation Oversampling
84 Zero Imputation Oversampling
85 Zero Imputation Oversampling
86 Zero Imputation Oversampling
87 Zero Imputation Oversampling
88 Zero Imputation Oversampling
89 Zero Imputation Oversampling
90 Zero Imputation Oversampling
91 Zero Imputation Oversampling
92 Zero Imputation Oversampling
93 Zero Imputation Oversampling
94 Zero Imputation Oversampling
95 Zero Imputation Oversampling
96 Zero Imputation Oversampling
97 Zero Imputation Oversampling
98 Zero Imputation Oversampling
99 Zero Imputation Oversampling
100 Zero Imputation Oversampling
101 Zero Imputation Oversampling
102 Zero Imputation Oversampling
103 Zero Imputation Oversampling
104 Zero Imputation Oversampling
105 Zero Imputation Oversampling
106 Zero Imputation Oversampling
107 Zero Imputation Oversampling
108 Zero Imputation Oversampling
109 Zero Imputation Oversampling
Model Unique Code
0 Support Vector Machines (SVM)_Oversampling_Zer...
1 Support Vector Machines (SVM)_Oversampling_Zer...
2 Support Vector Machines (SVM)_Oversampling_Zer...
3 Support Vector Machines (SVM)_Oversampling_Zer...
4 Support Vector Machines (SVM)_Oversampling_Zer...
5 Support Vector Machines (SVM)_Oversampling_Zer...
6 Support Vector Machines (SVM)_Oversampling_Zer...
7 Support Vector Machines (SVM)_Oversampling_Zer...
8 Support Vector Machines (SVM)_Oversampling_Zer...
9 Support Vector Machines (SVM)_Oversampling_Zer...
10 Support Vector Machines (SVM)_Oversampling_Zer...
11 Support Vector Machines (SVM)_Oversampling_Zer...
12 Support Vector Machines (SVM)_Oversampling_Zer...
13 Support Vector Machines (SVM)_Oversampling_Zer...
14 Support Vector Machines (SVM)_Oversampling_Zer...
15 Support Vector Machines (SVM)_Oversampling_Zer...
16 Support Vector Machines (SVM)_Oversampling_Zer...
17 Support Vector Machines (SVM)_Oversampling_Zer...
18 Support Vector Machines (SVM)_Oversampling_Zer...
19 Support Vector Machines (SVM)_Oversampling_Zer...
20 Support Vector Machines (SVM)_Oversampling_Zer...
21 Support Vector Machines (SVM)_Oversampling_Zer...
22 Support Vector Machines (SVM)_Oversampling_Zer...
23 Support Vector Machines (SVM)_Oversampling_Zer...
24 Support Vector Machines (SVM)_Oversampling_Zer...
25 Support Vector Machines (SVM)_Oversampling_Zer...
26 Support Vector Machines (SVM)_Oversampling_Zer...
27 Support Vector Machines (SVM)_Oversampling_Zer...
28 Support Vector Machines (SVM)_Oversampling_Zer...
29 Support Vector Machines (SVM)_Oversampling_Zer...
30 Support Vector Machines (SVM)_Oversampling_Zer...
31 Support Vector Machines (SVM)_Oversampling_Zer...
32 Support Vector Machines (SVM)_Oversampling_Zer...
33 Support Vector Machines (SVM)_Oversampling_Zer...
34 Support Vector Machines (SVM)_Oversampling_Zer...
35 Support Vector Machines (SVM)_Oversampling_Zer...
36 Support Vector Machines (SVM)_Oversampling_Zer...
37 Support Vector Machines (SVM)_Oversampling_Zer...
38 Support Vector Machines (SVM)_Oversampling_Zer...
39 Support Vector Machines (SVM)_Oversampling_Zer...
40 Support Vector Machines (SVM)_Oversampling_Zer...
41 Support Vector Machines (SVM)_Oversampling_Zer...
42 Support Vector Machines (SVM)_Oversampling_Zer...
43 Support Vector Machines (SVM)_Oversampling_Zer...
44 Support Vector Machines (SVM)_Oversampling_Zer...
45 Support Vector Machines (SVM)_Oversampling_Zer...
46 Support Vector Machines (SVM)_Oversampling_Zer...
47 Support Vector Machines (SVM)_Oversampling_Zer...
48 Support Vector Machines (SVM)_Oversampling_Zer...
49 Support Vector Machines (SVM)_Oversampling_Zer...
50 Support Vector Machines (SVM)_Oversampling_Zer...
51 Support Vector Machines (SVM)_Oversampling_Zer...
52 Support Vector Machines (SVM)_Oversampling_Zer...
53 Support Vector Machines (SVM)_Oversampling_Zer...
54 Support Vector Machines (SVM)_Oversampling_Zer...
55 Support Vector Machines (SVM)_Oversampling_Zer...
56 Support Vector Machines (SVM)_Oversampling_Zer...
57 Support Vector Machines (SVM)_Oversampling_Zer...
58 Support Vector Machines (SVM)_Oversampling_Zer...
59 Support Vector Machines (SVM)_Oversampling_Zer...
60 Support Vector Machines (SVM)_Oversampling_Zer...
61 Support Vector Machines (SVM)_Oversampling_Zer...
62 Support Vector Machines (SVM)_Oversampling_Zer...
63 Support Vector Machines (SVM)_Oversampling_Zer...
64 Support Vector Machines (SVM)_Oversampling_Zer...
65 Support Vector Machines (SVM)_Oversampling_Zer...
66 Support Vector Machines (SVM)_Oversampling_Zer...
67 Support Vector Machines (SVM)_Oversampling_Zer...
68 Support Vector Machines (SVM)_Oversampling_Zer...
69 Support Vector Machines (SVM)_Oversampling_Zer...
70 Support Vector Machines (SVM)_Oversampling_Zer...
71 Support Vector Machines (SVM)_Oversampling_Zer...
72 Support Vector Machines (SVM)_Oversampling_Zer...
73 Support Vector Machines (SVM)_Oversampling_Zer...
74 Support Vector Machines (SVM)_Oversampling_Zer...
75 Support Vector Machines (SVM)_Oversampling_Zer...
76 Support Vector Machines (SVM)_Oversampling_Zer...
77 Support Vector Machines (SVM)_Oversampling_Zer...
78 Support Vector Machines (SVM)_Oversampling_Zer...
79 Support Vector Machines (SVM)_Oversampling_Zer...
80 Support Vector Machines (SVM)_Oversampling_Zer...
81 Support Vector Machines (SVM)_Oversampling_Zer...
82 Support Vector Machines (SVM)_Oversampling_Zer...
83 Support Vector Machines (SVM)_Oversampling_Zer...
84 Support Vector Machines (SVM)_Oversampling_Zer...
85 Support Vector Machines (SVM)_Oversampling_Zer...
86 Support Vector Machines (SVM)_Oversampling_Zer...
87 Support Vector Machines (SVM)_Oversampling_Zer...
88 Support Vector Machines (SVM)_Oversampling_Zer...
89 Support Vector Machines (SVM)_Oversampling_Zer...
90 Support Vector Machines (SVM)_Oversampling_Zer...
91 Support Vector Machines (SVM)_Oversampling_Zer...
92 Support Vector Machines (SVM)_Oversampling_Zer...
93 Support Vector Machines (SVM)_Oversampling_Zer...
94 Support Vector Machines (SVM)_Oversampling_Zer...
95 Support Vector Machines (SVM)_Oversampling_Zer...
96 Support Vector Machines (SVM)_Oversampling_Zer...
97 Support Vector Machines (SVM)_Oversampling_Zer...
98 Support Vector Machines (SVM)_Oversampling_Zer...
99 Support Vector Machines (SVM)_Oversampling_Zer...
100 Support Vector Machines (SVM)_Oversampling_Zer...
101 Support Vector Machines (SVM)_Oversampling_Zer...
102 Support Vector Machines (SVM)_Oversampling_Zer...
103 Support Vector Machines (SVM)_Oversampling_Zer...
104 Support Vector Machines (SVM)_Oversampling_Zer...
105 Support Vector Machines (SVM)_Oversampling_Zer...
106 Support Vector Machines (SVM)_Oversampling_Zer...
107 Support Vector Machines (SVM)_Oversampling_Zer...
108 Support Vector Machines (SVM)_Oversampling_Zer...
109 Support Vector Machines (SVM)_Oversampling_Zer... ,
Algorithm Metrics Hyperparameters \
0 Support Vector Machines (SVM) 0.454569 NaN
1 Support Vector Machines (SVM) 0.073091 NaN
2 Support Vector Machines (SVM) 0.662447 NaN
3 Support Vector Machines (SVM) 0.131656 NaN
4 Support Vector Machines (SVM) 0.588358 NaN
5 Support Vector Machines (SVM) 0.156935 NaN
6 Support Vector Machines (SVM) 0.176716 NaN
7 Support Vector Machines (SVM) NaN 1.0
8 Support Vector Machines (SVM) NaN False
9 Support Vector Machines (SVM) NaN 200
10 Support Vector Machines (SVM) NaN None
11 Support Vector Machines (SVM) NaN 0.0
12 Support Vector Machines (SVM) NaN ovr
13 Support Vector Machines (SVM) NaN 3
14 Support Vector Machines (SVM) NaN scale
15 Support Vector Machines (SVM) NaN rbf
16 Support Vector Machines (SVM) NaN -1
17 Support Vector Machines (SVM) NaN True
18 Support Vector Machines (SVM) NaN None
19 Support Vector Machines (SVM) NaN True
20 Support Vector Machines (SVM) NaN 0.001
21 Support Vector Machines (SVM) NaN False
22 Support Vector Machines (SVM) 0.331841 NaN
23 Support Vector Machines (SVM) 0.053107 NaN
24 Support Vector Machines (SVM) 0.859756 NaN
25 Support Vector Machines (SVM) 0.100035 NaN
26 Support Vector Machines (SVM) 0.629368 NaN
27 Support Vector Machines (SVM) 0.243718 NaN
28 Support Vector Machines (SVM) 0.258736 NaN
29 Support Vector Machines (SVM) NaN 1.0
30 Support Vector Machines (SVM) NaN False
31 Support Vector Machines (SVM) NaN 200
32 Support Vector Machines (SVM) NaN None
33 Support Vector Machines (SVM) NaN 0.0
34 Support Vector Machines (SVM) NaN ovr
35 Support Vector Machines (SVM) NaN 3
36 Support Vector Machines (SVM) NaN scale
37 Support Vector Machines (SVM) NaN rbf
38 Support Vector Machines (SVM) NaN -1
39 Support Vector Machines (SVM) NaN True
40 Support Vector Machines (SVM) NaN None
41 Support Vector Machines (SVM) NaN True
42 Support Vector Machines (SVM) NaN 0.001
43 Support Vector Machines (SVM) NaN False
44 Support Vector Machines (SVM) 0.343166 NaN
45 Support Vector Machines (SVM) 0.058891 NaN
46 Support Vector Machines (SVM) 0.823529 NaN
47 Support Vector Machines (SVM) 0.109921 NaN
48 Support Vector Machines (SVM) 0.620919 NaN
49 Support Vector Machines (SVM) 0.209755 NaN
50 Support Vector Machines (SVM) 0.241839 NaN
51 Support Vector Machines (SVM) NaN 1.0
52 Support Vector Machines (SVM) NaN False
53 Support Vector Machines (SVM) NaN 200
54 Support Vector Machines (SVM) NaN None
55 Support Vector Machines (SVM) NaN 0.0
56 Support Vector Machines (SVM) NaN ovr
57 Support Vector Machines (SVM) NaN 3
58 Support Vector Machines (SVM) NaN scale
59 Support Vector Machines (SVM) NaN rbf
60 Support Vector Machines (SVM) NaN -1
61 Support Vector Machines (SVM) NaN True
62 Support Vector Machines (SVM) NaN None
63 Support Vector Machines (SVM) NaN True
64 Support Vector Machines (SVM) NaN 0.001
65 Support Vector Machines (SVM) NaN False
66 Support Vector Machines (SVM) 0.272919 NaN
67 Support Vector Machines (SVM) 0.060658 NaN
68 Support Vector Machines (SVM) 0.903061 NaN
69 Support Vector Machines (SVM) 0.113680 NaN
70 Support Vector Machines (SVM) 0.628517 NaN
71 Support Vector Machines (SVM) 0.232415 NaN
72 Support Vector Machines (SVM) 0.257034 NaN
73 Support Vector Machines (SVM) NaN 1.0
74 Support Vector Machines (SVM) NaN False
75 Support Vector Machines (SVM) NaN 200
76 Support Vector Machines (SVM) NaN None
77 Support Vector Machines (SVM) NaN 0.0
78 Support Vector Machines (SVM) NaN ovr
79 Support Vector Machines (SVM) NaN 3
80 Support Vector Machines (SVM) NaN scale
81 Support Vector Machines (SVM) NaN rbf
82 Support Vector Machines (SVM) NaN -1
83 Support Vector Machines (SVM) NaN True
84 Support Vector Machines (SVM) NaN None
85 Support Vector Machines (SVM) NaN True
86 Support Vector Machines (SVM) NaN 0.001
87 Support Vector Machines (SVM) NaN False
88 Support Vector Machines (SVM) 0.413593 NaN
89 Support Vector Machines (SVM) 0.070880 NaN
90 Support Vector Machines (SVM) 0.768519 NaN
91 Support Vector Machines (SVM) 0.129789 NaN
92 Support Vector Machines (SVM) 0.633570 NaN
93 Support Vector Machines (SVM) 0.212239 NaN
94 Support Vector Machines (SVM) 0.267140 NaN
95 Support Vector Machines (SVM) NaN 1.0
96 Support Vector Machines (SVM) NaN False
97 Support Vector Machines (SVM) NaN 200
98 Support Vector Machines (SVM) NaN None
99 Support Vector Machines (SVM) NaN 0.0
100 Support Vector Machines (SVM) NaN ovr
101 Support Vector Machines (SVM) NaN 3
102 Support Vector Machines (SVM) NaN scale
103 Support Vector Machines (SVM) NaN rbf
104 Support Vector Machines (SVM) NaN -1
105 Support Vector Machines (SVM) NaN True
106 Support Vector Machines (SVM) NaN None
107 Support Vector Machines (SVM) NaN True
108 Support Vector Machines (SVM) NaN 0.001
109 Support Vector Machines (SVM) NaN False
Imputation Technique Imbalance Class Technique \
0 Mean Imputation Oversampling
1 Mean Imputation Oversampling
2 Mean Imputation Oversampling
3 Mean Imputation Oversampling
4 Mean Imputation Oversampling
5 Mean Imputation Oversampling
6 Mean Imputation Oversampling
7 Mean Imputation Oversampling
8 Mean Imputation Oversampling
9 Mean Imputation Oversampling
10 Mean Imputation Oversampling
11 Mean Imputation Oversampling
12 Mean Imputation Oversampling
13 Mean Imputation Oversampling
14 Mean Imputation Oversampling
15 Mean Imputation Oversampling
16 Mean Imputation Oversampling
17 Mean Imputation Oversampling
18 Mean Imputation Oversampling
19 Mean Imputation Oversampling
20 Mean Imputation Oversampling
21 Mean Imputation Oversampling
22 Mean Imputation Oversampling
23 Mean Imputation Oversampling
24 Mean Imputation Oversampling
25 Mean Imputation Oversampling
26 Mean Imputation Oversampling
27 Mean Imputation Oversampling
28 Mean Imputation Oversampling
29 Mean Imputation Oversampling
30 Mean Imputation Oversampling
31 Mean Imputation Oversampling
32 Mean Imputation Oversampling
33 Mean Imputation Oversampling
34 Mean Imputation Oversampling
35 Mean Imputation Oversampling
36 Mean Imputation Oversampling
37 Mean Imputation Oversampling
38 Mean Imputation Oversampling
39 Mean Imputation Oversampling
40 Mean Imputation Oversampling
41 Mean Imputation Oversampling
42 Mean Imputation Oversampling
43 Mean Imputation Oversampling
44 Mean Imputation Oversampling
45 Mean Imputation Oversampling
46 Mean Imputation Oversampling
47 Mean Imputation Oversampling
48 Mean Imputation Oversampling
49 Mean Imputation Oversampling
50 Mean Imputation Oversampling
51 Mean Imputation Oversampling
52 Mean Imputation Oversampling
53 Mean Imputation Oversampling
54 Mean Imputation Oversampling
55 Mean Imputation Oversampling
56 Mean Imputation Oversampling
57 Mean Imputation Oversampling
58 Mean Imputation Oversampling
59 Mean Imputation Oversampling
60 Mean Imputation Oversampling
61 Mean Imputation Oversampling
62 Mean Imputation Oversampling
63 Mean Imputation Oversampling
64 Mean Imputation Oversampling
65 Mean Imputation Oversampling
66 Mean Imputation Oversampling
67 Mean Imputation Oversampling
68 Mean Imputation Oversampling
69 Mean Imputation Oversampling
70 Mean Imputation Oversampling
71 Mean Imputation Oversampling
72 Mean Imputation Oversampling
73 Mean Imputation Oversampling
74 Mean Imputation Oversampling
75 Mean Imputation Oversampling
76 Mean Imputation Oversampling
77 Mean Imputation Oversampling
78 Mean Imputation Oversampling
79 Mean Imputation Oversampling
80 Mean Imputation Oversampling
81 Mean Imputation Oversampling
82 Mean Imputation Oversampling
83 Mean Imputation Oversampling
84 Mean Imputation Oversampling
85 Mean Imputation Oversampling
86 Mean Imputation Oversampling
87 Mean Imputation Oversampling
88 Mean Imputation Oversampling
89 Mean Imputation Oversampling
90 Mean Imputation Oversampling
91 Mean Imputation Oversampling
92 Mean Imputation Oversampling
93 Mean Imputation Oversampling
94 Mean Imputation Oversampling
95 Mean Imputation Oversampling
96 Mean Imputation Oversampling
97 Mean Imputation Oversampling
98 Mean Imputation Oversampling
99 Mean Imputation Oversampling
100 Mean Imputation Oversampling
101 Mean Imputation Oversampling
102 Mean Imputation Oversampling
103 Mean Imputation Oversampling
104 Mean Imputation Oversampling
105 Mean Imputation Oversampling
106 Mean Imputation Oversampling
107 Mean Imputation Oversampling
108 Mean Imputation Oversampling
109 Mean Imputation Oversampling
Model Unique Code
0 Support Vector Machines (SVM)_Oversampling_Mea...
1 Support Vector Machines (SVM)_Oversampling_Mea...
2 Support Vector Machines (SVM)_Oversampling_Mea...
3 Support Vector Machines (SVM)_Oversampling_Mea...
4 Support Vector Machines (SVM)_Oversampling_Mea...
5 Support Vector Machines (SVM)_Oversampling_Mea...
6 Support Vector Machines (SVM)_Oversampling_Mea...
7 Support Vector Machines (SVM)_Oversampling_Mea...
8 Support Vector Machines (SVM)_Oversampling_Mea...
9 Support Vector Machines (SVM)_Oversampling_Mea...
10 Support Vector Machines (SVM)_Oversampling_Mea...
11 Support Vector Machines (SVM)_Oversampling_Mea...
12 Support Vector Machines (SVM)_Oversampling_Mea...
13 Support Vector Machines (SVM)_Oversampling_Mea...
14 Support Vector Machines (SVM)_Oversampling_Mea...
15 Support Vector Machines (SVM)_Oversampling_Mea...
16 Support Vector Machines (SVM)_Oversampling_Mea...
17 Support Vector Machines (SVM)_Oversampling_Mea...
18 Support Vector Machines (SVM)_Oversampling_Mea...
19 Support Vector Machines (SVM)_Oversampling_Mea...
20 Support Vector Machines (SVM)_Oversampling_Mea...
21 Support Vector Machines (SVM)_Oversampling_Mea...
22 Support Vector Machines (SVM)_Oversampling_Mea...
23 Support Vector Machines (SVM)_Oversampling_Mea...
24 Support Vector Machines (SVM)_Oversampling_Mea...
25 Support Vector Machines (SVM)_Oversampling_Mea...
26 Support Vector Machines (SVM)_Oversampling_Mea...
27 Support Vector Machines (SVM)_Oversampling_Mea...
28 Support Vector Machines (SVM)_Oversampling_Mea...
29 Support Vector Machines (SVM)_Oversampling_Mea...
30 Support Vector Machines (SVM)_Oversampling_Mea...
31 Support Vector Machines (SVM)_Oversampling_Mea...
32 Support Vector Machines (SVM)_Oversampling_Mea...
33 Support Vector Machines (SVM)_Oversampling_Mea...
34 Support Vector Machines (SVM)_Oversampling_Mea...
35 Support Vector Machines (SVM)_Oversampling_Mea...
36 Support Vector Machines (SVM)_Oversampling_Mea...
37 Support Vector Machines (SVM)_Oversampling_Mea...
38 Support Vector Machines (SVM)_Oversampling_Mea...
39 Support Vector Machines (SVM)_Oversampling_Mea...
40 Support Vector Machines (SVM)_Oversampling_Mea...
41 Support Vector Machines (SVM)_Oversampling_Mea...
42 Support Vector Machines (SVM)_Oversampling_Mea...
43 Support Vector Machines (SVM)_Oversampling_Mea...
44 Support Vector Machines (SVM)_Oversampling_Mea...
45 Support Vector Machines (SVM)_Oversampling_Mea...
46 Support Vector Machines (SVM)_Oversampling_Mea...
47 Support Vector Machines (SVM)_Oversampling_Mea...
48 Support Vector Machines (SVM)_Oversampling_Mea...
49 Support Vector Machines (SVM)_Oversampling_Mea...
50 Support Vector Machines (SVM)_Oversampling_Mea...
51 Support Vector Machines (SVM)_Oversampling_Mea...
52 Support Vector Machines (SVM)_Oversampling_Mea...
53 Support Vector Machines (SVM)_Oversampling_Mea...
54 Support Vector Machines (SVM)_Oversampling_Mea...
55 Support Vector Machines (SVM)_Oversampling_Mea...
56 Support Vector Machines (SVM)_Oversampling_Mea...
57 Support Vector Machines (SVM)_Oversampling_Mea...
58 Support Vector Machines (SVM)_Oversampling_Mea...
59 Support Vector Machines (SVM)_Oversampling_Mea...
60 Support Vector Machines (SVM)_Oversampling_Mea...
61 Support Vector Machines (SVM)_Oversampling_Mea...
62 Support Vector Machines (SVM)_Oversampling_Mea...
63 Support Vector Machines (SVM)_Oversampling_Mea...
64 Support Vector Machines (SVM)_Oversampling_Mea...
65 Support Vector Machines (SVM)_Oversampling_Mea...
66 Support Vector Machines (SVM)_Oversampling_Mea...
67 Support Vector Machines (SVM)_Oversampling_Mea...
68 Support Vector Machines (SVM)_Oversampling_Mea...
69 Support Vector Machines (SVM)_Oversampling_Mea...
70 Support Vector Machines (SVM)_Oversampling_Mea...
71 Support Vector Machines (SVM)_Oversampling_Mea...
72 Support Vector Machines (SVM)_Oversampling_Mea...
73 Support Vector Machines (SVM)_Oversampling_Mea...
74 Support Vector Machines (SVM)_Oversampling_Mea...
75 Support Vector Machines (SVM)_Oversampling_Mea...
76 Support Vector Machines (SVM)_Oversampling_Mea...
77 Support Vector Machines (SVM)_Oversampling_Mea...
78 Support Vector Machines (SVM)_Oversampling_Mea...
79 Support Vector Machines (SVM)_Oversampling_Mea...
80 Support Vector Machines (SVM)_Oversampling_Mea...
81 Support Vector Machines (SVM)_Oversampling_Mea...
82 Support Vector Machines (SVM)_Oversampling_Mea...
83 Support Vector Machines (SVM)_Oversampling_Mea...
84 Support Vector Machines (SVM)_Oversampling_Mea...
85 Support Vector Machines (SVM)_Oversampling_Mea...
86 Support Vector Machines (SVM)_Oversampling_Mea...
87 Support Vector Machines (SVM)_Oversampling_Mea...
88 Support Vector Machines (SVM)_Oversampling_Mea...
89 Support Vector Machines (SVM)_Oversampling_Mea...
90 Support Vector Machines (SVM)_Oversampling_Mea...
91 Support Vector Machines (SVM)_Oversampling_Mea...
92 Support Vector Machines (SVM)_Oversampling_Mea...
93 Support Vector Machines (SVM)_Oversampling_Mea...
94 Support Vector Machines (SVM)_Oversampling_Mea...
95 Support Vector Machines (SVM)_Oversampling_Mea...
96 Support Vector Machines (SVM)_Oversampling_Mea...
97 Support Vector Machines (SVM)_Oversampling_Mea...
98 Support Vector Machines (SVM)_Oversampling_Mea...
99 Support Vector Machines (SVM)_Oversampling_Mea...
100 Support Vector Machines (SVM)_Oversampling_Mea...
101 Support Vector Machines (SVM)_Oversampling_Mea...
102 Support Vector Machines (SVM)_Oversampling_Mea...
103 Support Vector Machines (SVM)_Oversampling_Mea...
104 Support Vector Machines (SVM)_Oversampling_Mea...
105 Support Vector Machines (SVM)_Oversampling_Mea...
106 Support Vector Machines (SVM)_Oversampling_Mea...
107 Support Vector Machines (SVM)_Oversampling_Mea...
108 Support Vector Machines (SVM)_Oversampling_Mea...
109 Support Vector Machines (SVM)_Oversampling_Mea... ,
Algorithm Metrics Hyperparameters \
0 Support Vector Machines (SVM) 0.405583 NaN
1 Support Vector Machines (SVM) 0.080565 NaN
2 Support Vector Machines (SVM) 0.818565 NaN
3 Support Vector Machines (SVM) 0.146692 NaN
4 Support Vector Machines (SVM) 0.643815 NaN
5 Support Vector Machines (SVM) 0.233736 NaN
6 Support Vector Machines (SVM) 0.287630 NaN
7 Support Vector Machines (SVM) NaN 1.0
8 Support Vector Machines (SVM) NaN False
9 Support Vector Machines (SVM) NaN 200
10 Support Vector Machines (SVM) NaN None
11 Support Vector Machines (SVM) NaN 0.0
12 Support Vector Machines (SVM) NaN ovr
13 Support Vector Machines (SVM) NaN 3
14 Support Vector Machines (SVM) NaN scale
15 Support Vector Machines (SVM) NaN rbf
16 Support Vector Machines (SVM) NaN -1
17 Support Vector Machines (SVM) NaN True
18 Support Vector Machines (SVM) NaN None
19 Support Vector Machines (SVM) NaN True
20 Support Vector Machines (SVM) NaN 0.001
21 Support Vector Machines (SVM) NaN False
22 Support Vector Machines (SVM) 0.383461 NaN
23 Support Vector Machines (SVM) 0.055533 NaN
24 Support Vector Machines (SVM) 0.829268 NaN
25 Support Vector Machines (SVM) 0.104095 NaN
26 Support Vector Machines (SVM) 0.660595 NaN
27 Support Vector Machines (SVM) 0.288291 NaN
28 Support Vector Machines (SVM) 0.321190 NaN
29 Support Vector Machines (SVM) NaN 1.0
30 Support Vector Machines (SVM) NaN False
31 Support Vector Machines (SVM) NaN 200
32 Support Vector Machines (SVM) NaN None
33 Support Vector Machines (SVM) NaN 0.0
34 Support Vector Machines (SVM) NaN ovr
35 Support Vector Machines (SVM) NaN 3
36 Support Vector Machines (SVM) NaN scale
37 Support Vector Machines (SVM) NaN rbf
38 Support Vector Machines (SVM) NaN -1
39 Support Vector Machines (SVM) NaN True
40 Support Vector Machines (SVM) NaN None
41 Support Vector Machines (SVM) NaN True
42 Support Vector Machines (SVM) NaN 0.001
43 Support Vector Machines (SVM) NaN False
44 Support Vector Machines (SVM) 0.378720 NaN
45 Support Vector Machines (SVM) 0.062450 NaN
46 Support Vector Machines (SVM) 0.828877 NaN
47 Support Vector Machines (SVM) 0.116148 NaN
48 Support Vector Machines (SVM) 0.657651 NaN
49 Support Vector Machines (SVM) 0.241381 NaN
50 Support Vector Machines (SVM) 0.315302 NaN
51 Support Vector Machines (SVM) NaN 1.0
52 Support Vector Machines (SVM) NaN False
53 Support Vector Machines (SVM) NaN 200
54 Support Vector Machines (SVM) NaN None
55 Support Vector Machines (SVM) NaN 0.0
56 Support Vector Machines (SVM) NaN ovr
57 Support Vector Machines (SVM) NaN 3
58 Support Vector Machines (SVM) NaN scale
59 Support Vector Machines (SVM) NaN rbf
60 Support Vector Machines (SVM) NaN -1
61 Support Vector Machines (SVM) NaN True
62 Support Vector Machines (SVM) NaN None
63 Support Vector Machines (SVM) NaN True
64 Support Vector Machines (SVM) NaN 0.001
65 Support Vector Machines (SVM) NaN False
66 Support Vector Machines (SVM) 0.373815 NaN
67 Support Vector Machines (SVM) 0.063974 NaN
68 Support Vector Machines (SVM) 0.816327 NaN
69 Support Vector Machines (SVM) 0.118650 NaN
70 Support Vector Machines (SVM) 0.640518 NaN
71 Support Vector Machines (SVM) 0.251837 NaN
72 Support Vector Machines (SVM) 0.281036 NaN
73 Support Vector Machines (SVM) NaN 1.0
74 Support Vector Machines (SVM) NaN False
75 Support Vector Machines (SVM) NaN 200
76 Support Vector Machines (SVM) NaN None
77 Support Vector Machines (SVM) NaN 0.0
78 Support Vector Machines (SVM) NaN ovr
79 Support Vector Machines (SVM) NaN 3
80 Support Vector Machines (SVM) NaN scale
81 Support Vector Machines (SVM) NaN rbf
82 Support Vector Machines (SVM) NaN -1
83 Support Vector Machines (SVM) NaN True
84 Support Vector Machines (SVM) NaN None
85 Support Vector Machines (SVM) NaN True
86 Support Vector Machines (SVM) NaN 0.001
87 Support Vector Machines (SVM) NaN False
88 Support Vector Machines (SVM) 0.400421 NaN
89 Support Vector Machines (SVM) 0.074028 NaN
90 Support Vector Machines (SVM) 0.828704 NaN
91 Support Vector Machines (SVM) 0.135915 NaN
92 Support Vector Machines (SVM) 0.670456 NaN
93 Support Vector Machines (SVM) 0.274581 NaN
94 Support Vector Machines (SVM) 0.340913 NaN
95 Support Vector Machines (SVM) NaN 1.0
96 Support Vector Machines (SVM) NaN False
97 Support Vector Machines (SVM) NaN 200
98 Support Vector Machines (SVM) NaN None
99 Support Vector Machines (SVM) NaN 0.0
100 Support Vector Machines (SVM) NaN ovr
101 Support Vector Machines (SVM) NaN 3
102 Support Vector Machines (SVM) NaN scale
103 Support Vector Machines (SVM) NaN rbf
104 Support Vector Machines (SVM) NaN -1
105 Support Vector Machines (SVM) NaN True
106 Support Vector Machines (SVM) NaN None
107 Support Vector Machines (SVM) NaN True
108 Support Vector Machines (SVM) NaN 0.001
109 Support Vector Machines (SVM) NaN False
Imputation Technique Imbalance Class Technique \
0 Median Imputation Oversampling
1 Median Imputation Oversampling
2 Median Imputation Oversampling
3 Median Imputation Oversampling
4 Median Imputation Oversampling
5 Median Imputation Oversampling
6 Median Imputation Oversampling
7 Median Imputation Oversampling
8 Median Imputation Oversampling
9 Median Imputation Oversampling
10 Median Imputation Oversampling
11 Median Imputation Oversampling
12 Median Imputation Oversampling
13 Median Imputation Oversampling
14 Median Imputation Oversampling
15 Median Imputation Oversampling
16 Median Imputation Oversampling
17 Median Imputation Oversampling
18 Median Imputation Oversampling
19 Median Imputation Oversampling
20 Median Imputation Oversampling
21 Median Imputation Oversampling
22 Median Imputation Oversampling
23 Median Imputation Oversampling
24 Median Imputation Oversampling
25 Median Imputation Oversampling
26 Median Imputation Oversampling
27 Median Imputation Oversampling
28 Median Imputation Oversampling
29 Median Imputation Oversampling
30 Median Imputation Oversampling
31 Median Imputation Oversampling
32 Median Imputation Oversampling
33 Median Imputation Oversampling
34 Median Imputation Oversampling
35 Median Imputation Oversampling
36 Median Imputation Oversampling
37 Median Imputation Oversampling
38 Median Imputation Oversampling
39 Median Imputation Oversampling
40 Median Imputation Oversampling
41 Median Imputation Oversampling
42 Median Imputation Oversampling
43 Median Imputation Oversampling
44 Median Imputation Oversampling
45 Median Imputation Oversampling
46 Median Imputation Oversampling
47 Median Imputation Oversampling
48 Median Imputation Oversampling
49 Median Imputation Oversampling
50 Median Imputation Oversampling
51 Median Imputation Oversampling
52 Median Imputation Oversampling
53 Median Imputation Oversampling
54 Median Imputation Oversampling
55 Median Imputation Oversampling
56 Median Imputation Oversampling
57 Median Imputation Oversampling
58 Median Imputation Oversampling
59 Median Imputation Oversampling
60 Median Imputation Oversampling
61 Median Imputation Oversampling
62 Median Imputation Oversampling
63 Median Imputation Oversampling
64 Median Imputation Oversampling
65 Median Imputation Oversampling
66 Median Imputation Oversampling
67 Median Imputation Oversampling
68 Median Imputation Oversampling
69 Median Imputation Oversampling
70 Median Imputation Oversampling
71 Median Imputation Oversampling
72 Median Imputation Oversampling
73 Median Imputation Oversampling
74 Median Imputation Oversampling
75 Median Imputation Oversampling
76 Median Imputation Oversampling
77 Median Imputation Oversampling
78 Median Imputation Oversampling
79 Median Imputation Oversampling
80 Median Imputation Oversampling
81 Median Imputation Oversampling
82 Median Imputation Oversampling
83 Median Imputation Oversampling
84 Median Imputation Oversampling
85 Median Imputation Oversampling
86 Median Imputation Oversampling
87 Median Imputation Oversampling
88 Median Imputation Oversampling
89 Median Imputation Oversampling
90 Median Imputation Oversampling
91 Median Imputation Oversampling
92 Median Imputation Oversampling
93 Median Imputation Oversampling
94 Median Imputation Oversampling
95 Median Imputation Oversampling
96 Median Imputation Oversampling
97 Median Imputation Oversampling
98 Median Imputation Oversampling
99 Median Imputation Oversampling
100 Median Imputation Oversampling
101 Median Imputation Oversampling
102 Median Imputation Oversampling
103 Median Imputation Oversampling
104 Median Imputation Oversampling
105 Median Imputation Oversampling
106 Median Imputation Oversampling
107 Median Imputation Oversampling
108 Median Imputation Oversampling
109 Median Imputation Oversampling
Model Unique Code
0 Support Vector Machines (SVM)_Oversampling_Med...
1 Support Vector Machines (SVM)_Oversampling_Med...
2 Support Vector Machines (SVM)_Oversampling_Med...
3 Support Vector Machines (SVM)_Oversampling_Med...
4 Support Vector Machines (SVM)_Oversampling_Med...
5 Support Vector Machines (SVM)_Oversampling_Med...
6 Support Vector Machines (SVM)_Oversampling_Med...
7 Support Vector Machines (SVM)_Oversampling_Med...
8 Support Vector Machines (SVM)_Oversampling_Med...
9 Support Vector Machines (SVM)_Oversampling_Med...
10 Support Vector Machines (SVM)_Oversampling_Med...
11 Support Vector Machines (SVM)_Oversampling_Med...
12 Support Vector Machines (SVM)_Oversampling_Med...
13 Support Vector Machines (SVM)_Oversampling_Med...
14 Support Vector Machines (SVM)_Oversampling_Med...
15 Support Vector Machines (SVM)_Oversampling_Med...
16 Support Vector Machines (SVM)_Oversampling_Med...
17 Support Vector Machines (SVM)_Oversampling_Med...
18 Support Vector Machines (SVM)_Oversampling_Med...
19 Support Vector Machines (SVM)_Oversampling_Med...
20 Support Vector Machines (SVM)_Oversampling_Med...
21 Support Vector Machines (SVM)_Oversampling_Med...
22 Support Vector Machines (SVM)_Oversampling_Med...
23 Support Vector Machines (SVM)_Oversampling_Med...
24 Support Vector Machines (SVM)_Oversampling_Med...
25 Support Vector Machines (SVM)_Oversampling_Med...
26 Support Vector Machines (SVM)_Oversampling_Med...
27 Support Vector Machines (SVM)_Oversampling_Med...
28 Support Vector Machines (SVM)_Oversampling_Med...
29 Support Vector Machines (SVM)_Oversampling_Med...
30 Support Vector Machines (SVM)_Oversampling_Med...
31 Support Vector Machines (SVM)_Oversampling_Med...
32 Support Vector Machines (SVM)_Oversampling_Med...
33 Support Vector Machines (SVM)_Oversampling_Med...
34 Support Vector Machines (SVM)_Oversampling_Med...
35 Support Vector Machines (SVM)_Oversampling_Med...
36 Support Vector Machines (SVM)_Oversampling_Med...
37 Support Vector Machines (SVM)_Oversampling_Med...
38 Support Vector Machines (SVM)_Oversampling_Med...
39 Support Vector Machines (SVM)_Oversampling_Med...
40 Support Vector Machines (SVM)_Oversampling_Med...
41 Support Vector Machines (SVM)_Oversampling_Med...
42 Support Vector Machines (SVM)_Oversampling_Med...
43 Support Vector Machines (SVM)_Oversampling_Med...
44 Support Vector Machines (SVM)_Oversampling_Med...
45 Support Vector Machines (SVM)_Oversampling_Med...
46 Support Vector Machines (SVM)_Oversampling_Med...
47 Support Vector Machines (SVM)_Oversampling_Med...
48 Support Vector Machines (SVM)_Oversampling_Med...
49 Support Vector Machines (SVM)_Oversampling_Med...
50 Support Vector Machines (SVM)_Oversampling_Med...
51 Support Vector Machines (SVM)_Oversampling_Med...
52 Support Vector Machines (SVM)_Oversampling_Med...
53 Support Vector Machines (SVM)_Oversampling_Med...
54 Support Vector Machines (SVM)_Oversampling_Med...
55 Support Vector Machines (SVM)_Oversampling_Med...
56 Support Vector Machines (SVM)_Oversampling_Med...
57 Support Vector Machines (SVM)_Oversampling_Med...
58 Support Vector Machines (SVM)_Oversampling_Med...
59 Support Vector Machines (SVM)_Oversampling_Med...
60 Support Vector Machines (SVM)_Oversampling_Med...
61 Support Vector Machines (SVM)_Oversampling_Med...
62 Support Vector Machines (SVM)_Oversampling_Med...
63 Support Vector Machines (SVM)_Oversampling_Med...
64 Support Vector Machines (SVM)_Oversampling_Med...
65 Support Vector Machines (SVM)_Oversampling_Med...
66 Support Vector Machines (SVM)_Oversampling_Med...
67 Support Vector Machines (SVM)_Oversampling_Med...
68 Support Vector Machines (SVM)_Oversampling_Med...
69 Support Vector Machines (SVM)_Oversampling_Med...
70 Support Vector Machines (SVM)_Oversampling_Med...
71 Support Vector Machines (SVM)_Oversampling_Med...
72 Support Vector Machines (SVM)_Oversampling_Med...
73 Support Vector Machines (SVM)_Oversampling_Med...
74 Support Vector Machines (SVM)_Oversampling_Med...
75 Support Vector Machines (SVM)_Oversampling_Med...
76 Support Vector Machines (SVM)_Oversampling_Med...
77 Support Vector Machines (SVM)_Oversampling_Med...
78 Support Vector Machines (SVM)_Oversampling_Med...
79 Support Vector Machines (SVM)_Oversampling_Med...
80 Support Vector Machines (SVM)_Oversampling_Med...
81 Support Vector Machines (SVM)_Oversampling_Med...
82 Support Vector Machines (SVM)_Oversampling_Med...
83 Support Vector Machines (SVM)_Oversampling_Med...
84 Support Vector Machines (SVM)_Oversampling_Med...
85 Support Vector Machines (SVM)_Oversampling_Med...
86 Support Vector Machines (SVM)_Oversampling_Med...
87 Support Vector Machines (SVM)_Oversampling_Med...
88 Support Vector Machines (SVM)_Oversampling_Med...
89 Support Vector Machines (SVM)_Oversampling_Med...
90 Support Vector Machines (SVM)_Oversampling_Med...
91 Support Vector Machines (SVM)_Oversampling_Med...
92 Support Vector Machines (SVM)_Oversampling_Med...
93 Support Vector Machines (SVM)_Oversampling_Med...
94 Support Vector Machines (SVM)_Oversampling_Med...
95 Support Vector Machines (SVM)_Oversampling_Med...
96 Support Vector Machines (SVM)_Oversampling_Med...
97 Support Vector Machines (SVM)_Oversampling_Med...
98 Support Vector Machines (SVM)_Oversampling_Med...
99 Support Vector Machines (SVM)_Oversampling_Med...
100 Support Vector Machines (SVM)_Oversampling_Med...
101 Support Vector Machines (SVM)_Oversampling_Med...
102 Support Vector Machines (SVM)_Oversampling_Med...
103 Support Vector Machines (SVM)_Oversampling_Med...
104 Support Vector Machines (SVM)_Oversampling_Med...
105 Support Vector Machines (SVM)_Oversampling_Med...
106 Support Vector Machines (SVM)_Oversampling_Med...
107 Support Vector Machines (SVM)_Oversampling_Med...
108 Support Vector Machines (SVM)_Oversampling_Med...
109 Support Vector Machines (SVM)_Oversampling_Med... ,
Algorithm Metrics Hyperparameters \
0 Support Vector Machines (SVM) 0.397682 NaN
1 Support Vector Machines (SVM) 0.077494 NaN
2 Support Vector Machines (SVM) 0.793249 NaN
3 Support Vector Machines (SVM) 0.141194 NaN
4 Support Vector Machines (SVM) 0.627430 NaN
5 Support Vector Machines (SVM) 0.200847 NaN
6 Support Vector Machines (SVM) 0.254861 NaN
7 Support Vector Machines (SVM) NaN 1.0
8 Support Vector Machines (SVM) NaN False
9 Support Vector Machines (SVM) NaN 200
10 Support Vector Machines (SVM) NaN None
11 Support Vector Machines (SVM) NaN 0.0
12 Support Vector Machines (SVM) NaN ovr
13 Support Vector Machines (SVM) NaN 3
14 Support Vector Machines (SVM) NaN scale
15 Support Vector Machines (SVM) NaN rbf
16 Support Vector Machines (SVM) NaN -1
17 Support Vector Machines (SVM) NaN True
18 Support Vector Machines (SVM) NaN None
19 Support Vector Machines (SVM) NaN True
20 Support Vector Machines (SVM) NaN 0.001
21 Support Vector Machines (SVM) NaN False
22 Support Vector Machines (SVM) 0.419805 NaN
23 Support Vector Machines (SVM) 0.058084 NaN
24 Support Vector Machines (SVM) 0.817073 NaN
25 Support Vector Machines (SVM) 0.108458 NaN
26 Support Vector Machines (SVM) 0.671298 NaN
27 Support Vector Machines (SVM) 0.274677 NaN
28 Support Vector Machines (SVM) 0.342596 NaN
29 Support Vector Machines (SVM) NaN 1.0
30 Support Vector Machines (SVM) NaN False
31 Support Vector Machines (SVM) NaN 200
32 Support Vector Machines (SVM) NaN None
33 Support Vector Machines (SVM) NaN 0.0
34 Support Vector Machines (SVM) NaN ovr
35 Support Vector Machines (SVM) NaN 3
36 Support Vector Machines (SVM) NaN scale
37 Support Vector Machines (SVM) NaN rbf
38 Support Vector Machines (SVM) NaN -1
39 Support Vector Machines (SVM) NaN True
40 Support Vector Machines (SVM) NaN None
41 Support Vector Machines (SVM) NaN True
42 Support Vector Machines (SVM) NaN 0.001
43 Support Vector Machines (SVM) NaN False
44 Support Vector Machines (SVM) 0.400053 NaN
45 Support Vector Machines (SVM) 0.060899 NaN
46 Support Vector Machines (SVM) 0.775401 NaN
47 Support Vector Machines (SVM) 0.112928 NaN
48 Support Vector Machines (SVM) 0.642343 NaN
49 Support Vector Machines (SVM) 0.226702 NaN
50 Support Vector Machines (SVM) 0.284686 NaN
51 Support Vector Machines (SVM) NaN 1.0
52 Support Vector Machines (SVM) NaN False
53 Support Vector Machines (SVM) NaN 200
54 Support Vector Machines (SVM) NaN None
55 Support Vector Machines (SVM) NaN 0.0
56 Support Vector Machines (SVM) NaN ovr
57 Support Vector Machines (SVM) NaN 3
58 Support Vector Machines (SVM) NaN scale
59 Support Vector Machines (SVM) NaN rbf
60 Support Vector Machines (SVM) NaN -1
61 Support Vector Machines (SVM) NaN True
62 Support Vector Machines (SVM) NaN None
63 Support Vector Machines (SVM) NaN True
64 Support Vector Machines (SVM) NaN 0.001
65 Support Vector Machines (SVM) NaN False
66 Support Vector Machines (SVM) 0.413593 NaN
67 Support Vector Machines (SVM) 0.066610 NaN
68 Support Vector Machines (SVM) 0.795918 NaN
69 Support Vector Machines (SVM) 0.122931 NaN
70 Support Vector Machines (SVM) 0.639204 NaN
71 Support Vector Machines (SVM) 0.207200 NaN
72 Support Vector Machines (SVM) 0.278408 NaN
73 Support Vector Machines (SVM) NaN 1.0
74 Support Vector Machines (SVM) NaN False
75 Support Vector Machines (SVM) NaN 200
76 Support Vector Machines (SVM) NaN None
77 Support Vector Machines (SVM) NaN 0.0
78 Support Vector Machines (SVM) NaN ovr
79 Support Vector Machines (SVM) NaN 3
80 Support Vector Machines (SVM) NaN scale
81 Support Vector Machines (SVM) NaN rbf
82 Support Vector Machines (SVM) NaN -1
83 Support Vector Machines (SVM) NaN True
84 Support Vector Machines (SVM) NaN None
85 Support Vector Machines (SVM) NaN True
86 Support Vector Machines (SVM) NaN 0.001
87 Support Vector Machines (SVM) NaN False
88 Support Vector Machines (SVM) 0.453635 NaN
89 Support Vector Machines (SVM) 0.073462 NaN
90 Support Vector Machines (SVM) 0.740741 NaN
91 Support Vector Machines (SVM) 0.133668 NaN
92 Support Vector Machines (SVM) 0.657546 NaN
93 Support Vector Machines (SVM) 0.258975 NaN
94 Support Vector Machines (SVM) 0.315092 NaN
95 Support Vector Machines (SVM) NaN 1.0
96 Support Vector Machines (SVM) NaN False
97 Support Vector Machines (SVM) NaN 200
98 Support Vector Machines (SVM) NaN None
99 Support Vector Machines (SVM) NaN 0.0
100 Support Vector Machines (SVM) NaN ovr
101 Support Vector Machines (SVM) NaN 3
102 Support Vector Machines (SVM) NaN scale
103 Support Vector Machines (SVM) NaN rbf
104 Support Vector Machines (SVM) NaN -1
105 Support Vector Machines (SVM) NaN True
106 Support Vector Machines (SVM) NaN None
107 Support Vector Machines (SVM) NaN True
108 Support Vector Machines (SVM) NaN 0.001
109 Support Vector Machines (SVM) NaN False
Imputation Technique Imbalance Class Technique \
0 Zero Imputation Undersampling
1 Zero Imputation Undersampling
2 Zero Imputation Undersampling
3 Zero Imputation Undersampling
4 Zero Imputation Undersampling
5 Zero Imputation Undersampling
6 Zero Imputation Undersampling
7 Zero Imputation Undersampling
8 Zero Imputation Undersampling
9 Zero Imputation Undersampling
10 Zero Imputation Undersampling
11 Zero Imputation Undersampling
12 Zero Imputation Undersampling
13 Zero Imputation Undersampling
14 Zero Imputation Undersampling
15 Zero Imputation Undersampling
16 Zero Imputation Undersampling
17 Zero Imputation Undersampling
18 Zero Imputation Undersampling
19 Zero Imputation Undersampling
20 Zero Imputation Undersampling
21 Zero Imputation Undersampling
22 Zero Imputation Undersampling
23 Zero Imputation Undersampling
24 Zero Imputation Undersampling
25 Zero Imputation Undersampling
26 Zero Imputation Undersampling
27 Zero Imputation Undersampling
28 Zero Imputation Undersampling
29 Zero Imputation Undersampling
30 Zero Imputation Undersampling
31 Zero Imputation Undersampling
32 Zero Imputation Undersampling
33 Zero Imputation Undersampling
34 Zero Imputation Undersampling
35 Zero Imputation Undersampling
36 Zero Imputation Undersampling
37 Zero Imputation Undersampling
38 Zero Imputation Undersampling
39 Zero Imputation Undersampling
40 Zero Imputation Undersampling
41 Zero Imputation Undersampling
42 Zero Imputation Undersampling
43 Zero Imputation Undersampling
44 Zero Imputation Undersampling
45 Zero Imputation Undersampling
46 Zero Imputation Undersampling
47 Zero Imputation Undersampling
48 Zero Imputation Undersampling
49 Zero Imputation Undersampling
50 Zero Imputation Undersampling
51 Zero Imputation Undersampling
52 Zero Imputation Undersampling
53 Zero Imputation Undersampling
54 Zero Imputation Undersampling
55 Zero Imputation Undersampling
56 Zero Imputation Undersampling
57 Zero Imputation Undersampling
58 Zero Imputation Undersampling
59 Zero Imputation Undersampling
60 Zero Imputation Undersampling
61 Zero Imputation Undersampling
62 Zero Imputation Undersampling
63 Zero Imputation Undersampling
64 Zero Imputation Undersampling
65 Zero Imputation Undersampling
66 Zero Imputation Undersampling
67 Zero Imputation Undersampling
68 Zero Imputation Undersampling
69 Zero Imputation Undersampling
70 Zero Imputation Undersampling
71 Zero Imputation Undersampling
72 Zero Imputation Undersampling
73 Zero Imputation Undersampling
74 Zero Imputation Undersampling
75 Zero Imputation Undersampling
76 Zero Imputation Undersampling
77 Zero Imputation Undersampling
78 Zero Imputation Undersampling
79 Zero Imputation Undersampling
80 Zero Imputation Undersampling
81 Zero Imputation Undersampling
82 Zero Imputation Undersampling
83 Zero Imputation Undersampling
84 Zero Imputation Undersampling
85 Zero Imputation Undersampling
86 Zero Imputation Undersampling
87 Zero Imputation Undersampling
88 Zero Imputation Undersampling
89 Zero Imputation Undersampling
90 Zero Imputation Undersampling
91 Zero Imputation Undersampling
92 Zero Imputation Undersampling
93 Zero Imputation Undersampling
94 Zero Imputation Undersampling
95 Zero Imputation Undersampling
96 Zero Imputation Undersampling
97 Zero Imputation Undersampling
98 Zero Imputation Undersampling
99 Zero Imputation Undersampling
100 Zero Imputation Undersampling
101 Zero Imputation Undersampling
102 Zero Imputation Undersampling
103 Zero Imputation Undersampling
104 Zero Imputation Undersampling
105 Zero Imputation Undersampling
106 Zero Imputation Undersampling
107 Zero Imputation Undersampling
108 Zero Imputation Undersampling
109 Zero Imputation Undersampling
Model Unique Code
0 Support Vector Machines (SVM)_Undersampling_Ze...
1 Support Vector Machines (SVM)_Undersampling_Ze...
2 Support Vector Machines (SVM)_Undersampling_Ze...
3 Support Vector Machines (SVM)_Undersampling_Ze...
4 Support Vector Machines (SVM)_Undersampling_Ze...
5 Support Vector Machines (SVM)_Undersampling_Ze...
6 Support Vector Machines (SVM)_Undersampling_Ze...
7 Support Vector Machines (SVM)_Undersampling_Ze...
8 Support Vector Machines (SVM)_Undersampling_Ze...
9 Support Vector Machines (SVM)_Undersampling_Ze...
10 Support Vector Machines (SVM)_Undersampling_Ze...
11 Support Vector Machines (SVM)_Undersampling_Ze...
12 Support Vector Machines (SVM)_Undersampling_Ze...
13 Support Vector Machines (SVM)_Undersampling_Ze...
14 Support Vector Machines (SVM)_Undersampling_Ze...
15 Support Vector Machines (SVM)_Undersampling_Ze...
16 Support Vector Machines (SVM)_Undersampling_Ze...
17 Support Vector Machines (SVM)_Undersampling_Ze...
18 Support Vector Machines (SVM)_Undersampling_Ze...
19 Support Vector Machines (SVM)_Undersampling_Ze...
20 Support Vector Machines (SVM)_Undersampling_Ze...
21 Support Vector Machines (SVM)_Undersampling_Ze...
22 Support Vector Machines (SVM)_Undersampling_Ze...
23 Support Vector Machines (SVM)_Undersampling_Ze...
24 Support Vector Machines (SVM)_Undersampling_Ze...
25 Support Vector Machines (SVM)_Undersampling_Ze...
26 Support Vector Machines (SVM)_Undersampling_Ze...
27 Support Vector Machines (SVM)_Undersampling_Ze...
28 Support Vector Machines (SVM)_Undersampling_Ze...
29 Support Vector Machines (SVM)_Undersampling_Ze...
30 Support Vector Machines (SVM)_Undersampling_Ze...
31 Support Vector Machines (SVM)_Undersampling_Ze...
32 Support Vector Machines (SVM)_Undersampling_Ze...
33 Support Vector Machines (SVM)_Undersampling_Ze...
34 Support Vector Machines (SVM)_Undersampling_Ze...
35 Support Vector Machines (SVM)_Undersampling_Ze...
36 Support Vector Machines (SVM)_Undersampling_Ze...
37 Support Vector Machines (SVM)_Undersampling_Ze...
38 Support Vector Machines (SVM)_Undersampling_Ze...
39 Support Vector Machines (SVM)_Undersampling_Ze...
40 Support Vector Machines (SVM)_Undersampling_Ze...
41 Support Vector Machines (SVM)_Undersampling_Ze...
42 Support Vector Machines (SVM)_Undersampling_Ze...
43 Support Vector Machines (SVM)_Undersampling_Ze...
44 Support Vector Machines (SVM)_Undersampling_Ze...
45 Support Vector Machines (SVM)_Undersampling_Ze...
46 Support Vector Machines (SVM)_Undersampling_Ze...
47 Support Vector Machines (SVM)_Undersampling_Ze...
48 Support Vector Machines (SVM)_Undersampling_Ze...
49 Support Vector Machines (SVM)_Undersampling_Ze...
50 Support Vector Machines (SVM)_Undersampling_Ze...
51 Support Vector Machines (SVM)_Undersampling_Ze...
52 Support Vector Machines (SVM)_Undersampling_Ze...
53 Support Vector Machines (SVM)_Undersampling_Ze...
54 Support Vector Machines (SVM)_Undersampling_Ze...
55 Support Vector Machines (SVM)_Undersampling_Ze...
56 Support Vector Machines (SVM)_Undersampling_Ze...
57 Support Vector Machines (SVM)_Undersampling_Ze...
58 Support Vector Machines (SVM)_Undersampling_Ze...
59 Support Vector Machines (SVM)_Undersampling_Ze...
60 Support Vector Machines (SVM)_Undersampling_Ze...
61 Support Vector Machines (SVM)_Undersampling_Ze...
62 Support Vector Machines (SVM)_Undersampling_Ze...
63 Support Vector Machines (SVM)_Undersampling_Ze...
64 Support Vector Machines (SVM)_Undersampling_Ze...
65 Support Vector Machines (SVM)_Undersampling_Ze...
66 Support Vector Machines (SVM)_Undersampling_Ze...
67 Support Vector Machines (SVM)_Undersampling_Ze...
68 Support Vector Machines (SVM)_Undersampling_Ze...
69 Support Vector Machines (SVM)_Undersampling_Ze...
70 Support Vector Machines (SVM)_Undersampling_Ze...
71 Support Vector Machines (SVM)_Undersampling_Ze...
72 Support Vector Machines (SVM)_Undersampling_Ze...
73 Support Vector Machines (SVM)_Undersampling_Ze...
74 Support Vector Machines (SVM)_Undersampling_Ze...
75 Support Vector Machines (SVM)_Undersampling_Ze...
76 Support Vector Machines (SVM)_Undersampling_Ze...
77 Support Vector Machines (SVM)_Undersampling_Ze...
78 Support Vector Machines (SVM)_Undersampling_Ze...
79 Support Vector Machines (SVM)_Undersampling_Ze...
80 Support Vector Machines (SVM)_Undersampling_Ze...
81 Support Vector Machines (SVM)_Undersampling_Ze...
82 Support Vector Machines (SVM)_Undersampling_Ze...
83 Support Vector Machines (SVM)_Undersampling_Ze...
84 Support Vector Machines (SVM)_Undersampling_Ze...
85 Support Vector Machines (SVM)_Undersampling_Ze...
86 Support Vector Machines (SVM)_Undersampling_Ze...
87 Support Vector Machines (SVM)_Undersampling_Ze...
88 Support Vector Machines (SVM)_Undersampling_Ze...
89 Support Vector Machines (SVM)_Undersampling_Ze...
90 Support Vector Machines (SVM)_Undersampling_Ze...
91 Support Vector Machines (SVM)_Undersampling_Ze...
92 Support Vector Machines (SVM)_Undersampling_Ze...
93 Support Vector Machines (SVM)_Undersampling_Ze...
94 Support Vector Machines (SVM)_Undersampling_Ze...
95 Support Vector Machines (SVM)_Undersampling_Ze...
96 Support Vector Machines (SVM)_Undersampling_Ze...
97 Support Vector Machines (SVM)_Undersampling_Ze...
98 Support Vector Machines (SVM)_Undersampling_Ze...
99 Support Vector Machines (SVM)_Undersampling_Ze...
100 Support Vector Machines (SVM)_Undersampling_Ze...
101 Support Vector Machines (SVM)_Undersampling_Ze...
102 Support Vector Machines (SVM)_Undersampling_Ze...
103 Support Vector Machines (SVM)_Undersampling_Ze...
104 Support Vector Machines (SVM)_Undersampling_Ze...
105 Support Vector Machines (SVM)_Undersampling_Ze...
106 Support Vector Machines (SVM)_Undersampling_Ze...
107 Support Vector Machines (SVM)_Undersampling_Ze...
108 Support Vector Machines (SVM)_Undersampling_Ze...
109 Support Vector Machines (SVM)_Undersampling_Ze... ,
Algorithm Metrics Hyperparameters \
0 Support Vector Machines (SVM) 0.423492 NaN
1 Support Vector Machines (SVM) 0.065450 NaN
2 Support Vector Machines (SVM) 0.620253 NaN
3 Support Vector Machines (SVM) 0.118405 NaN
4 Support Vector Machines (SVM) 0.553104 NaN
5 Support Vector Machines (SVM) 0.126036 NaN
6 Support Vector Machines (SVM) 0.106207 NaN
7 Support Vector Machines (SVM) NaN 1.0
8 Support Vector Machines (SVM) NaN False
9 Support Vector Machines (SVM) NaN 200
10 Support Vector Machines (SVM) NaN None
11 Support Vector Machines (SVM) NaN 0.0
12 Support Vector Machines (SVM) NaN ovr
13 Support Vector Machines (SVM) NaN 3
14 Support Vector Machines (SVM) NaN scale
15 Support Vector Machines (SVM) NaN rbf
16 Support Vector Machines (SVM) NaN -1
17 Support Vector Machines (SVM) NaN True
18 Support Vector Machines (SVM) NaN None
19 Support Vector Machines (SVM) NaN True
20 Support Vector Machines (SVM) NaN 0.001
21 Support Vector Machines (SVM) NaN False
22 Support Vector Machines (SVM) 0.160126 NaN
23 Support Vector Machines (SVM) 0.047833 NaN
24 Support Vector Machines (SVM) 0.975610 NaN
25 Support Vector Machines (SVM) 0.091194 NaN
26 Support Vector Machines (SVM) 0.632623 NaN
27 Support Vector Machines (SVM) 0.242155 NaN
28 Support Vector Machines (SVM) 0.265246 NaN
29 Support Vector Machines (SVM) NaN 1.0
30 Support Vector Machines (SVM) NaN False
31 Support Vector Machines (SVM) NaN 200
32 Support Vector Machines (SVM) NaN None
33 Support Vector Machines (SVM) NaN 0.0
34 Support Vector Machines (SVM) NaN ovr
35 Support Vector Machines (SVM) NaN 3
36 Support Vector Machines (SVM) NaN scale
37 Support Vector Machines (SVM) NaN rbf
38 Support Vector Machines (SVM) NaN -1
39 Support Vector Machines (SVM) NaN True
40 Support Vector Machines (SVM) NaN None
41 Support Vector Machines (SVM) NaN True
42 Support Vector Machines (SVM) NaN 0.001
43 Support Vector Machines (SVM) NaN False
44 Support Vector Machines (SVM) 0.171188 NaN
45 Support Vector Machines (SVM) 0.053410 NaN
46 Support Vector Machines (SVM) 0.946524 NaN
47 Support Vector Machines (SVM) 0.101114 NaN
48 Support Vector Machines (SVM) 0.577636 NaN
49 Support Vector Machines (SVM) 0.161832 NaN
50 Support Vector Machines (SVM) 0.155273 NaN
51 Support Vector Machines (SVM) NaN 1.0
52 Support Vector Machines (SVM) NaN False
53 Support Vector Machines (SVM) NaN 200
54 Support Vector Machines (SVM) NaN None
55 Support Vector Machines (SVM) NaN 0.0
56 Support Vector Machines (SVM) NaN ovr
57 Support Vector Machines (SVM) NaN 3
58 Support Vector Machines (SVM) NaN scale
59 Support Vector Machines (SVM) NaN rbf
60 Support Vector Machines (SVM) NaN -1
61 Support Vector Machines (SVM) NaN True
62 Support Vector Machines (SVM) NaN None
63 Support Vector Machines (SVM) NaN True
64 Support Vector Machines (SVM) NaN 0.001
65 Support Vector Machines (SVM) NaN False
66 Support Vector Machines (SVM) 0.207850 NaN
67 Support Vector Machines (SVM) 0.057880 NaN
68 Support Vector Machines (SVM) 0.938776 NaN
69 Support Vector Machines (SVM) 0.109037 NaN
70 Support Vector Machines (SVM) 0.563563 NaN
71 Support Vector Machines (SVM) 0.159700 NaN
72 Support Vector Machines (SVM) 0.127126 NaN
73 Support Vector Machines (SVM) NaN 1.0
74 Support Vector Machines (SVM) NaN False
75 Support Vector Machines (SVM) NaN 200
76 Support Vector Machines (SVM) NaN None
77 Support Vector Machines (SVM) NaN 0.0
78 Support Vector Machines (SVM) NaN ovr
79 Support Vector Machines (SVM) NaN 3
80 Support Vector Machines (SVM) NaN scale
81 Support Vector Machines (SVM) NaN rbf
82 Support Vector Machines (SVM) NaN -1
83 Support Vector Machines (SVM) NaN True
84 Support Vector Machines (SVM) NaN None
85 Support Vector Machines (SVM) NaN True
86 Support Vector Machines (SVM) NaN 0.001
87 Support Vector Machines (SVM) NaN False
88 Support Vector Machines (SVM) 0.181243 NaN
89 Support Vector Machines (SVM) 0.062878 NaN
90 Support Vector Machines (SVM) 0.962963 NaN
91 Support Vector Machines (SVM) 0.118048 NaN
92 Support Vector Machines (SVM) 0.580049 NaN
93 Support Vector Machines (SVM) 0.191511 NaN
94 Support Vector Machines (SVM) 0.160097 NaN
95 Support Vector Machines (SVM) NaN 1.0
96 Support Vector Machines (SVM) NaN False
97 Support Vector Machines (SVM) NaN 200
98 Support Vector Machines (SVM) NaN None
99 Support Vector Machines (SVM) NaN 0.0
100 Support Vector Machines (SVM) NaN ovr
101 Support Vector Machines (SVM) NaN 3
102 Support Vector Machines (SVM) NaN scale
103 Support Vector Machines (SVM) NaN rbf
104 Support Vector Machines (SVM) NaN -1
105 Support Vector Machines (SVM) NaN True
106 Support Vector Machines (SVM) NaN None
107 Support Vector Machines (SVM) NaN True
108 Support Vector Machines (SVM) NaN 0.001
109 Support Vector Machines (SVM) NaN False
Imputation Technique Imbalance Class Technique \
0 Mean Imputation Undersampling
1 Mean Imputation Undersampling
2 Mean Imputation Undersampling
3 Mean Imputation Undersampling
4 Mean Imputation Undersampling
5 Mean Imputation Undersampling
6 Mean Imputation Undersampling
7 Mean Imputation Undersampling
8 Mean Imputation Undersampling
9 Mean Imputation Undersampling
10 Mean Imputation Undersampling
11 Mean Imputation Undersampling
12 Mean Imputation Undersampling
13 Mean Imputation Undersampling
14 Mean Imputation Undersampling
15 Mean Imputation Undersampling
16 Mean Imputation Undersampling
17 Mean Imputation Undersampling
18 Mean Imputation Undersampling
19 Mean Imputation Undersampling
20 Mean Imputation Undersampling
21 Mean Imputation Undersampling
22 Mean Imputation Undersampling
23 Mean Imputation Undersampling
24 Mean Imputation Undersampling
25 Mean Imputation Undersampling
26 Mean Imputation Undersampling
27 Mean Imputation Undersampling
28 Mean Imputation Undersampling
29 Mean Imputation Undersampling
30 Mean Imputation Undersampling
31 Mean Imputation Undersampling
32 Mean Imputation Undersampling
33 Mean Imputation Undersampling
34 Mean Imputation Undersampling
35 Mean Imputation Undersampling
36 Mean Imputation Undersampling
37 Mean Imputation Undersampling
38 Mean Imputation Undersampling
39 Mean Imputation Undersampling
40 Mean Imputation Undersampling
41 Mean Imputation Undersampling
42 Mean Imputation Undersampling
43 Mean Imputation Undersampling
44 Mean Imputation Undersampling
45 Mean Imputation Undersampling
46 Mean Imputation Undersampling
47 Mean Imputation Undersampling
48 Mean Imputation Undersampling
49 Mean Imputation Undersampling
50 Mean Imputation Undersampling
51 Mean Imputation Undersampling
52 Mean Imputation Undersampling
53 Mean Imputation Undersampling
54 Mean Imputation Undersampling
55 Mean Imputation Undersampling
56 Mean Imputation Undersampling
57 Mean Imputation Undersampling
58 Mean Imputation Undersampling
59 Mean Imputation Undersampling
60 Mean Imputation Undersampling
61 Mean Imputation Undersampling
62 Mean Imputation Undersampling
63 Mean Imputation Undersampling
64 Mean Imputation Undersampling
65 Mean Imputation Undersampling
66 Mean Imputation Undersampling
67 Mean Imputation Undersampling
68 Mean Imputation Undersampling
69 Mean Imputation Undersampling
70 Mean Imputation Undersampling
71 Mean Imputation Undersampling
72 Mean Imputation Undersampling
73 Mean Imputation Undersampling
74 Mean Imputation Undersampling
75 Mean Imputation Undersampling
76 Mean Imputation Undersampling
77 Mean Imputation Undersampling
78 Mean Imputation Undersampling
79 Mean Imputation Undersampling
80 Mean Imputation Undersampling
81 Mean Imputation Undersampling
82 Mean Imputation Undersampling
83 Mean Imputation Undersampling
84 Mean Imputation Undersampling
85 Mean Imputation Undersampling
86 Mean Imputation Undersampling
87 Mean Imputation Undersampling
88 Mean Imputation Undersampling
89 Mean Imputation Undersampling
90 Mean Imputation Undersampling
91 Mean Imputation Undersampling
92 Mean Imputation Undersampling
93 Mean Imputation Undersampling
94 Mean Imputation Undersampling
95 Mean Imputation Undersampling
96 Mean Imputation Undersampling
97 Mean Imputation Undersampling
98 Mean Imputation Undersampling
99 Mean Imputation Undersampling
100 Mean Imputation Undersampling
101 Mean Imputation Undersampling
102 Mean Imputation Undersampling
103 Mean Imputation Undersampling
104 Mean Imputation Undersampling
105 Mean Imputation Undersampling
106 Mean Imputation Undersampling
107 Mean Imputation Undersampling
108 Mean Imputation Undersampling
109 Mean Imputation Undersampling
Model Unique Code
0 Support Vector Machines (SVM)_Undersampling_Me...
1 Support Vector Machines (SVM)_Undersampling_Me...
2 Support Vector Machines (SVM)_Undersampling_Me...
3 Support Vector Machines (SVM)_Undersampling_Me...
4 Support Vector Machines (SVM)_Undersampling_Me...
5 Support Vector Machines (SVM)_Undersampling_Me...
6 Support Vector Machines (SVM)_Undersampling_Me...
7 Support Vector Machines (SVM)_Undersampling_Me...
8 Support Vector Machines (SVM)_Undersampling_Me...
9 Support Vector Machines (SVM)_Undersampling_Me...
10 Support Vector Machines (SVM)_Undersampling_Me...
11 Support Vector Machines (SVM)_Undersampling_Me...
12 Support Vector Machines (SVM)_Undersampling_Me...
13 Support Vector Machines (SVM)_Undersampling_Me...
14 Support Vector Machines (SVM)_Undersampling_Me...
15 Support Vector Machines (SVM)_Undersampling_Me...
16 Support Vector Machines (SVM)_Undersampling_Me...
17 Support Vector Machines (SVM)_Undersampling_Me...
18 Support Vector Machines (SVM)_Undersampling_Me...
19 Support Vector Machines (SVM)_Undersampling_Me...
20 Support Vector Machines (SVM)_Undersampling_Me...
21 Support Vector Machines (SVM)_Undersampling_Me...
22 Support Vector Machines (SVM)_Undersampling_Me...
23 Support Vector Machines (SVM)_Undersampling_Me...
24 Support Vector Machines (SVM)_Undersampling_Me...
25 Support Vector Machines (SVM)_Undersampling_Me...
26 Support Vector Machines (SVM)_Undersampling_Me...
27 Support Vector Machines (SVM)_Undersampling_Me...
28 Support Vector Machines (SVM)_Undersampling_Me...
29 Support Vector Machines (SVM)_Undersampling_Me...
30 Support Vector Machines (SVM)_Undersampling_Me...
31 Support Vector Machines (SVM)_Undersampling_Me...
32 Support Vector Machines (SVM)_Undersampling_Me...
33 Support Vector Machines (SVM)_Undersampling_Me...
34 Support Vector Machines (SVM)_Undersampling_Me...
35 Support Vector Machines (SVM)_Undersampling_Me...
36 Support Vector Machines (SVM)_Undersampling_Me...
37 Support Vector Machines (SVM)_Undersampling_Me...
38 Support Vector Machines (SVM)_Undersampling_Me...
39 Support Vector Machines (SVM)_Undersampling_Me...
40 Support Vector Machines (SVM)_Undersampling_Me...
41 Support Vector Machines (SVM)_Undersampling_Me...
42 Support Vector Machines (SVM)_Undersampling_Me...
43 Support Vector Machines (SVM)_Undersampling_Me...
44 Support Vector Machines (SVM)_Undersampling_Me...
45 Support Vector Machines (SVM)_Undersampling_Me...
46 Support Vector Machines (SVM)_Undersampling_Me...
47 Support Vector Machines (SVM)_Undersampling_Me...
48 Support Vector Machines (SVM)_Undersampling_Me...
49 Support Vector Machines (SVM)_Undersampling_Me...
50 Support Vector Machines (SVM)_Undersampling_Me...
51 Support Vector Machines (SVM)_Undersampling_Me...
52 Support Vector Machines (SVM)_Undersampling_Me...
53 Support Vector Machines (SVM)_Undersampling_Me...
54 Support Vector Machines (SVM)_Undersampling_Me...
55 Support Vector Machines (SVM)_Undersampling_Me...
56 Support Vector Machines (SVM)_Undersampling_Me...
57 Support Vector Machines (SVM)_Undersampling_Me...
58 Support Vector Machines (SVM)_Undersampling_Me...
59 Support Vector Machines (SVM)_Undersampling_Me...
60 Support Vector Machines (SVM)_Undersampling_Me...
61 Support Vector Machines (SVM)_Undersampling_Me...
62 Support Vector Machines (SVM)_Undersampling_Me...
63 Support Vector Machines (SVM)_Undersampling_Me...
64 Support Vector Machines (SVM)_Undersampling_Me...
65 Support Vector Machines (SVM)_Undersampling_Me...
66 Support Vector Machines (SVM)_Undersampling_Me...
67 Support Vector Machines (SVM)_Undersampling_Me...
68 Support Vector Machines (SVM)_Undersampling_Me...
69 Support Vector Machines (SVM)_Undersampling_Me...
70 Support Vector Machines (SVM)_Undersampling_Me...
71 Support Vector Machines (SVM)_Undersampling_Me...
72 Support Vector Machines (SVM)_Undersampling_Me...
73 Support Vector Machines (SVM)_Undersampling_Me...
74 Support Vector Machines (SVM)_Undersampling_Me...
75 Support Vector Machines (SVM)_Undersampling_Me...
76 Support Vector Machines (SVM)_Undersampling_Me...
77 Support Vector Machines (SVM)_Undersampling_Me...
78 Support Vector Machines (SVM)_Undersampling_Me...
79 Support Vector Machines (SVM)_Undersampling_Me...
80 Support Vector Machines (SVM)_Undersampling_Me...
81 Support Vector Machines (SVM)_Undersampling_Me...
82 Support Vector Machines (SVM)_Undersampling_Me...
83 Support Vector Machines (SVM)_Undersampling_Me...
84 Support Vector Machines (SVM)_Undersampling_Me...
85 Support Vector Machines (SVM)_Undersampling_Me...
86 Support Vector Machines (SVM)_Undersampling_Me...
87 Support Vector Machines (SVM)_Undersampling_Me...
88 Support Vector Machines (SVM)_Undersampling_Me...
89 Support Vector Machines (SVM)_Undersampling_Me...
90 Support Vector Machines (SVM)_Undersampling_Me...
91 Support Vector Machines (SVM)_Undersampling_Me...
92 Support Vector Machines (SVM)_Undersampling_Me...
93 Support Vector Machines (SVM)_Undersampling_Me...
94 Support Vector Machines (SVM)_Undersampling_Me...
95 Support Vector Machines (SVM)_Undersampling_Me...
96 Support Vector Machines (SVM)_Undersampling_Me...
97 Support Vector Machines (SVM)_Undersampling_Me...
98 Support Vector Machines (SVM)_Undersampling_Me...
99 Support Vector Machines (SVM)_Undersampling_Me...
100 Support Vector Machines (SVM)_Undersampling_Me...
101 Support Vector Machines (SVM)_Undersampling_Me...
102 Support Vector Machines (SVM)_Undersampling_Me...
103 Support Vector Machines (SVM)_Undersampling_Me...
104 Support Vector Machines (SVM)_Undersampling_Me...
105 Support Vector Machines (SVM)_Undersampling_Me...
106 Support Vector Machines (SVM)_Undersampling_Me...
107 Support Vector Machines (SVM)_Undersampling_Me...
108 Support Vector Machines (SVM)_Undersampling_Me...
109 Support Vector Machines (SVM)_Undersampling_Me... ,
Algorithm Metrics Hyperparameters \
0 Support Vector Machines (SVM) 0.280221 NaN
1 Support Vector Machines (SVM) 0.071429 NaN
2 Support Vector Machines (SVM) 0.877637 NaN
3 Support Vector Machines (SVM) 0.132105 NaN
4 Support Vector Machines (SVM) 0.617127 NaN
5 Support Vector Machines (SVM) 0.214396 NaN
6 Support Vector Machines (SVM) 0.234253 NaN
7 Support Vector Machines (SVM) NaN 1.0
8 Support Vector Machines (SVM) NaN False
9 Support Vector Machines (SVM) NaN 200
10 Support Vector Machines (SVM) NaN None
11 Support Vector Machines (SVM) NaN 0.0
12 Support Vector Machines (SVM) NaN ovr
13 Support Vector Machines (SVM) NaN 3
14 Support Vector Machines (SVM) NaN scale
15 Support Vector Machines (SVM) NaN rbf
16 Support Vector Machines (SVM) NaN -1
17 Support Vector Machines (SVM) NaN True
18 Support Vector Machines (SVM) NaN None
19 Support Vector Machines (SVM) NaN True
20 Support Vector Machines (SVM) NaN 0.001
21 Support Vector Machines (SVM) NaN False
22 Support Vector Machines (SVM) 0.241770 NaN
23 Support Vector Machines (SVM) 0.049751 NaN
24 Support Vector Machines (SVM) 0.914634 NaN
25 Support Vector Machines (SVM) 0.094369 NaN
26 Support Vector Machines (SVM) 0.646067 NaN
27 Support Vector Machines (SVM) 0.276926 NaN
28 Support Vector Machines (SVM) 0.292134 NaN
29 Support Vector Machines (SVM) NaN 1.0
30 Support Vector Machines (SVM) NaN False
31 Support Vector Machines (SVM) NaN 200
32 Support Vector Machines (SVM) NaN None
33 Support Vector Machines (SVM) NaN 0.0
34 Support Vector Machines (SVM) NaN ovr
35 Support Vector Machines (SVM) NaN 3
36 Support Vector Machines (SVM) NaN scale
37 Support Vector Machines (SVM) NaN rbf
38 Support Vector Machines (SVM) NaN -1
39 Support Vector Machines (SVM) NaN True
40 Support Vector Machines (SVM) NaN None
41 Support Vector Machines (SVM) NaN True
42 Support Vector Machines (SVM) NaN 0.001
43 Support Vector Machines (SVM) NaN False
44 Support Vector Machines (SVM) 0.306031 NaN
45 Support Vector Machines (SVM) 0.058442 NaN
46 Support Vector Machines (SVM) 0.866310 NaN
47 Support Vector Machines (SVM) 0.109496 NaN
48 Support Vector Machines (SVM) 0.588730 NaN
49 Support Vector Machines (SVM) 0.187069 NaN
50 Support Vector Machines (SVM) 0.177460 NaN
51 Support Vector Machines (SVM) NaN 1.0
52 Support Vector Machines (SVM) NaN False
53 Support Vector Machines (SVM) NaN 200
54 Support Vector Machines (SVM) NaN None
55 Support Vector Machines (SVM) NaN 0.0
56 Support Vector Machines (SVM) NaN ovr
57 Support Vector Machines (SVM) NaN 3
58 Support Vector Machines (SVM) NaN scale
59 Support Vector Machines (SVM) NaN rbf
60 Support Vector Machines (SVM) NaN -1
61 Support Vector Machines (SVM) NaN True
62 Support Vector Machines (SVM) NaN None
63 Support Vector Machines (SVM) NaN True
64 Support Vector Machines (SVM) NaN 0.001
65 Support Vector Machines (SVM) NaN False
66 Support Vector Machines (SVM) 0.290832 NaN
67 Support Vector Machines (SVM) 0.060563 NaN
68 Support Vector Machines (SVM) 0.877551 NaN
69 Support Vector Machines (SVM) 0.113307 NaN
70 Support Vector Machines (SVM) 0.596604 NaN
71 Support Vector Machines (SVM) 0.231871 NaN
72 Support Vector Machines (SVM) 0.193207 NaN
73 Support Vector Machines (SVM) NaN 1.0
74 Support Vector Machines (SVM) NaN False
75 Support Vector Machines (SVM) NaN 200
76 Support Vector Machines (SVM) NaN None
77 Support Vector Machines (SVM) NaN 0.0
78 Support Vector Machines (SVM) NaN ovr
79 Support Vector Machines (SVM) NaN 3
80 Support Vector Machines (SVM) NaN scale
81 Support Vector Machines (SVM) NaN rbf
82 Support Vector Machines (SVM) NaN -1
83 Support Vector Machines (SVM) NaN True
84 Support Vector Machines (SVM) NaN None
85 Support Vector Machines (SVM) NaN True
86 Support Vector Machines (SVM) NaN 0.001
87 Support Vector Machines (SVM) NaN False
88 Support Vector Machines (SVM) 0.288725 NaN
89 Support Vector Machines (SVM) 0.068150 NaN
90 Support Vector Machines (SVM) 0.907407 NaN
91 Support Vector Machines (SVM) 0.126779 NaN
92 Support Vector Machines (SVM) 0.614877 NaN
93 Support Vector Machines (SVM) 0.242732 NaN
94 Support Vector Machines (SVM) 0.229754 NaN
95 Support Vector Machines (SVM) NaN 1.0
96 Support Vector Machines (SVM) NaN False
97 Support Vector Machines (SVM) NaN 200
98 Support Vector Machines (SVM) NaN None
99 Support Vector Machines (SVM) NaN 0.0
100 Support Vector Machines (SVM) NaN ovr
101 Support Vector Machines (SVM) NaN 3
102 Support Vector Machines (SVM) NaN scale
103 Support Vector Machines (SVM) NaN rbf
104 Support Vector Machines (SVM) NaN -1
105 Support Vector Machines (SVM) NaN True
106 Support Vector Machines (SVM) NaN None
107 Support Vector Machines (SVM) NaN True
108 Support Vector Machines (SVM) NaN 0.001
109 Support Vector Machines (SVM) NaN False
Imputation Technique Imbalance Class Technique \
0 Median Imputation Undersampling
1 Median Imputation Undersampling
2 Median Imputation Undersampling
3 Median Imputation Undersampling
4 Median Imputation Undersampling
5 Median Imputation Undersampling
6 Median Imputation Undersampling
7 Median Imputation Undersampling
8 Median Imputation Undersampling
9 Median Imputation Undersampling
10 Median Imputation Undersampling
11 Median Imputation Undersampling
12 Median Imputation Undersampling
13 Median Imputation Undersampling
14 Median Imputation Undersampling
15 Median Imputation Undersampling
16 Median Imputation Undersampling
17 Median Imputation Undersampling
18 Median Imputation Undersampling
19 Median Imputation Undersampling
20 Median Imputation Undersampling
21 Median Imputation Undersampling
22 Median Imputation Undersampling
23 Median Imputation Undersampling
24 Median Imputation Undersampling
25 Median Imputation Undersampling
26 Median Imputation Undersampling
27 Median Imputation Undersampling
28 Median Imputation Undersampling
29 Median Imputation Undersampling
30 Median Imputation Undersampling
31 Median Imputation Undersampling
32 Median Imputation Undersampling
33 Median Imputation Undersampling
34 Median Imputation Undersampling
35 Median Imputation Undersampling
36 Median Imputation Undersampling
37 Median Imputation Undersampling
38 Median Imputation Undersampling
39 Median Imputation Undersampling
40 Median Imputation Undersampling
41 Median Imputation Undersampling
42 Median Imputation Undersampling
43 Median Imputation Undersampling
44 Median Imputation Undersampling
45 Median Imputation Undersampling
46 Median Imputation Undersampling
47 Median Imputation Undersampling
48 Median Imputation Undersampling
49 Median Imputation Undersampling
50 Median Imputation Undersampling
51 Median Imputation Undersampling
52 Median Imputation Undersampling
53 Median Imputation Undersampling
54 Median Imputation Undersampling
55 Median Imputation Undersampling
56 Median Imputation Undersampling
57 Median Imputation Undersampling
58 Median Imputation Undersampling
59 Median Imputation Undersampling
60 Median Imputation Undersampling
61 Median Imputation Undersampling
62 Median Imputation Undersampling
63 Median Imputation Undersampling
64 Median Imputation Undersampling
65 Median Imputation Undersampling
66 Median Imputation Undersampling
67 Median Imputation Undersampling
68 Median Imputation Undersampling
69 Median Imputation Undersampling
70 Median Imputation Undersampling
71 Median Imputation Undersampling
72 Median Imputation Undersampling
73 Median Imputation Undersampling
74 Median Imputation Undersampling
75 Median Imputation Undersampling
76 Median Imputation Undersampling
77 Median Imputation Undersampling
78 Median Imputation Undersampling
79 Median Imputation Undersampling
80 Median Imputation Undersampling
81 Median Imputation Undersampling
82 Median Imputation Undersampling
83 Median Imputation Undersampling
84 Median Imputation Undersampling
85 Median Imputation Undersampling
86 Median Imputation Undersampling
87 Median Imputation Undersampling
88 Median Imputation Undersampling
89 Median Imputation Undersampling
90 Median Imputation Undersampling
91 Median Imputation Undersampling
92 Median Imputation Undersampling
93 Median Imputation Undersampling
94 Median Imputation Undersampling
95 Median Imputation Undersampling
96 Median Imputation Undersampling
97 Median Imputation Undersampling
98 Median Imputation Undersampling
99 Median Imputation Undersampling
100 Median Imputation Undersampling
101 Median Imputation Undersampling
102 Median Imputation Undersampling
103 Median Imputation Undersampling
104 Median Imputation Undersampling
105 Median Imputation Undersampling
106 Median Imputation Undersampling
107 Median Imputation Undersampling
108 Median Imputation Undersampling
109 Median Imputation Undersampling
Model Unique Code
0 Support Vector Machines (SVM)_Undersampling_Me...
1 Support Vector Machines (SVM)_Undersampling_Me...
2 Support Vector Machines (SVM)_Undersampling_Me...
3 Support Vector Machines (SVM)_Undersampling_Me...
4 Support Vector Machines (SVM)_Undersampling_Me...
5 Support Vector Machines (SVM)_Undersampling_Me...
6 Support Vector Machines (SVM)_Undersampling_Me...
7 Support Vector Machines (SVM)_Undersampling_Me...
8 Support Vector Machines (SVM)_Undersampling_Me...
9 Support Vector Machines (SVM)_Undersampling_Me...
10 Support Vector Machines (SVM)_Undersampling_Me...
11 Support Vector Machines (SVM)_Undersampling_Me...
12 Support Vector Machines (SVM)_Undersampling_Me...
13 Support Vector Machines (SVM)_Undersampling_Me...
14 Support Vector Machines (SVM)_Undersampling_Me...
15 Support Vector Machines (SVM)_Undersampling_Me...
16 Support Vector Machines (SVM)_Undersampling_Me...
17 Support Vector Machines (SVM)_Undersampling_Me...
18 Support Vector Machines (SVM)_Undersampling_Me...
19 Support Vector Machines (SVM)_Undersampling_Me...
20 Support Vector Machines (SVM)_Undersampling_Me...
21 Support Vector Machines (SVM)_Undersampling_Me...
22 Support Vector Machines (SVM)_Undersampling_Me...
23 Support Vector Machines (SVM)_Undersampling_Me...
24 Support Vector Machines (SVM)_Undersampling_Me...
25 Support Vector Machines (SVM)_Undersampling_Me...
26 Support Vector Machines (SVM)_Undersampling_Me...
27 Support Vector Machines (SVM)_Undersampling_Me...
28 Support Vector Machines (SVM)_Undersampling_Me...
29 Support Vector Machines (SVM)_Undersampling_Me...
30 Support Vector Machines (SVM)_Undersampling_Me...
31 Support Vector Machines (SVM)_Undersampling_Me...
32 Support Vector Machines (SVM)_Undersampling_Me...
33 Support Vector Machines (SVM)_Undersampling_Me...
34 Support Vector Machines (SVM)_Undersampling_Me...
35 Support Vector Machines (SVM)_Undersampling_Me...
36 Support Vector Machines (SVM)_Undersampling_Me...
37 Support Vector Machines (SVM)_Undersampling_Me...
38 Support Vector Machines (SVM)_Undersampling_Me...
39 Support Vector Machines (SVM)_Undersampling_Me...
40 Support Vector Machines (SVM)_Undersampling_Me...
41 Support Vector Machines (SVM)_Undersampling_Me...
42 Support Vector Machines (SVM)_Undersampling_Me...
43 Support Vector Machines (SVM)_Undersampling_Me...
44 Support Vector Machines (SVM)_Undersampling_Me...
45 Support Vector Machines (SVM)_Undersampling_Me...
46 Support Vector Machines (SVM)_Undersampling_Me...
47 Support Vector Machines (SVM)_Undersampling_Me...
48 Support Vector Machines (SVM)_Undersampling_Me...
49 Support Vector Machines (SVM)_Undersampling_Me...
50 Support Vector Machines (SVM)_Undersampling_Me...
51 Support Vector Machines (SVM)_Undersampling_Me...
52 Support Vector Machines (SVM)_Undersampling_Me...
53 Support Vector Machines (SVM)_Undersampling_Me...
54 Support Vector Machines (SVM)_Undersampling_Me...
55 Support Vector Machines (SVM)_Undersampling_Me...
56 Support Vector Machines (SVM)_Undersampling_Me...
57 Support Vector Machines (SVM)_Undersampling_Me...
58 Support Vector Machines (SVM)_Undersampling_Me...
59 Support Vector Machines (SVM)_Undersampling_Me...
60 Support Vector Machines (SVM)_Undersampling_Me...
61 Support Vector Machines (SVM)_Undersampling_Me...
62 Support Vector Machines (SVM)_Undersampling_Me...
63 Support Vector Machines (SVM)_Undersampling_Me...
64 Support Vector Machines (SVM)_Undersampling_Me...
65 Support Vector Machines (SVM)_Undersampling_Me...
66 Support Vector Machines (SVM)_Undersampling_Me...
67 Support Vector Machines (SVM)_Undersampling_Me...
68 Support Vector Machines (SVM)_Undersampling_Me...
69 Support Vector Machines (SVM)_Undersampling_Me...
70 Support Vector Machines (SVM)_Undersampling_Me...
71 Support Vector Machines (SVM)_Undersampling_Me...
72 Support Vector Machines (SVM)_Undersampling_Me...
73 Support Vector Machines (SVM)_Undersampling_Me...
74 Support Vector Machines (SVM)_Undersampling_Me...
75 Support Vector Machines (SVM)_Undersampling_Me...
76 Support Vector Machines (SVM)_Undersampling_Me...
77 Support Vector Machines (SVM)_Undersampling_Me...
78 Support Vector Machines (SVM)_Undersampling_Me...
79 Support Vector Machines (SVM)_Undersampling_Me...
80 Support Vector Machines (SVM)_Undersampling_Me...
81 Support Vector Machines (SVM)_Undersampling_Me...
82 Support Vector Machines (SVM)_Undersampling_Me...
83 Support Vector Machines (SVM)_Undersampling_Me...
84 Support Vector Machines (SVM)_Undersampling_Me...
85 Support Vector Machines (SVM)_Undersampling_Me...
86 Support Vector Machines (SVM)_Undersampling_Me...
87 Support Vector Machines (SVM)_Undersampling_Me...
88 Support Vector Machines (SVM)_Undersampling_Me...
89 Support Vector Machines (SVM)_Undersampling_Me...
90 Support Vector Machines (SVM)_Undersampling_Me...
91 Support Vector Machines (SVM)_Undersampling_Me...
92 Support Vector Machines (SVM)_Undersampling_Me...
93 Support Vector Machines (SVM)_Undersampling_Me...
94 Support Vector Machines (SVM)_Undersampling_Me...
95 Support Vector Machines (SVM)_Undersampling_Me...
96 Support Vector Machines (SVM)_Undersampling_Me...
97 Support Vector Machines (SVM)_Undersampling_Me...
98 Support Vector Machines (SVM)_Undersampling_Me...
99 Support Vector Machines (SVM)_Undersampling_Me...
100 Support Vector Machines (SVM)_Undersampling_Me...
101 Support Vector Machines (SVM)_Undersampling_Me...
102 Support Vector Machines (SVM)_Undersampling_Me...
103 Support Vector Machines (SVM)_Undersampling_Me...
104 Support Vector Machines (SVM)_Undersampling_Me...
105 Support Vector Machines (SVM)_Undersampling_Me...
106 Support Vector Machines (SVM)_Undersampling_Me...
107 Support Vector Machines (SVM)_Undersampling_Me...
108 Support Vector Machines (SVM)_Undersampling_Me...
109 Support Vector Machines (SVM)_Undersampling_Me... ,
Algorithm Metrics Hyperparameters \
0 Support Vector Machines (SVM) 0.355017 NaN
1 Support Vector Machines (SVM) 0.076894 NaN
2 Support Vector Machines (SVM) 0.848101 NaN
3 Support Vector Machines (SVM) 0.141003 NaN
4 Support Vector Machines (SVM) 0.641670 NaN
5 Support Vector Machines (SVM) 0.229685 NaN
6 Support Vector Machines (SVM) 0.283340 NaN
7 Support Vector Machines (SVM) NaN 1.0
8 Support Vector Machines (SVM) NaN False
9 Support Vector Machines (SVM) NaN 200
10 Support Vector Machines (SVM) NaN None
11 Support Vector Machines (SVM) NaN 0.0
12 Support Vector Machines (SVM) NaN ovr
13 Support Vector Machines (SVM) NaN 3
14 Support Vector Machines (SVM) NaN scale
15 Support Vector Machines (SVM) NaN rbf
16 Support Vector Machines (SVM) NaN -1
17 Support Vector Machines (SVM) NaN True
18 Support Vector Machines (SVM) NaN None
19 Support Vector Machines (SVM) NaN True
20 Support Vector Machines (SVM) NaN 0.001
21 Support Vector Machines (SVM) NaN False
22 Support Vector Machines (SVM) 0.331841 NaN
23 Support Vector Machines (SVM) 0.054784 NaN
24 Support Vector Machines (SVM) 0.890244 NaN
25 Support Vector Machines (SVM) 0.103217 NaN
26 Support Vector Machines (SVM) 0.667777 NaN
27 Support Vector Machines (SVM) 0.273460 NaN
28 Support Vector Machines (SVM) 0.335554 NaN
29 Support Vector Machines (SVM) NaN 1.0
30 Support Vector Machines (SVM) NaN False
31 Support Vector Machines (SVM) NaN 200
32 Support Vector Machines (SVM) NaN None
33 Support Vector Machines (SVM) NaN 0.0
34 Support Vector Machines (SVM) NaN ovr
35 Support Vector Machines (SVM) NaN 3
36 Support Vector Machines (SVM) NaN scale
37 Support Vector Machines (SVM) NaN rbf
38 Support Vector Machines (SVM) NaN -1
39 Support Vector Machines (SVM) NaN True
40 Support Vector Machines (SVM) NaN None
41 Support Vector Machines (SVM) NaN True
42 Support Vector Machines (SVM) NaN 0.001
43 Support Vector Machines (SVM) NaN False
44 Support Vector Machines (SVM) 0.345273 NaN
45 Support Vector Machines (SVM) 0.059747 NaN
46 Support Vector Machines (SVM) 0.834225 NaN
47 Support Vector Machines (SVM) 0.111508 NaN
48 Support Vector Machines (SVM) 0.652221 NaN
49 Support Vector Machines (SVM) 0.265315 NaN
50 Support Vector Machines (SVM) 0.304441 NaN
51 Support Vector Machines (SVM) NaN 1.0
52 Support Vector Machines (SVM) NaN False
53 Support Vector Machines (SVM) NaN 200
54 Support Vector Machines (SVM) NaN None
55 Support Vector Machines (SVM) NaN 0.0
56 Support Vector Machines (SVM) NaN ovr
57 Support Vector Machines (SVM) NaN 3
58 Support Vector Machines (SVM) NaN scale
59 Support Vector Machines (SVM) NaN rbf
60 Support Vector Machines (SVM) NaN -1
61 Support Vector Machines (SVM) NaN True
62 Support Vector Machines (SVM) NaN None
63 Support Vector Machines (SVM) NaN True
64 Support Vector Machines (SVM) NaN 0.001
65 Support Vector Machines (SVM) NaN False
66 Support Vector Machines (SVM) 0.328504 NaN
67 Support Vector Machines (SVM) 0.064421 NaN
68 Support Vector Machines (SVM) 0.887755 NaN
69 Support Vector Machines (SVM) 0.120124 NaN
70 Support Vector Machines (SVM) 0.647746 NaN
71 Support Vector Machines (SVM) 0.237738 NaN
72 Support Vector Machines (SVM) 0.295492 NaN
73 Support Vector Machines (SVM) NaN 1.0
74 Support Vector Machines (SVM) NaN False
75 Support Vector Machines (SVM) NaN 200
76 Support Vector Machines (SVM) NaN None
77 Support Vector Machines (SVM) NaN 0.0
78 Support Vector Machines (SVM) NaN ovr
79 Support Vector Machines (SVM) NaN 3
80 Support Vector Machines (SVM) NaN scale
81 Support Vector Machines (SVM) NaN rbf
82 Support Vector Machines (SVM) NaN -1
83 Support Vector Machines (SVM) NaN True
84 Support Vector Machines (SVM) NaN None
85 Support Vector Machines (SVM) NaN True
86 Support Vector Machines (SVM) NaN 0.001
87 Support Vector Machines (SVM) NaN False
88 Support Vector Machines (SVM) 0.337724 NaN
89 Support Vector Machines (SVM) 0.069985 NaN
90 Support Vector Machines (SVM) 0.865741 NaN
91 Support Vector Machines (SVM) 0.129501 NaN
92 Support Vector Machines (SVM) 0.673923 NaN
93 Support Vector Machines (SVM) 0.290306 NaN
94 Support Vector Machines (SVM) 0.347846 NaN
95 Support Vector Machines (SVM) NaN 1.0
96 Support Vector Machines (SVM) NaN False
97 Support Vector Machines (SVM) NaN 200
98 Support Vector Machines (SVM) NaN None
99 Support Vector Machines (SVM) NaN 0.0
100 Support Vector Machines (SVM) NaN ovr
101 Support Vector Machines (SVM) NaN 3
102 Support Vector Machines (SVM) NaN scale
103 Support Vector Machines (SVM) NaN rbf
104 Support Vector Machines (SVM) NaN -1
105 Support Vector Machines (SVM) NaN True
106 Support Vector Machines (SVM) NaN None
107 Support Vector Machines (SVM) NaN True
108 Support Vector Machines (SVM) NaN 0.001
109 Support Vector Machines (SVM) NaN False
Imputation Technique Imbalance Class Technique \
0 Zero Imputation Hybrid Sampling (SMOTEENN)
1 Zero Imputation Hybrid Sampling (SMOTEENN)
2 Zero Imputation Hybrid Sampling (SMOTEENN)
3 Zero Imputation Hybrid Sampling (SMOTEENN)
4 Zero Imputation Hybrid Sampling (SMOTEENN)
5 Zero Imputation Hybrid Sampling (SMOTEENN)
6 Zero Imputation Hybrid Sampling (SMOTEENN)
7 Zero Imputation Hybrid Sampling (SMOTEENN)
8 Zero Imputation Hybrid Sampling (SMOTEENN)
9 Zero Imputation Hybrid Sampling (SMOTEENN)
10 Zero Imputation Hybrid Sampling (SMOTEENN)
11 Zero Imputation Hybrid Sampling (SMOTEENN)
12 Zero Imputation Hybrid Sampling (SMOTEENN)
13 Zero Imputation Hybrid Sampling (SMOTEENN)
14 Zero Imputation Hybrid Sampling (SMOTEENN)
15 Zero Imputation Hybrid Sampling (SMOTEENN)
16 Zero Imputation Hybrid Sampling (SMOTEENN)
17 Zero Imputation Hybrid Sampling (SMOTEENN)
18 Zero Imputation Hybrid Sampling (SMOTEENN)
19 Zero Imputation Hybrid Sampling (SMOTEENN)
20 Zero Imputation Hybrid Sampling (SMOTEENN)
21 Zero Imputation Hybrid Sampling (SMOTEENN)
22 Zero Imputation Hybrid Sampling (SMOTEENN)
23 Zero Imputation Hybrid Sampling (SMOTEENN)
24 Zero Imputation Hybrid Sampling (SMOTEENN)
25 Zero Imputation Hybrid Sampling (SMOTEENN)
26 Zero Imputation Hybrid Sampling (SMOTEENN)
27 Zero Imputation Hybrid Sampling (SMOTEENN)
28 Zero Imputation Hybrid Sampling (SMOTEENN)
29 Zero Imputation Hybrid Sampling (SMOTEENN)
30 Zero Imputation Hybrid Sampling (SMOTEENN)
31 Zero Imputation Hybrid Sampling (SMOTEENN)
32 Zero Imputation Hybrid Sampling (SMOTEENN)
33 Zero Imputation Hybrid Sampling (SMOTEENN)
34 Zero Imputation Hybrid Sampling (SMOTEENN)
35 Zero Imputation Hybrid Sampling (SMOTEENN)
36 Zero Imputation Hybrid Sampling (SMOTEENN)
37 Zero Imputation Hybrid Sampling (SMOTEENN)
38 Zero Imputation Hybrid Sampling (SMOTEENN)
39 Zero Imputation Hybrid Sampling (SMOTEENN)
40 Zero Imputation Hybrid Sampling (SMOTEENN)
41 Zero Imputation Hybrid Sampling (SMOTEENN)
42 Zero Imputation Hybrid Sampling (SMOTEENN)
43 Zero Imputation Hybrid Sampling (SMOTEENN)
44 Zero Imputation Hybrid Sampling (SMOTEENN)
45 Zero Imputation Hybrid Sampling (SMOTEENN)
46 Zero Imputation Hybrid Sampling (SMOTEENN)
47 Zero Imputation Hybrid Sampling (SMOTEENN)
48 Zero Imputation Hybrid Sampling (SMOTEENN)
49 Zero Imputation Hybrid Sampling (SMOTEENN)
50 Zero Imputation Hybrid Sampling (SMOTEENN)
51 Zero Imputation Hybrid Sampling (SMOTEENN)
52 Zero Imputation Hybrid Sampling (SMOTEENN)
53 Zero Imputation Hybrid Sampling (SMOTEENN)
54 Zero Imputation Hybrid Sampling (SMOTEENN)
55 Zero Imputation Hybrid Sampling (SMOTEENN)
56 Zero Imputation Hybrid Sampling (SMOTEENN)
57 Zero Imputation Hybrid Sampling (SMOTEENN)
58 Zero Imputation Hybrid Sampling (SMOTEENN)
59 Zero Imputation Hybrid Sampling (SMOTEENN)
60 Zero Imputation Hybrid Sampling (SMOTEENN)
61 Zero Imputation Hybrid Sampling (SMOTEENN)
62 Zero Imputation Hybrid Sampling (SMOTEENN)
63 Zero Imputation Hybrid Sampling (SMOTEENN)
64 Zero Imputation Hybrid Sampling (SMOTEENN)
65 Zero Imputation Hybrid Sampling (SMOTEENN)
66 Zero Imputation Hybrid Sampling (SMOTEENN)
67 Zero Imputation Hybrid Sampling (SMOTEENN)
68 Zero Imputation Hybrid Sampling (SMOTEENN)
69 Zero Imputation Hybrid Sampling (SMOTEENN)
70 Zero Imputation Hybrid Sampling (SMOTEENN)
71 Zero Imputation Hybrid Sampling (SMOTEENN)
72 Zero Imputation Hybrid Sampling (SMOTEENN)
73 Zero Imputation Hybrid Sampling (SMOTEENN)
74 Zero Imputation Hybrid Sampling (SMOTEENN)
75 Zero Imputation Hybrid Sampling (SMOTEENN)
76 Zero Imputation Hybrid Sampling (SMOTEENN)
77 Zero Imputation Hybrid Sampling (SMOTEENN)
78 Zero Imputation Hybrid Sampling (SMOTEENN)
79 Zero Imputation Hybrid Sampling (SMOTEENN)
80 Zero Imputation Hybrid Sampling (SMOTEENN)
81 Zero Imputation Hybrid Sampling (SMOTEENN)
82 Zero Imputation Hybrid Sampling (SMOTEENN)
83 Zero Imputation Hybrid Sampling (SMOTEENN)
84 Zero Imputation Hybrid Sampling (SMOTEENN)
85 Zero Imputation Hybrid Sampling (SMOTEENN)
86 Zero Imputation Hybrid Sampling (SMOTEENN)
87 Zero Imputation Hybrid Sampling (SMOTEENN)
88 Zero Imputation Hybrid Sampling (SMOTEENN)
89 Zero Imputation Hybrid Sampling (SMOTEENN)
90 Zero Imputation Hybrid Sampling (SMOTEENN)
91 Zero Imputation Hybrid Sampling (SMOTEENN)
92 Zero Imputation Hybrid Sampling (SMOTEENN)
93 Zero Imputation Hybrid Sampling (SMOTEENN)
94 Zero Imputation Hybrid Sampling (SMOTEENN)
95 Zero Imputation Hybrid Sampling (SMOTEENN)
96 Zero Imputation Hybrid Sampling (SMOTEENN)
97 Zero Imputation Hybrid Sampling (SMOTEENN)
98 Zero Imputation Hybrid Sampling (SMOTEENN)
99 Zero Imputation Hybrid Sampling (SMOTEENN)
100 Zero Imputation Hybrid Sampling (SMOTEENN)
101 Zero Imputation Hybrid Sampling (SMOTEENN)
102 Zero Imputation Hybrid Sampling (SMOTEENN)
103 Zero Imputation Hybrid Sampling (SMOTEENN)
104 Zero Imputation Hybrid Sampling (SMOTEENN)
105 Zero Imputation Hybrid Sampling (SMOTEENN)
106 Zero Imputation Hybrid Sampling (SMOTEENN)
107 Zero Imputation Hybrid Sampling (SMOTEENN)
108 Zero Imputation Hybrid Sampling (SMOTEENN)
109 Zero Imputation Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Support Vector Machines (SVM)_Hybrid Sampling ...
1 Support Vector Machines (SVM)_Hybrid Sampling ...
2 Support Vector Machines (SVM)_Hybrid Sampling ...
3 Support Vector Machines (SVM)_Hybrid Sampling ...
4 Support Vector Machines (SVM)_Hybrid Sampling ...
5 Support Vector Machines (SVM)_Hybrid Sampling ...
6 Support Vector Machines (SVM)_Hybrid Sampling ...
7 Support Vector Machines (SVM)_Hybrid Sampling ...
8 Support Vector Machines (SVM)_Hybrid Sampling ...
9 Support Vector Machines (SVM)_Hybrid Sampling ...
10 Support Vector Machines (SVM)_Hybrid Sampling ...
11 Support Vector Machines (SVM)_Hybrid Sampling ...
12 Support Vector Machines (SVM)_Hybrid Sampling ...
13 Support Vector Machines (SVM)_Hybrid Sampling ...
14 Support Vector Machines (SVM)_Hybrid Sampling ...
15 Support Vector Machines (SVM)_Hybrid Sampling ...
16 Support Vector Machines (SVM)_Hybrid Sampling ...
17 Support Vector Machines (SVM)_Hybrid Sampling ...
18 Support Vector Machines (SVM)_Hybrid Sampling ...
19 Support Vector Machines (SVM)_Hybrid Sampling ...
20 Support Vector Machines (SVM)_Hybrid Sampling ...
21 Support Vector Machines (SVM)_Hybrid Sampling ...
22 Support Vector Machines (SVM)_Hybrid Sampling ...
23 Support Vector Machines (SVM)_Hybrid Sampling ...
24 Support Vector Machines (SVM)_Hybrid Sampling ...
25 Support Vector Machines (SVM)_Hybrid Sampling ...
26 Support Vector Machines (SVM)_Hybrid Sampling ...
27 Support Vector Machines (SVM)_Hybrid Sampling ...
28 Support Vector Machines (SVM)_Hybrid Sampling ...
29 Support Vector Machines (SVM)_Hybrid Sampling ...
30 Support Vector Machines (SVM)_Hybrid Sampling ...
31 Support Vector Machines (SVM)_Hybrid Sampling ...
32 Support Vector Machines (SVM)_Hybrid Sampling ...
33 Support Vector Machines (SVM)_Hybrid Sampling ...
34 Support Vector Machines (SVM)_Hybrid Sampling ...
35 Support Vector Machines (SVM)_Hybrid Sampling ...
36 Support Vector Machines (SVM)_Hybrid Sampling ...
37 Support Vector Machines (SVM)_Hybrid Sampling ...
38 Support Vector Machines (SVM)_Hybrid Sampling ...
39 Support Vector Machines (SVM)_Hybrid Sampling ...
40 Support Vector Machines (SVM)_Hybrid Sampling ...
41 Support Vector Machines (SVM)_Hybrid Sampling ...
42 Support Vector Machines (SVM)_Hybrid Sampling ...
43 Support Vector Machines (SVM)_Hybrid Sampling ...
44 Support Vector Machines (SVM)_Hybrid Sampling ...
45 Support Vector Machines (SVM)_Hybrid Sampling ...
46 Support Vector Machines (SVM)_Hybrid Sampling ...
47 Support Vector Machines (SVM)_Hybrid Sampling ...
48 Support Vector Machines (SVM)_Hybrid Sampling ...
49 Support Vector Machines (SVM)_Hybrid Sampling ...
50 Support Vector Machines (SVM)_Hybrid Sampling ...
51 Support Vector Machines (SVM)_Hybrid Sampling ...
52 Support Vector Machines (SVM)_Hybrid Sampling ...
53 Support Vector Machines (SVM)_Hybrid Sampling ...
54 Support Vector Machines (SVM)_Hybrid Sampling ...
55 Support Vector Machines (SVM)_Hybrid Sampling ...
56 Support Vector Machines (SVM)_Hybrid Sampling ...
57 Support Vector Machines (SVM)_Hybrid Sampling ...
58 Support Vector Machines (SVM)_Hybrid Sampling ...
59 Support Vector Machines (SVM)_Hybrid Sampling ...
60 Support Vector Machines (SVM)_Hybrid Sampling ...
61 Support Vector Machines (SVM)_Hybrid Sampling ...
62 Support Vector Machines (SVM)_Hybrid Sampling ...
63 Support Vector Machines (SVM)_Hybrid Sampling ...
64 Support Vector Machines (SVM)_Hybrid Sampling ...
65 Support Vector Machines (SVM)_Hybrid Sampling ...
66 Support Vector Machines (SVM)_Hybrid Sampling ...
67 Support Vector Machines (SVM)_Hybrid Sampling ...
68 Support Vector Machines (SVM)_Hybrid Sampling ...
69 Support Vector Machines (SVM)_Hybrid Sampling ...
70 Support Vector Machines (SVM)_Hybrid Sampling ...
71 Support Vector Machines (SVM)_Hybrid Sampling ...
72 Support Vector Machines (SVM)_Hybrid Sampling ...
73 Support Vector Machines (SVM)_Hybrid Sampling ...
74 Support Vector Machines (SVM)_Hybrid Sampling ...
75 Support Vector Machines (SVM)_Hybrid Sampling ...
76 Support Vector Machines (SVM)_Hybrid Sampling ...
77 Support Vector Machines (SVM)_Hybrid Sampling ...
78 Support Vector Machines (SVM)_Hybrid Sampling ...
79 Support Vector Machines (SVM)_Hybrid Sampling ...
80 Support Vector Machines (SVM)_Hybrid Sampling ...
81 Support Vector Machines (SVM)_Hybrid Sampling ...
82 Support Vector Machines (SVM)_Hybrid Sampling ...
83 Support Vector Machines (SVM)_Hybrid Sampling ...
84 Support Vector Machines (SVM)_Hybrid Sampling ...
85 Support Vector Machines (SVM)_Hybrid Sampling ...
86 Support Vector Machines (SVM)_Hybrid Sampling ...
87 Support Vector Machines (SVM)_Hybrid Sampling ...
88 Support Vector Machines (SVM)_Hybrid Sampling ...
89 Support Vector Machines (SVM)_Hybrid Sampling ...
90 Support Vector Machines (SVM)_Hybrid Sampling ...
91 Support Vector Machines (SVM)_Hybrid Sampling ...
92 Support Vector Machines (SVM)_Hybrid Sampling ...
93 Support Vector Machines (SVM)_Hybrid Sampling ...
94 Support Vector Machines (SVM)_Hybrid Sampling ...
95 Support Vector Machines (SVM)_Hybrid Sampling ...
96 Support Vector Machines (SVM)_Hybrid Sampling ...
97 Support Vector Machines (SVM)_Hybrid Sampling ...
98 Support Vector Machines (SVM)_Hybrid Sampling ...
99 Support Vector Machines (SVM)_Hybrid Sampling ...
100 Support Vector Machines (SVM)_Hybrid Sampling ...
101 Support Vector Machines (SVM)_Hybrid Sampling ...
102 Support Vector Machines (SVM)_Hybrid Sampling ...
103 Support Vector Machines (SVM)_Hybrid Sampling ...
104 Support Vector Machines (SVM)_Hybrid Sampling ...
105 Support Vector Machines (SVM)_Hybrid Sampling ...
106 Support Vector Machines (SVM)_Hybrid Sampling ...
107 Support Vector Machines (SVM)_Hybrid Sampling ...
108 Support Vector Machines (SVM)_Hybrid Sampling ...
109 Support Vector Machines (SVM)_Hybrid Sampling ... ,
Algorithm Metrics Hyperparameters \
0 Support Vector Machines (SVM) 0.203582 NaN
1 Support Vector Machines (SVM) 0.066563 NaN
2 Support Vector Machines (SVM) 0.902954 NaN
3 Support Vector Machines (SVM) 0.123986 NaN
4 Support Vector Machines (SVM) 0.622080 NaN
5 Support Vector Machines (SVM) 0.219860 NaN
6 Support Vector Machines (SVM) 0.244159 NaN
7 Support Vector Machines (SVM) NaN 1.0
8 Support Vector Machines (SVM) NaN False
9 Support Vector Machines (SVM) NaN 200
10 Support Vector Machines (SVM) NaN None
11 Support Vector Machines (SVM) NaN 0.0
12 Support Vector Machines (SVM) NaN ovr
13 Support Vector Machines (SVM) NaN 3
14 Support Vector Machines (SVM) NaN scale
15 Support Vector Machines (SVM) NaN rbf
16 Support Vector Machines (SVM) NaN -1
17 Support Vector Machines (SVM) NaN True
18 Support Vector Machines (SVM) NaN None
19 Support Vector Machines (SVM) NaN True
20 Support Vector Machines (SVM) NaN 0.001
21 Support Vector Machines (SVM) NaN False
22 Support Vector Machines (SVM) 0.185673 NaN
23 Support Vector Machines (SVM) 0.048427 NaN
24 Support Vector Machines (SVM) 0.957317 NaN
25 Support Vector Machines (SVM) 0.092190 NaN
26 Support Vector Machines (SVM) 0.642406 NaN
27 Support Vector Machines (SVM) 0.263607 NaN
28 Support Vector Machines (SVM) 0.284813 NaN
29 Support Vector Machines (SVM) NaN 1.0
30 Support Vector Machines (SVM) NaN False
31 Support Vector Machines (SVM) NaN 200
32 Support Vector Machines (SVM) NaN None
33 Support Vector Machines (SVM) NaN 0.0
34 Support Vector Machines (SVM) NaN ovr
35 Support Vector Machines (SVM) NaN 3
36 Support Vector Machines (SVM) NaN scale
37 Support Vector Machines (SVM) NaN rbf
38 Support Vector Machines (SVM) NaN -1
39 Support Vector Machines (SVM) NaN True
40 Support Vector Machines (SVM) NaN None
41 Support Vector Machines (SVM) NaN True
42 Support Vector Machines (SVM) NaN 0.001
43 Support Vector Machines (SVM) NaN False
44 Support Vector Machines (SVM) 0.201475 NaN
45 Support Vector Machines (SVM) 0.055330 NaN
46 Support Vector Machines (SVM) 0.946524 NaN
47 Support Vector Machines (SVM) 0.104548 NaN
48 Support Vector Machines (SVM) 0.620065 NaN
49 Support Vector Machines (SVM) 0.257247 NaN
50 Support Vector Machines (SVM) 0.240129 NaN
51 Support Vector Machines (SVM) NaN 1.0
52 Support Vector Machines (SVM) NaN False
53 Support Vector Machines (SVM) NaN 200
54 Support Vector Machines (SVM) NaN None
55 Support Vector Machines (SVM) NaN 0.0
56 Support Vector Machines (SVM) NaN ovr
57 Support Vector Machines (SVM) NaN 3
58 Support Vector Machines (SVM) NaN scale
59 Support Vector Machines (SVM) NaN rbf
60 Support Vector Machines (SVM) NaN -1
61 Support Vector Machines (SVM) NaN True
62 Support Vector Machines (SVM) NaN None
63 Support Vector Machines (SVM) NaN True
64 Support Vector Machines (SVM) NaN 0.001
65 Support Vector Machines (SVM) NaN False
66 Support Vector Machines (SVM) 0.176502 NaN
67 Support Vector Machines (SVM) 0.056866 NaN
68 Support Vector Machines (SVM) 0.959184 NaN
69 Support Vector Machines (SVM) 0.107367 NaN
70 Support Vector Machines (SVM) 0.609985 NaN
71 Support Vector Machines (SVM) 0.192829 NaN
72 Support Vector Machines (SVM) 0.219970 NaN
73 Support Vector Machines (SVM) NaN 1.0
74 Support Vector Machines (SVM) NaN False
75 Support Vector Machines (SVM) NaN 200
76 Support Vector Machines (SVM) NaN None
77 Support Vector Machines (SVM) NaN 0.0
78 Support Vector Machines (SVM) NaN ovr
79 Support Vector Machines (SVM) NaN 3
80 Support Vector Machines (SVM) NaN scale
81 Support Vector Machines (SVM) NaN rbf
82 Support Vector Machines (SVM) NaN -1
83 Support Vector Machines (SVM) NaN True
84 Support Vector Machines (SVM) NaN None
85 Support Vector Machines (SVM) NaN True
86 Support Vector Machines (SVM) NaN 0.001
87 Support Vector Machines (SVM) NaN False
88 Support Vector Machines (SVM) 0.199420 NaN
89 Support Vector Machines (SVM) 0.063679 NaN
90 Support Vector Machines (SVM) 0.953704 NaN
91 Support Vector Machines (SVM) 0.119386 NaN
92 Support Vector Machines (SVM) 0.638917 NaN
93 Support Vector Machines (SVM) 0.249591 NaN
94 Support Vector Machines (SVM) 0.277835 NaN
95 Support Vector Machines (SVM) NaN 1.0
96 Support Vector Machines (SVM) NaN False
97 Support Vector Machines (SVM) NaN 200
98 Support Vector Machines (SVM) NaN None
99 Support Vector Machines (SVM) NaN 0.0
100 Support Vector Machines (SVM) NaN ovr
101 Support Vector Machines (SVM) NaN 3
102 Support Vector Machines (SVM) NaN scale
103 Support Vector Machines (SVM) NaN rbf
104 Support Vector Machines (SVM) NaN -1
105 Support Vector Machines (SVM) NaN True
106 Support Vector Machines (SVM) NaN None
107 Support Vector Machines (SVM) NaN True
108 Support Vector Machines (SVM) NaN 0.001
109 Support Vector Machines (SVM) NaN False
Imputation Technique Imbalance Class Technique \
0 Mean Imputation Hybrid Sampling (SMOTEENN)
1 Mean Imputation Hybrid Sampling (SMOTEENN)
2 Mean Imputation Hybrid Sampling (SMOTEENN)
3 Mean Imputation Hybrid Sampling (SMOTEENN)
4 Mean Imputation Hybrid Sampling (SMOTEENN)
5 Mean Imputation Hybrid Sampling (SMOTEENN)
6 Mean Imputation Hybrid Sampling (SMOTEENN)
7 Mean Imputation Hybrid Sampling (SMOTEENN)
8 Mean Imputation Hybrid Sampling (SMOTEENN)
9 Mean Imputation Hybrid Sampling (SMOTEENN)
10 Mean Imputation Hybrid Sampling (SMOTEENN)
11 Mean Imputation Hybrid Sampling (SMOTEENN)
12 Mean Imputation Hybrid Sampling (SMOTEENN)
13 Mean Imputation Hybrid Sampling (SMOTEENN)
14 Mean Imputation Hybrid Sampling (SMOTEENN)
15 Mean Imputation Hybrid Sampling (SMOTEENN)
16 Mean Imputation Hybrid Sampling (SMOTEENN)
17 Mean Imputation Hybrid Sampling (SMOTEENN)
18 Mean Imputation Hybrid Sampling (SMOTEENN)
19 Mean Imputation Hybrid Sampling (SMOTEENN)
20 Mean Imputation Hybrid Sampling (SMOTEENN)
21 Mean Imputation Hybrid Sampling (SMOTEENN)
22 Mean Imputation Hybrid Sampling (SMOTEENN)
23 Mean Imputation Hybrid Sampling (SMOTEENN)
24 Mean Imputation Hybrid Sampling (SMOTEENN)
25 Mean Imputation Hybrid Sampling (SMOTEENN)
26 Mean Imputation Hybrid Sampling (SMOTEENN)
27 Mean Imputation Hybrid Sampling (SMOTEENN)
28 Mean Imputation Hybrid Sampling (SMOTEENN)
29 Mean Imputation Hybrid Sampling (SMOTEENN)
30 Mean Imputation Hybrid Sampling (SMOTEENN)
31 Mean Imputation Hybrid Sampling (SMOTEENN)
32 Mean Imputation Hybrid Sampling (SMOTEENN)
33 Mean Imputation Hybrid Sampling (SMOTEENN)
34 Mean Imputation Hybrid Sampling (SMOTEENN)
35 Mean Imputation Hybrid Sampling (SMOTEENN)
36 Mean Imputation Hybrid Sampling (SMOTEENN)
37 Mean Imputation Hybrid Sampling (SMOTEENN)
38 Mean Imputation Hybrid Sampling (SMOTEENN)
39 Mean Imputation Hybrid Sampling (SMOTEENN)
40 Mean Imputation Hybrid Sampling (SMOTEENN)
41 Mean Imputation Hybrid Sampling (SMOTEENN)
42 Mean Imputation Hybrid Sampling (SMOTEENN)
43 Mean Imputation Hybrid Sampling (SMOTEENN)
44 Mean Imputation Hybrid Sampling (SMOTEENN)
45 Mean Imputation Hybrid Sampling (SMOTEENN)
46 Mean Imputation Hybrid Sampling (SMOTEENN)
47 Mean Imputation Hybrid Sampling (SMOTEENN)
48 Mean Imputation Hybrid Sampling (SMOTEENN)
49 Mean Imputation Hybrid Sampling (SMOTEENN)
50 Mean Imputation Hybrid Sampling (SMOTEENN)
51 Mean Imputation Hybrid Sampling (SMOTEENN)
52 Mean Imputation Hybrid Sampling (SMOTEENN)
53 Mean Imputation Hybrid Sampling (SMOTEENN)
54 Mean Imputation Hybrid Sampling (SMOTEENN)
55 Mean Imputation Hybrid Sampling (SMOTEENN)
56 Mean Imputation Hybrid Sampling (SMOTEENN)
57 Mean Imputation Hybrid Sampling (SMOTEENN)
58 Mean Imputation Hybrid Sampling (SMOTEENN)
59 Mean Imputation Hybrid Sampling (SMOTEENN)
60 Mean Imputation Hybrid Sampling (SMOTEENN)
61 Mean Imputation Hybrid Sampling (SMOTEENN)
62 Mean Imputation Hybrid Sampling (SMOTEENN)
63 Mean Imputation Hybrid Sampling (SMOTEENN)
64 Mean Imputation Hybrid Sampling (SMOTEENN)
65 Mean Imputation Hybrid Sampling (SMOTEENN)
66 Mean Imputation Hybrid Sampling (SMOTEENN)
67 Mean Imputation Hybrid Sampling (SMOTEENN)
68 Mean Imputation Hybrid Sampling (SMOTEENN)
69 Mean Imputation Hybrid Sampling (SMOTEENN)
70 Mean Imputation Hybrid Sampling (SMOTEENN)
71 Mean Imputation Hybrid Sampling (SMOTEENN)
72 Mean Imputation Hybrid Sampling (SMOTEENN)
73 Mean Imputation Hybrid Sampling (SMOTEENN)
74 Mean Imputation Hybrid Sampling (SMOTEENN)
75 Mean Imputation Hybrid Sampling (SMOTEENN)
76 Mean Imputation Hybrid Sampling (SMOTEENN)
77 Mean Imputation Hybrid Sampling (SMOTEENN)
78 Mean Imputation Hybrid Sampling (SMOTEENN)
79 Mean Imputation Hybrid Sampling (SMOTEENN)
80 Mean Imputation Hybrid Sampling (SMOTEENN)
81 Mean Imputation Hybrid Sampling (SMOTEENN)
82 Mean Imputation Hybrid Sampling (SMOTEENN)
83 Mean Imputation Hybrid Sampling (SMOTEENN)
84 Mean Imputation Hybrid Sampling (SMOTEENN)
85 Mean Imputation Hybrid Sampling (SMOTEENN)
86 Mean Imputation Hybrid Sampling (SMOTEENN)
87 Mean Imputation Hybrid Sampling (SMOTEENN)
88 Mean Imputation Hybrid Sampling (SMOTEENN)
89 Mean Imputation Hybrid Sampling (SMOTEENN)
90 Mean Imputation Hybrid Sampling (SMOTEENN)
91 Mean Imputation Hybrid Sampling (SMOTEENN)
92 Mean Imputation Hybrid Sampling (SMOTEENN)
93 Mean Imputation Hybrid Sampling (SMOTEENN)
94 Mean Imputation Hybrid Sampling (SMOTEENN)
95 Mean Imputation Hybrid Sampling (SMOTEENN)
96 Mean Imputation Hybrid Sampling (SMOTEENN)
97 Mean Imputation Hybrid Sampling (SMOTEENN)
98 Mean Imputation Hybrid Sampling (SMOTEENN)
99 Mean Imputation Hybrid Sampling (SMOTEENN)
100 Mean Imputation Hybrid Sampling (SMOTEENN)
101 Mean Imputation Hybrid Sampling (SMOTEENN)
102 Mean Imputation Hybrid Sampling (SMOTEENN)
103 Mean Imputation Hybrid Sampling (SMOTEENN)
104 Mean Imputation Hybrid Sampling (SMOTEENN)
105 Mean Imputation Hybrid Sampling (SMOTEENN)
106 Mean Imputation Hybrid Sampling (SMOTEENN)
107 Mean Imputation Hybrid Sampling (SMOTEENN)
108 Mean Imputation Hybrid Sampling (SMOTEENN)
109 Mean Imputation Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Support Vector Machines (SVM)_Hybrid Sampling ...
1 Support Vector Machines (SVM)_Hybrid Sampling ...
2 Support Vector Machines (SVM)_Hybrid Sampling ...
3 Support Vector Machines (SVM)_Hybrid Sampling ...
4 Support Vector Machines (SVM)_Hybrid Sampling ...
5 Support Vector Machines (SVM)_Hybrid Sampling ...
6 Support Vector Machines (SVM)_Hybrid Sampling ...
7 Support Vector Machines (SVM)_Hybrid Sampling ...
8 Support Vector Machines (SVM)_Hybrid Sampling ...
9 Support Vector Machines (SVM)_Hybrid Sampling ...
10 Support Vector Machines (SVM)_Hybrid Sampling ...
11 Support Vector Machines (SVM)_Hybrid Sampling ...
12 Support Vector Machines (SVM)_Hybrid Sampling ...
13 Support Vector Machines (SVM)_Hybrid Sampling ...
14 Support Vector Machines (SVM)_Hybrid Sampling ...
15 Support Vector Machines (SVM)_Hybrid Sampling ...
16 Support Vector Machines (SVM)_Hybrid Sampling ...
17 Support Vector Machines (SVM)_Hybrid Sampling ...
18 Support Vector Machines (SVM)_Hybrid Sampling ...
19 Support Vector Machines (SVM)_Hybrid Sampling ...
20 Support Vector Machines (SVM)_Hybrid Sampling ...
21 Support Vector Machines (SVM)_Hybrid Sampling ...
22 Support Vector Machines (SVM)_Hybrid Sampling ...
23 Support Vector Machines (SVM)_Hybrid Sampling ...
24 Support Vector Machines (SVM)_Hybrid Sampling ...
25 Support Vector Machines (SVM)_Hybrid Sampling ...
26 Support Vector Machines (SVM)_Hybrid Sampling ...
27 Support Vector Machines (SVM)_Hybrid Sampling ...
28 Support Vector Machines (SVM)_Hybrid Sampling ...
29 Support Vector Machines (SVM)_Hybrid Sampling ...
30 Support Vector Machines (SVM)_Hybrid Sampling ...
31 Support Vector Machines (SVM)_Hybrid Sampling ...
32 Support Vector Machines (SVM)_Hybrid Sampling ...
33 Support Vector Machines (SVM)_Hybrid Sampling ...
34 Support Vector Machines (SVM)_Hybrid Sampling ...
35 Support Vector Machines (SVM)_Hybrid Sampling ...
36 Support Vector Machines (SVM)_Hybrid Sampling ...
37 Support Vector Machines (SVM)_Hybrid Sampling ...
38 Support Vector Machines (SVM)_Hybrid Sampling ...
39 Support Vector Machines (SVM)_Hybrid Sampling ...
40 Support Vector Machines (SVM)_Hybrid Sampling ...
41 Support Vector Machines (SVM)_Hybrid Sampling ...
42 Support Vector Machines (SVM)_Hybrid Sampling ...
43 Support Vector Machines (SVM)_Hybrid Sampling ...
44 Support Vector Machines (SVM)_Hybrid Sampling ...
45 Support Vector Machines (SVM)_Hybrid Sampling ...
46 Support Vector Machines (SVM)_Hybrid Sampling ...
47 Support Vector Machines (SVM)_Hybrid Sampling ...
48 Support Vector Machines (SVM)_Hybrid Sampling ...
49 Support Vector Machines (SVM)_Hybrid Sampling ...
50 Support Vector Machines (SVM)_Hybrid Sampling ...
51 Support Vector Machines (SVM)_Hybrid Sampling ...
52 Support Vector Machines (SVM)_Hybrid Sampling ...
53 Support Vector Machines (SVM)_Hybrid Sampling ...
54 Support Vector Machines (SVM)_Hybrid Sampling ...
55 Support Vector Machines (SVM)_Hybrid Sampling ...
56 Support Vector Machines (SVM)_Hybrid Sampling ...
57 Support Vector Machines (SVM)_Hybrid Sampling ...
58 Support Vector Machines (SVM)_Hybrid Sampling ...
59 Support Vector Machines (SVM)_Hybrid Sampling ...
60 Support Vector Machines (SVM)_Hybrid Sampling ...
61 Support Vector Machines (SVM)_Hybrid Sampling ...
62 Support Vector Machines (SVM)_Hybrid Sampling ...
63 Support Vector Machines (SVM)_Hybrid Sampling ...
64 Support Vector Machines (SVM)_Hybrid Sampling ...
65 Support Vector Machines (SVM)_Hybrid Sampling ...
66 Support Vector Machines (SVM)_Hybrid Sampling ...
67 Support Vector Machines (SVM)_Hybrid Sampling ...
68 Support Vector Machines (SVM)_Hybrid Sampling ...
69 Support Vector Machines (SVM)_Hybrid Sampling ...
70 Support Vector Machines (SVM)_Hybrid Sampling ...
71 Support Vector Machines (SVM)_Hybrid Sampling ...
72 Support Vector Machines (SVM)_Hybrid Sampling ...
73 Support Vector Machines (SVM)_Hybrid Sampling ...
74 Support Vector Machines (SVM)_Hybrid Sampling ...
75 Support Vector Machines (SVM)_Hybrid Sampling ...
76 Support Vector Machines (SVM)_Hybrid Sampling ...
77 Support Vector Machines (SVM)_Hybrid Sampling ...
78 Support Vector Machines (SVM)_Hybrid Sampling ...
79 Support Vector Machines (SVM)_Hybrid Sampling ...
80 Support Vector Machines (SVM)_Hybrid Sampling ...
81 Support Vector Machines (SVM)_Hybrid Sampling ...
82 Support Vector Machines (SVM)_Hybrid Sampling ...
83 Support Vector Machines (SVM)_Hybrid Sampling ...
84 Support Vector Machines (SVM)_Hybrid Sampling ...
85 Support Vector Machines (SVM)_Hybrid Sampling ...
86 Support Vector Machines (SVM)_Hybrid Sampling ...
87 Support Vector Machines (SVM)_Hybrid Sampling ...
88 Support Vector Machines (SVM)_Hybrid Sampling ...
89 Support Vector Machines (SVM)_Hybrid Sampling ...
90 Support Vector Machines (SVM)_Hybrid Sampling ...
91 Support Vector Machines (SVM)_Hybrid Sampling ...
92 Support Vector Machines (SVM)_Hybrid Sampling ...
93 Support Vector Machines (SVM)_Hybrid Sampling ...
94 Support Vector Machines (SVM)_Hybrid Sampling ...
95 Support Vector Machines (SVM)_Hybrid Sampling ...
96 Support Vector Machines (SVM)_Hybrid Sampling ...
97 Support Vector Machines (SVM)_Hybrid Sampling ...
98 Support Vector Machines (SVM)_Hybrid Sampling ...
99 Support Vector Machines (SVM)_Hybrid Sampling ...
100 Support Vector Machines (SVM)_Hybrid Sampling ...
101 Support Vector Machines (SVM)_Hybrid Sampling ...
102 Support Vector Machines (SVM)_Hybrid Sampling ...
103 Support Vector Machines (SVM)_Hybrid Sampling ...
104 Support Vector Machines (SVM)_Hybrid Sampling ...
105 Support Vector Machines (SVM)_Hybrid Sampling ...
106 Support Vector Machines (SVM)_Hybrid Sampling ...
107 Support Vector Machines (SVM)_Hybrid Sampling ...
108 Support Vector Machines (SVM)_Hybrid Sampling ...
109 Support Vector Machines (SVM)_Hybrid Sampling ... ,
Algorithm Metrics Hyperparameters \
0 Support Vector Machines (SVM) 0.309718 NaN
1 Support Vector Machines (SVM) 0.074590 NaN
2 Support Vector Machines (SVM) 0.881857 NaN
3 Support Vector Machines (SVM) 0.137545 NaN
4 Support Vector Machines (SVM) 0.637241 NaN
5 Support Vector Machines (SVM) 0.222532 NaN
6 Support Vector Machines (SVM) 0.274482 NaN
7 Support Vector Machines (SVM) NaN 1.0
8 Support Vector Machines (SVM) NaN False
9 Support Vector Machines (SVM) NaN 200
10 Support Vector Machines (SVM) NaN None
11 Support Vector Machines (SVM) NaN 0.0
12 Support Vector Machines (SVM) NaN ovr
13 Support Vector Machines (SVM) NaN 3
14 Support Vector Machines (SVM) NaN scale
15 Support Vector Machines (SVM) NaN rbf
16 Support Vector Machines (SVM) NaN -1
17 Support Vector Machines (SVM) NaN True
18 Support Vector Machines (SVM) NaN None
19 Support Vector Machines (SVM) NaN True
20 Support Vector Machines (SVM) NaN 0.001
21 Support Vector Machines (SVM) NaN False
22 Support Vector Machines (SVM) 0.290756 NaN
23 Support Vector Machines (SVM) 0.052389 NaN
24 Support Vector Machines (SVM) 0.902439 NaN
25 Support Vector Machines (SVM) 0.099030 NaN
26 Support Vector Machines (SVM) 0.652181 NaN
27 Support Vector Machines (SVM) 0.268132 NaN
28 Support Vector Machines (SVM) 0.304363 NaN
29 Support Vector Machines (SVM) NaN 1.0
30 Support Vector Machines (SVM) NaN False
31 Support Vector Machines (SVM) NaN 200
32 Support Vector Machines (SVM) NaN None
33 Support Vector Machines (SVM) NaN 0.0
34 Support Vector Machines (SVM) NaN ovr
35 Support Vector Machines (SVM) NaN 3
36 Support Vector Machines (SVM) NaN scale
37 Support Vector Machines (SVM) NaN rbf
38 Support Vector Machines (SVM) NaN -1
39 Support Vector Machines (SVM) NaN True
40 Support Vector Machines (SVM) NaN None
41 Support Vector Machines (SVM) NaN True
42 Support Vector Machines (SVM) NaN 0.001
43 Support Vector Machines (SVM) NaN False
44 Support Vector Machines (SVM) 0.285489 NaN
45 Support Vector Machines (SVM) 0.057773 NaN
46 Support Vector Machines (SVM) 0.882353 NaN
47 Support Vector Machines (SVM) 0.108446 NaN
48 Support Vector Machines (SVM) 0.640422 NaN
49 Support Vector Machines (SVM) 0.239938 NaN
50 Support Vector Machines (SVM) 0.280845 NaN
51 Support Vector Machines (SVM) NaN 1.0
52 Support Vector Machines (SVM) NaN False
53 Support Vector Machines (SVM) NaN 200
54 Support Vector Machines (SVM) NaN None
55 Support Vector Machines (SVM) NaN 0.0
56 Support Vector Machines (SVM) NaN ovr
57 Support Vector Machines (SVM) NaN 3
58 Support Vector Machines (SVM) NaN scale
59 Support Vector Machines (SVM) NaN rbf
60 Support Vector Machines (SVM) NaN -1
61 Support Vector Machines (SVM) NaN True
62 Support Vector Machines (SVM) NaN None
63 Support Vector Machines (SVM) NaN True
64 Support Vector Machines (SVM) NaN 0.001
65 Support Vector Machines (SVM) NaN False
66 Support Vector Machines (SVM) 0.276870 NaN
67 Support Vector Machines (SVM) 0.060669 NaN
68 Support Vector Machines (SVM) 0.897959 NaN
69 Support Vector Machines (SVM) 0.113658 NaN
70 Support Vector Machines (SVM) 0.627589 NaN
71 Support Vector Machines (SVM) 0.231043 NaN
72 Support Vector Machines (SVM) 0.255177 NaN
73 Support Vector Machines (SVM) NaN 1.0
74 Support Vector Machines (SVM) NaN False
75 Support Vector Machines (SVM) NaN 200
76 Support Vector Machines (SVM) NaN None
77 Support Vector Machines (SVM) NaN 0.0
78 Support Vector Machines (SVM) NaN ovr
79 Support Vector Machines (SVM) NaN 3
80 Support Vector Machines (SVM) NaN scale
81 Support Vector Machines (SVM) NaN rbf
82 Support Vector Machines (SVM) NaN -1
83 Support Vector Machines (SVM) NaN True
84 Support Vector Machines (SVM) NaN None
85 Support Vector Machines (SVM) NaN True
86 Support Vector Machines (SVM) NaN 0.001
87 Support Vector Machines (SVM) NaN False
88 Support Vector Machines (SVM) 0.295838 NaN
89 Support Vector Machines (SVM) 0.067277 NaN
90 Support Vector Machines (SVM) 0.884259 NaN
91 Support Vector Machines (SVM) 0.125041 NaN
92 Support Vector Machines (SVM) 0.666149 NaN
93 Support Vector Machines (SVM) 0.283090 NaN
94 Support Vector Machines (SVM) 0.332297 NaN
95 Support Vector Machines (SVM) NaN 1.0
96 Support Vector Machines (SVM) NaN False
97 Support Vector Machines (SVM) NaN 200
98 Support Vector Machines (SVM) NaN None
99 Support Vector Machines (SVM) NaN 0.0
100 Support Vector Machines (SVM) NaN ovr
101 Support Vector Machines (SVM) NaN 3
102 Support Vector Machines (SVM) NaN scale
103 Support Vector Machines (SVM) NaN rbf
104 Support Vector Machines (SVM) NaN -1
105 Support Vector Machines (SVM) NaN True
106 Support Vector Machines (SVM) NaN None
107 Support Vector Machines (SVM) NaN True
108 Support Vector Machines (SVM) NaN 0.001
109 Support Vector Machines (SVM) NaN False
Imputation Technique Imbalance Class Technique \
0 Median Imputation Hybrid Sampling (SMOTEENN)
1 Median Imputation Hybrid Sampling (SMOTEENN)
2 Median Imputation Hybrid Sampling (SMOTEENN)
3 Median Imputation Hybrid Sampling (SMOTEENN)
4 Median Imputation Hybrid Sampling (SMOTEENN)
5 Median Imputation Hybrid Sampling (SMOTEENN)
6 Median Imputation Hybrid Sampling (SMOTEENN)
7 Median Imputation Hybrid Sampling (SMOTEENN)
8 Median Imputation Hybrid Sampling (SMOTEENN)
9 Median Imputation Hybrid Sampling (SMOTEENN)
10 Median Imputation Hybrid Sampling (SMOTEENN)
11 Median Imputation Hybrid Sampling (SMOTEENN)
12 Median Imputation Hybrid Sampling (SMOTEENN)
13 Median Imputation Hybrid Sampling (SMOTEENN)
14 Median Imputation Hybrid Sampling (SMOTEENN)
15 Median Imputation Hybrid Sampling (SMOTEENN)
16 Median Imputation Hybrid Sampling (SMOTEENN)
17 Median Imputation Hybrid Sampling (SMOTEENN)
18 Median Imputation Hybrid Sampling (SMOTEENN)
19 Median Imputation Hybrid Sampling (SMOTEENN)
20 Median Imputation Hybrid Sampling (SMOTEENN)
21 Median Imputation Hybrid Sampling (SMOTEENN)
22 Median Imputation Hybrid Sampling (SMOTEENN)
23 Median Imputation Hybrid Sampling (SMOTEENN)
24 Median Imputation Hybrid Sampling (SMOTEENN)
25 Median Imputation Hybrid Sampling (SMOTEENN)
26 Median Imputation Hybrid Sampling (SMOTEENN)
27 Median Imputation Hybrid Sampling (SMOTEENN)
28 Median Imputation Hybrid Sampling (SMOTEENN)
29 Median Imputation Hybrid Sampling (SMOTEENN)
30 Median Imputation Hybrid Sampling (SMOTEENN)
31 Median Imputation Hybrid Sampling (SMOTEENN)
32 Median Imputation Hybrid Sampling (SMOTEENN)
33 Median Imputation Hybrid Sampling (SMOTEENN)
34 Median Imputation Hybrid Sampling (SMOTEENN)
35 Median Imputation Hybrid Sampling (SMOTEENN)
36 Median Imputation Hybrid Sampling (SMOTEENN)
37 Median Imputation Hybrid Sampling (SMOTEENN)
38 Median Imputation Hybrid Sampling (SMOTEENN)
39 Median Imputation Hybrid Sampling (SMOTEENN)
40 Median Imputation Hybrid Sampling (SMOTEENN)
41 Median Imputation Hybrid Sampling (SMOTEENN)
42 Median Imputation Hybrid Sampling (SMOTEENN)
43 Median Imputation Hybrid Sampling (SMOTEENN)
44 Median Imputation Hybrid Sampling (SMOTEENN)
45 Median Imputation Hybrid Sampling (SMOTEENN)
46 Median Imputation Hybrid Sampling (SMOTEENN)
47 Median Imputation Hybrid Sampling (SMOTEENN)
48 Median Imputation Hybrid Sampling (SMOTEENN)
49 Median Imputation Hybrid Sampling (SMOTEENN)
50 Median Imputation Hybrid Sampling (SMOTEENN)
51 Median Imputation Hybrid Sampling (SMOTEENN)
52 Median Imputation Hybrid Sampling (SMOTEENN)
53 Median Imputation Hybrid Sampling (SMOTEENN)
54 Median Imputation Hybrid Sampling (SMOTEENN)
55 Median Imputation Hybrid Sampling (SMOTEENN)
56 Median Imputation Hybrid Sampling (SMOTEENN)
57 Median Imputation Hybrid Sampling (SMOTEENN)
58 Median Imputation Hybrid Sampling (SMOTEENN)
59 Median Imputation Hybrid Sampling (SMOTEENN)
60 Median Imputation Hybrid Sampling (SMOTEENN)
61 Median Imputation Hybrid Sampling (SMOTEENN)
62 Median Imputation Hybrid Sampling (SMOTEENN)
63 Median Imputation Hybrid Sampling (SMOTEENN)
64 Median Imputation Hybrid Sampling (SMOTEENN)
65 Median Imputation Hybrid Sampling (SMOTEENN)
66 Median Imputation Hybrid Sampling (SMOTEENN)
67 Median Imputation Hybrid Sampling (SMOTEENN)
68 Median Imputation Hybrid Sampling (SMOTEENN)
69 Median Imputation Hybrid Sampling (SMOTEENN)
70 Median Imputation Hybrid Sampling (SMOTEENN)
71 Median Imputation Hybrid Sampling (SMOTEENN)
72 Median Imputation Hybrid Sampling (SMOTEENN)
73 Median Imputation Hybrid Sampling (SMOTEENN)
74 Median Imputation Hybrid Sampling (SMOTEENN)
75 Median Imputation Hybrid Sampling (SMOTEENN)
76 Median Imputation Hybrid Sampling (SMOTEENN)
77 Median Imputation Hybrid Sampling (SMOTEENN)
78 Median Imputation Hybrid Sampling (SMOTEENN)
79 Median Imputation Hybrid Sampling (SMOTEENN)
80 Median Imputation Hybrid Sampling (SMOTEENN)
81 Median Imputation Hybrid Sampling (SMOTEENN)
82 Median Imputation Hybrid Sampling (SMOTEENN)
83 Median Imputation Hybrid Sampling (SMOTEENN)
84 Median Imputation Hybrid Sampling (SMOTEENN)
85 Median Imputation Hybrid Sampling (SMOTEENN)
86 Median Imputation Hybrid Sampling (SMOTEENN)
87 Median Imputation Hybrid Sampling (SMOTEENN)
88 Median Imputation Hybrid Sampling (SMOTEENN)
89 Median Imputation Hybrid Sampling (SMOTEENN)
90 Median Imputation Hybrid Sampling (SMOTEENN)
91 Median Imputation Hybrid Sampling (SMOTEENN)
92 Median Imputation Hybrid Sampling (SMOTEENN)
93 Median Imputation Hybrid Sampling (SMOTEENN)
94 Median Imputation Hybrid Sampling (SMOTEENN)
95 Median Imputation Hybrid Sampling (SMOTEENN)
96 Median Imputation Hybrid Sampling (SMOTEENN)
97 Median Imputation Hybrid Sampling (SMOTEENN)
98 Median Imputation Hybrid Sampling (SMOTEENN)
99 Median Imputation Hybrid Sampling (SMOTEENN)
100 Median Imputation Hybrid Sampling (SMOTEENN)
101 Median Imputation Hybrid Sampling (SMOTEENN)
102 Median Imputation Hybrid Sampling (SMOTEENN)
103 Median Imputation Hybrid Sampling (SMOTEENN)
104 Median Imputation Hybrid Sampling (SMOTEENN)
105 Median Imputation Hybrid Sampling (SMOTEENN)
106 Median Imputation Hybrid Sampling (SMOTEENN)
107 Median Imputation Hybrid Sampling (SMOTEENN)
108 Median Imputation Hybrid Sampling (SMOTEENN)
109 Median Imputation Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Support Vector Machines (SVM)_Hybrid Sampling ...
1 Support Vector Machines (SVM)_Hybrid Sampling ...
2 Support Vector Machines (SVM)_Hybrid Sampling ...
3 Support Vector Machines (SVM)_Hybrid Sampling ...
4 Support Vector Machines (SVM)_Hybrid Sampling ...
5 Support Vector Machines (SVM)_Hybrid Sampling ...
6 Support Vector Machines (SVM)_Hybrid Sampling ...
7 Support Vector Machines (SVM)_Hybrid Sampling ...
8 Support Vector Machines (SVM)_Hybrid Sampling ...
9 Support Vector Machines (SVM)_Hybrid Sampling ...
10 Support Vector Machines (SVM)_Hybrid Sampling ...
11 Support Vector Machines (SVM)_Hybrid Sampling ...
12 Support Vector Machines (SVM)_Hybrid Sampling ...
13 Support Vector Machines (SVM)_Hybrid Sampling ...
14 Support Vector Machines (SVM)_Hybrid Sampling ...
15 Support Vector Machines (SVM)_Hybrid Sampling ...
16 Support Vector Machines (SVM)_Hybrid Sampling ...
17 Support Vector Machines (SVM)_Hybrid Sampling ...
18 Support Vector Machines (SVM)_Hybrid Sampling ...
19 Support Vector Machines (SVM)_Hybrid Sampling ...
20 Support Vector Machines (SVM)_Hybrid Sampling ...
21 Support Vector Machines (SVM)_Hybrid Sampling ...
22 Support Vector Machines (SVM)_Hybrid Sampling ...
23 Support Vector Machines (SVM)_Hybrid Sampling ...
24 Support Vector Machines (SVM)_Hybrid Sampling ...
25 Support Vector Machines (SVM)_Hybrid Sampling ...
26 Support Vector Machines (SVM)_Hybrid Sampling ...
27 Support Vector Machines (SVM)_Hybrid Sampling ...
28 Support Vector Machines (SVM)_Hybrid Sampling ...
29 Support Vector Machines (SVM)_Hybrid Sampling ...
30 Support Vector Machines (SVM)_Hybrid Sampling ...
31 Support Vector Machines (SVM)_Hybrid Sampling ...
32 Support Vector Machines (SVM)_Hybrid Sampling ...
33 Support Vector Machines (SVM)_Hybrid Sampling ...
34 Support Vector Machines (SVM)_Hybrid Sampling ...
35 Support Vector Machines (SVM)_Hybrid Sampling ...
36 Support Vector Machines (SVM)_Hybrid Sampling ...
37 Support Vector Machines (SVM)_Hybrid Sampling ...
38 Support Vector Machines (SVM)_Hybrid Sampling ...
39 Support Vector Machines (SVM)_Hybrid Sampling ...
40 Support Vector Machines (SVM)_Hybrid Sampling ...
41 Support Vector Machines (SVM)_Hybrid Sampling ...
42 Support Vector Machines (SVM)_Hybrid Sampling ...
43 Support Vector Machines (SVM)_Hybrid Sampling ...
44 Support Vector Machines (SVM)_Hybrid Sampling ...
45 Support Vector Machines (SVM)_Hybrid Sampling ...
46 Support Vector Machines (SVM)_Hybrid Sampling ...
47 Support Vector Machines (SVM)_Hybrid Sampling ...
48 Support Vector Machines (SVM)_Hybrid Sampling ...
49 Support Vector Machines (SVM)_Hybrid Sampling ...
50 Support Vector Machines (SVM)_Hybrid Sampling ...
51 Support Vector Machines (SVM)_Hybrid Sampling ...
52 Support Vector Machines (SVM)_Hybrid Sampling ...
53 Support Vector Machines (SVM)_Hybrid Sampling ...
54 Support Vector Machines (SVM)_Hybrid Sampling ...
55 Support Vector Machines (SVM)_Hybrid Sampling ...
56 Support Vector Machines (SVM)_Hybrid Sampling ...
57 Support Vector Machines (SVM)_Hybrid Sampling ...
58 Support Vector Machines (SVM)_Hybrid Sampling ...
59 Support Vector Machines (SVM)_Hybrid Sampling ...
60 Support Vector Machines (SVM)_Hybrid Sampling ...
61 Support Vector Machines (SVM)_Hybrid Sampling ...
62 Support Vector Machines (SVM)_Hybrid Sampling ...
63 Support Vector Machines (SVM)_Hybrid Sampling ...
64 Support Vector Machines (SVM)_Hybrid Sampling ...
65 Support Vector Machines (SVM)_Hybrid Sampling ...
66 Support Vector Machines (SVM)_Hybrid Sampling ...
67 Support Vector Machines (SVM)_Hybrid Sampling ...
68 Support Vector Machines (SVM)_Hybrid Sampling ...
69 Support Vector Machines (SVM)_Hybrid Sampling ...
70 Support Vector Machines (SVM)_Hybrid Sampling ...
71 Support Vector Machines (SVM)_Hybrid Sampling ...
72 Support Vector Machines (SVM)_Hybrid Sampling ...
73 Support Vector Machines (SVM)_Hybrid Sampling ...
74 Support Vector Machines (SVM)_Hybrid Sampling ...
75 Support Vector Machines (SVM)_Hybrid Sampling ...
76 Support Vector Machines (SVM)_Hybrid Sampling ...
77 Support Vector Machines (SVM)_Hybrid Sampling ...
78 Support Vector Machines (SVM)_Hybrid Sampling ...
79 Support Vector Machines (SVM)_Hybrid Sampling ...
80 Support Vector Machines (SVM)_Hybrid Sampling ...
81 Support Vector Machines (SVM)_Hybrid Sampling ...
82 Support Vector Machines (SVM)_Hybrid Sampling ...
83 Support Vector Machines (SVM)_Hybrid Sampling ...
84 Support Vector Machines (SVM)_Hybrid Sampling ...
85 Support Vector Machines (SVM)_Hybrid Sampling ...
86 Support Vector Machines (SVM)_Hybrid Sampling ...
87 Support Vector Machines (SVM)_Hybrid Sampling ...
88 Support Vector Machines (SVM)_Hybrid Sampling ...
89 Support Vector Machines (SVM)_Hybrid Sampling ...
90 Support Vector Machines (SVM)_Hybrid Sampling ...
91 Support Vector Machines (SVM)_Hybrid Sampling ...
92 Support Vector Machines (SVM)_Hybrid Sampling ...
93 Support Vector Machines (SVM)_Hybrid Sampling ...
94 Support Vector Machines (SVM)_Hybrid Sampling ...
95 Support Vector Machines (SVM)_Hybrid Sampling ...
96 Support Vector Machines (SVM)_Hybrid Sampling ...
97 Support Vector Machines (SVM)_Hybrid Sampling ...
98 Support Vector Machines (SVM)_Hybrid Sampling ...
99 Support Vector Machines (SVM)_Hybrid Sampling ...
100 Support Vector Machines (SVM)_Hybrid Sampling ...
101 Support Vector Machines (SVM)_Hybrid Sampling ...
102 Support Vector Machines (SVM)_Hybrid Sampling ...
103 Support Vector Machines (SVM)_Hybrid Sampling ...
104 Support Vector Machines (SVM)_Hybrid Sampling ...
105 Support Vector Machines (SVM)_Hybrid Sampling ...
106 Support Vector Machines (SVM)_Hybrid Sampling ...
107 Support Vector Machines (SVM)_Hybrid Sampling ...
108 Support Vector Machines (SVM)_Hybrid Sampling ...
109 Support Vector Machines (SVM)_Hybrid Sampling ... ,
Algorithm Metrics Hyperparameters \
0 Support Vector Machines (SVM) 0.501185 NaN
1 Support Vector Machines (SVM) 0.085957 NaN
2 Support Vector Machines (SVM) 0.725738 NaN
3 Support Vector Machines (SVM) 0.153709 NaN
4 Support Vector Machines (SVM) 0.640596 NaN
5 Support Vector Machines (SVM) 0.214162 NaN
6 Support Vector Machines (SVM) 0.281192 NaN
7 Support Vector Machines (SVM) NaN 1.0
8 Support Vector Machines (SVM) NaN False
9 Support Vector Machines (SVM) NaN 200
10 Support Vector Machines (SVM) NaN None
11 Support Vector Machines (SVM) NaN 0.0
12 Support Vector Machines (SVM) NaN ovr
13 Support Vector Machines (SVM) NaN 3
14 Support Vector Machines (SVM) NaN scale
15 Support Vector Machines (SVM) NaN rbf
16 Support Vector Machines (SVM) NaN -1
17 Support Vector Machines (SVM) NaN True
18 Support Vector Machines (SVM) NaN None
19 Support Vector Machines (SVM) NaN True
20 Support Vector Machines (SVM) NaN 0.001
21 Support Vector Machines (SVM) NaN False
22 Support Vector Machines (SVM) 0.488544 NaN
23 Support Vector Machines (SVM) 0.062500 NaN
24 Support Vector Machines (SVM) 0.774390 NaN
25 Support Vector Machines (SVM) 0.115665 NaN
26 Support Vector Machines (SVM) 0.680118 NaN
27 Support Vector Machines (SVM) 0.273133 NaN
28 Support Vector Machines (SVM) 0.360236 NaN
29 Support Vector Machines (SVM) NaN 1.0
30 Support Vector Machines (SVM) NaN False
31 Support Vector Machines (SVM) NaN 200
32 Support Vector Machines (SVM) NaN None
33 Support Vector Machines (SVM) NaN 0.0
34 Support Vector Machines (SVM) NaN ovr
35 Support Vector Machines (SVM) NaN 3
36 Support Vector Machines (SVM) NaN scale
37 Support Vector Machines (SVM) NaN rbf
38 Support Vector Machines (SVM) NaN -1
39 Support Vector Machines (SVM) NaN True
40 Support Vector Machines (SVM) NaN None
41 Support Vector Machines (SVM) NaN True
42 Support Vector Machines (SVM) NaN 0.001
43 Support Vector Machines (SVM) NaN False
44 Support Vector Machines (SVM) 0.476165 NaN
45 Support Vector Machines (SVM) 0.067658 NaN
46 Support Vector Machines (SVM) 0.754011 NaN
47 Support Vector Machines (SVM) 0.124174 NaN
48 Support Vector Machines (SVM) 0.651016 NaN
49 Support Vector Machines (SVM) 0.226668 NaN
50 Support Vector Machines (SVM) 0.302032 NaN
51 Support Vector Machines (SVM) NaN 1.0
52 Support Vector Machines (SVM) NaN False
53 Support Vector Machines (SVM) NaN 200
54 Support Vector Machines (SVM) NaN None
55 Support Vector Machines (SVM) NaN 0.0
56 Support Vector Machines (SVM) NaN ovr
57 Support Vector Machines (SVM) NaN 3
58 Support Vector Machines (SVM) NaN scale
59 Support Vector Machines (SVM) NaN rbf
60 Support Vector Machines (SVM) NaN -1
61 Support Vector Machines (SVM) NaN True
62 Support Vector Machines (SVM) NaN None
63 Support Vector Machines (SVM) NaN True
64 Support Vector Machines (SVM) NaN 0.001
65 Support Vector Machines (SVM) NaN False
66 Support Vector Machines (SVM) 0.474447 NaN
67 Support Vector Machines (SVM) 0.068171 NaN
68 Support Vector Machines (SVM) 0.724490 NaN
69 Support Vector Machines (SVM) 0.124616 NaN
70 Support Vector Machines (SVM) 0.646269 NaN
71 Support Vector Machines (SVM) 0.215901 NaN
72 Support Vector Machines (SVM) 0.292538 NaN
73 Support Vector Machines (SVM) NaN 1.0
74 Support Vector Machines (SVM) NaN False
75 Support Vector Machines (SVM) NaN 200
76 Support Vector Machines (SVM) NaN None
77 Support Vector Machines (SVM) NaN 0.0
78 Support Vector Machines (SVM) NaN ovr
79 Support Vector Machines (SVM) NaN 3
80 Support Vector Machines (SVM) NaN scale
81 Support Vector Machines (SVM) NaN rbf
82 Support Vector Machines (SVM) NaN -1
83 Support Vector Machines (SVM) NaN True
84 Support Vector Machines (SVM) NaN None
85 Support Vector Machines (SVM) NaN True
86 Support Vector Machines (SVM) NaN 0.001
87 Support Vector Machines (SVM) NaN False
88 Support Vector Machines (SVM) 0.500263 NaN
89 Support Vector Machines (SVM) 0.079119 NaN
90 Support Vector Machines (SVM) 0.731481 NaN
91 Support Vector Machines (SVM) 0.142793 NaN
92 Support Vector Machines (SVM) 0.666259 NaN
93 Support Vector Machines (SVM) 0.270945 NaN
94 Support Vector Machines (SVM) 0.332519 NaN
95 Support Vector Machines (SVM) NaN 1.0
96 Support Vector Machines (SVM) NaN False
97 Support Vector Machines (SVM) NaN 200
98 Support Vector Machines (SVM) NaN None
99 Support Vector Machines (SVM) NaN 0.0
100 Support Vector Machines (SVM) NaN ovr
101 Support Vector Machines (SVM) NaN 3
102 Support Vector Machines (SVM) NaN scale
103 Support Vector Machines (SVM) NaN rbf
104 Support Vector Machines (SVM) NaN -1
105 Support Vector Machines (SVM) NaN True
106 Support Vector Machines (SVM) NaN None
107 Support Vector Machines (SVM) NaN True
108 Support Vector Machines (SVM) NaN 0.001
109 Support Vector Machines (SVM) NaN False
Imputation Technique Imbalance Class Technique \
0 Zero Imputation Hybrid Sampling (SMOTETomek)
1 Zero Imputation Hybrid Sampling (SMOTETomek)
2 Zero Imputation Hybrid Sampling (SMOTETomek)
3 Zero Imputation Hybrid Sampling (SMOTETomek)
4 Zero Imputation Hybrid Sampling (SMOTETomek)
5 Zero Imputation Hybrid Sampling (SMOTETomek)
6 Zero Imputation Hybrid Sampling (SMOTETomek)
7 Zero Imputation Hybrid Sampling (SMOTETomek)
8 Zero Imputation Hybrid Sampling (SMOTETomek)
9 Zero Imputation Hybrid Sampling (SMOTETomek)
10 Zero Imputation Hybrid Sampling (SMOTETomek)
11 Zero Imputation Hybrid Sampling (SMOTETomek)
12 Zero Imputation Hybrid Sampling (SMOTETomek)
13 Zero Imputation Hybrid Sampling (SMOTETomek)
14 Zero Imputation Hybrid Sampling (SMOTETomek)
15 Zero Imputation Hybrid Sampling (SMOTETomek)
16 Zero Imputation Hybrid Sampling (SMOTETomek)
17 Zero Imputation Hybrid Sampling (SMOTETomek)
18 Zero Imputation Hybrid Sampling (SMOTETomek)
19 Zero Imputation Hybrid Sampling (SMOTETomek)
20 Zero Imputation Hybrid Sampling (SMOTETomek)
21 Zero Imputation Hybrid Sampling (SMOTETomek)
22 Zero Imputation Hybrid Sampling (SMOTETomek)
23 Zero Imputation Hybrid Sampling (SMOTETomek)
24 Zero Imputation Hybrid Sampling (SMOTETomek)
25 Zero Imputation Hybrid Sampling (SMOTETomek)
26 Zero Imputation Hybrid Sampling (SMOTETomek)
27 Zero Imputation Hybrid Sampling (SMOTETomek)
28 Zero Imputation Hybrid Sampling (SMOTETomek)
29 Zero Imputation Hybrid Sampling (SMOTETomek)
30 Zero Imputation Hybrid Sampling (SMOTETomek)
31 Zero Imputation Hybrid Sampling (SMOTETomek)
32 Zero Imputation Hybrid Sampling (SMOTETomek)
33 Zero Imputation Hybrid Sampling (SMOTETomek)
34 Zero Imputation Hybrid Sampling (SMOTETomek)
35 Zero Imputation Hybrid Sampling (SMOTETomek)
36 Zero Imputation Hybrid Sampling (SMOTETomek)
37 Zero Imputation Hybrid Sampling (SMOTETomek)
38 Zero Imputation Hybrid Sampling (SMOTETomek)
39 Zero Imputation Hybrid Sampling (SMOTETomek)
40 Zero Imputation Hybrid Sampling (SMOTETomek)
41 Zero Imputation Hybrid Sampling (SMOTETomek)
42 Zero Imputation Hybrid Sampling (SMOTETomek)
43 Zero Imputation Hybrid Sampling (SMOTETomek)
44 Zero Imputation Hybrid Sampling (SMOTETomek)
45 Zero Imputation Hybrid Sampling (SMOTETomek)
46 Zero Imputation Hybrid Sampling (SMOTETomek)
47 Zero Imputation Hybrid Sampling (SMOTETomek)
48 Zero Imputation Hybrid Sampling (SMOTETomek)
49 Zero Imputation Hybrid Sampling (SMOTETomek)
50 Zero Imputation Hybrid Sampling (SMOTETomek)
51 Zero Imputation Hybrid Sampling (SMOTETomek)
52 Zero Imputation Hybrid Sampling (SMOTETomek)
53 Zero Imputation Hybrid Sampling (SMOTETomek)
54 Zero Imputation Hybrid Sampling (SMOTETomek)
55 Zero Imputation Hybrid Sampling (SMOTETomek)
56 Zero Imputation Hybrid Sampling (SMOTETomek)
57 Zero Imputation Hybrid Sampling (SMOTETomek)
58 Zero Imputation Hybrid Sampling (SMOTETomek)
59 Zero Imputation Hybrid Sampling (SMOTETomek)
60 Zero Imputation Hybrid Sampling (SMOTETomek)
61 Zero Imputation Hybrid Sampling (SMOTETomek)
62 Zero Imputation Hybrid Sampling (SMOTETomek)
63 Zero Imputation Hybrid Sampling (SMOTETomek)
64 Zero Imputation Hybrid Sampling (SMOTETomek)
65 Zero Imputation Hybrid Sampling (SMOTETomek)
66 Zero Imputation Hybrid Sampling (SMOTETomek)
67 Zero Imputation Hybrid Sampling (SMOTETomek)
68 Zero Imputation Hybrid Sampling (SMOTETomek)
69 Zero Imputation Hybrid Sampling (SMOTETomek)
70 Zero Imputation Hybrid Sampling (SMOTETomek)
71 Zero Imputation Hybrid Sampling (SMOTETomek)
72 Zero Imputation Hybrid Sampling (SMOTETomek)
73 Zero Imputation Hybrid Sampling (SMOTETomek)
74 Zero Imputation Hybrid Sampling (SMOTETomek)
75 Zero Imputation Hybrid Sampling (SMOTETomek)
76 Zero Imputation Hybrid Sampling (SMOTETomek)
77 Zero Imputation Hybrid Sampling (SMOTETomek)
78 Zero Imputation Hybrid Sampling (SMOTETomek)
79 Zero Imputation Hybrid Sampling (SMOTETomek)
80 Zero Imputation Hybrid Sampling (SMOTETomek)
81 Zero Imputation Hybrid Sampling (SMOTETomek)
82 Zero Imputation Hybrid Sampling (SMOTETomek)
83 Zero Imputation Hybrid Sampling (SMOTETomek)
84 Zero Imputation Hybrid Sampling (SMOTETomek)
85 Zero Imputation Hybrid Sampling (SMOTETomek)
86 Zero Imputation Hybrid Sampling (SMOTETomek)
87 Zero Imputation Hybrid Sampling (SMOTETomek)
88 Zero Imputation Hybrid Sampling (SMOTETomek)
89 Zero Imputation Hybrid Sampling (SMOTETomek)
90 Zero Imputation Hybrid Sampling (SMOTETomek)
91 Zero Imputation Hybrid Sampling (SMOTETomek)
92 Zero Imputation Hybrid Sampling (SMOTETomek)
93 Zero Imputation Hybrid Sampling (SMOTETomek)
94 Zero Imputation Hybrid Sampling (SMOTETomek)
95 Zero Imputation Hybrid Sampling (SMOTETomek)
96 Zero Imputation Hybrid Sampling (SMOTETomek)
97 Zero Imputation Hybrid Sampling (SMOTETomek)
98 Zero Imputation Hybrid Sampling (SMOTETomek)
99 Zero Imputation Hybrid Sampling (SMOTETomek)
100 Zero Imputation Hybrid Sampling (SMOTETomek)
101 Zero Imputation Hybrid Sampling (SMOTETomek)
102 Zero Imputation Hybrid Sampling (SMOTETomek)
103 Zero Imputation Hybrid Sampling (SMOTETomek)
104 Zero Imputation Hybrid Sampling (SMOTETomek)
105 Zero Imputation Hybrid Sampling (SMOTETomek)
106 Zero Imputation Hybrid Sampling (SMOTETomek)
107 Zero Imputation Hybrid Sampling (SMOTETomek)
108 Zero Imputation Hybrid Sampling (SMOTETomek)
109 Zero Imputation Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Support Vector Machines (SVM)_Hybrid Sampling ...
1 Support Vector Machines (SVM)_Hybrid Sampling ...
2 Support Vector Machines (SVM)_Hybrid Sampling ...
3 Support Vector Machines (SVM)_Hybrid Sampling ...
4 Support Vector Machines (SVM)_Hybrid Sampling ...
5 Support Vector Machines (SVM)_Hybrid Sampling ...
6 Support Vector Machines (SVM)_Hybrid Sampling ...
7 Support Vector Machines (SVM)_Hybrid Sampling ...
8 Support Vector Machines (SVM)_Hybrid Sampling ...
9 Support Vector Machines (SVM)_Hybrid Sampling ...
10 Support Vector Machines (SVM)_Hybrid Sampling ...
11 Support Vector Machines (SVM)_Hybrid Sampling ...
12 Support Vector Machines (SVM)_Hybrid Sampling ...
13 Support Vector Machines (SVM)_Hybrid Sampling ...
14 Support Vector Machines (SVM)_Hybrid Sampling ...
15 Support Vector Machines (SVM)_Hybrid Sampling ...
16 Support Vector Machines (SVM)_Hybrid Sampling ...
17 Support Vector Machines (SVM)_Hybrid Sampling ...
18 Support Vector Machines (SVM)_Hybrid Sampling ...
19 Support Vector Machines (SVM)_Hybrid Sampling ...
20 Support Vector Machines (SVM)_Hybrid Sampling ...
21 Support Vector Machines (SVM)_Hybrid Sampling ...
22 Support Vector Machines (SVM)_Hybrid Sampling ...
23 Support Vector Machines (SVM)_Hybrid Sampling ...
24 Support Vector Machines (SVM)_Hybrid Sampling ...
25 Support Vector Machines (SVM)_Hybrid Sampling ...
26 Support Vector Machines (SVM)_Hybrid Sampling ...
27 Support Vector Machines (SVM)_Hybrid Sampling ...
28 Support Vector Machines (SVM)_Hybrid Sampling ...
29 Support Vector Machines (SVM)_Hybrid Sampling ...
30 Support Vector Machines (SVM)_Hybrid Sampling ...
31 Support Vector Machines (SVM)_Hybrid Sampling ...
32 Support Vector Machines (SVM)_Hybrid Sampling ...
33 Support Vector Machines (SVM)_Hybrid Sampling ...
34 Support Vector Machines (SVM)_Hybrid Sampling ...
35 Support Vector Machines (SVM)_Hybrid Sampling ...
36 Support Vector Machines (SVM)_Hybrid Sampling ...
37 Support Vector Machines (SVM)_Hybrid Sampling ...
38 Support Vector Machines (SVM)_Hybrid Sampling ...
39 Support Vector Machines (SVM)_Hybrid Sampling ...
40 Support Vector Machines (SVM)_Hybrid Sampling ...
41 Support Vector Machines (SVM)_Hybrid Sampling ...
42 Support Vector Machines (SVM)_Hybrid Sampling ...
43 Support Vector Machines (SVM)_Hybrid Sampling ...
44 Support Vector Machines (SVM)_Hybrid Sampling ...
45 Support Vector Machines (SVM)_Hybrid Sampling ...
46 Support Vector Machines (SVM)_Hybrid Sampling ...
47 Support Vector Machines (SVM)_Hybrid Sampling ...
48 Support Vector Machines (SVM)_Hybrid Sampling ...
49 Support Vector Machines (SVM)_Hybrid Sampling ...
50 Support Vector Machines (SVM)_Hybrid Sampling ...
51 Support Vector Machines (SVM)_Hybrid Sampling ...
52 Support Vector Machines (SVM)_Hybrid Sampling ...
53 Support Vector Machines (SVM)_Hybrid Sampling ...
54 Support Vector Machines (SVM)_Hybrid Sampling ...
55 Support Vector Machines (SVM)_Hybrid Sampling ...
56 Support Vector Machines (SVM)_Hybrid Sampling ...
57 Support Vector Machines (SVM)_Hybrid Sampling ...
58 Support Vector Machines (SVM)_Hybrid Sampling ...
59 Support Vector Machines (SVM)_Hybrid Sampling ...
60 Support Vector Machines (SVM)_Hybrid Sampling ...
61 Support Vector Machines (SVM)_Hybrid Sampling ...
62 Support Vector Machines (SVM)_Hybrid Sampling ...
63 Support Vector Machines (SVM)_Hybrid Sampling ...
64 Support Vector Machines (SVM)_Hybrid Sampling ...
65 Support Vector Machines (SVM)_Hybrid Sampling ...
66 Support Vector Machines (SVM)_Hybrid Sampling ...
67 Support Vector Machines (SVM)_Hybrid Sampling ...
68 Support Vector Machines (SVM)_Hybrid Sampling ...
69 Support Vector Machines (SVM)_Hybrid Sampling ...
70 Support Vector Machines (SVM)_Hybrid Sampling ...
71 Support Vector Machines (SVM)_Hybrid Sampling ...
72 Support Vector Machines (SVM)_Hybrid Sampling ...
73 Support Vector Machines (SVM)_Hybrid Sampling ...
74 Support Vector Machines (SVM)_Hybrid Sampling ...
75 Support Vector Machines (SVM)_Hybrid Sampling ...
76 Support Vector Machines (SVM)_Hybrid Sampling ...
77 Support Vector Machines (SVM)_Hybrid Sampling ...
78 Support Vector Machines (SVM)_Hybrid Sampling ...
79 Support Vector Machines (SVM)_Hybrid Sampling ...
80 Support Vector Machines (SVM)_Hybrid Sampling ...
81 Support Vector Machines (SVM)_Hybrid Sampling ...
82 Support Vector Machines (SVM)_Hybrid Sampling ...
83 Support Vector Machines (SVM)_Hybrid Sampling ...
84 Support Vector Machines (SVM)_Hybrid Sampling ...
85 Support Vector Machines (SVM)_Hybrid Sampling ...
86 Support Vector Machines (SVM)_Hybrid Sampling ...
87 Support Vector Machines (SVM)_Hybrid Sampling ...
88 Support Vector Machines (SVM)_Hybrid Sampling ...
89 Support Vector Machines (SVM)_Hybrid Sampling ...
90 Support Vector Machines (SVM)_Hybrid Sampling ...
91 Support Vector Machines (SVM)_Hybrid Sampling ...
92 Support Vector Machines (SVM)_Hybrid Sampling ...
93 Support Vector Machines (SVM)_Hybrid Sampling ...
94 Support Vector Machines (SVM)_Hybrid Sampling ...
95 Support Vector Machines (SVM)_Hybrid Sampling ...
96 Support Vector Machines (SVM)_Hybrid Sampling ...
97 Support Vector Machines (SVM)_Hybrid Sampling ...
98 Support Vector Machines (SVM)_Hybrid Sampling ...
99 Support Vector Machines (SVM)_Hybrid Sampling ...
100 Support Vector Machines (SVM)_Hybrid Sampling ...
101 Support Vector Machines (SVM)_Hybrid Sampling ...
102 Support Vector Machines (SVM)_Hybrid Sampling ...
103 Support Vector Machines (SVM)_Hybrid Sampling ...
104 Support Vector Machines (SVM)_Hybrid Sampling ...
105 Support Vector Machines (SVM)_Hybrid Sampling ...
106 Support Vector Machines (SVM)_Hybrid Sampling ...
107 Support Vector Machines (SVM)_Hybrid Sampling ...
108 Support Vector Machines (SVM)_Hybrid Sampling ...
109 Support Vector Machines (SVM)_Hybrid Sampling ... ,
Algorithm Metrics Hyperparameters \
0 Support Vector Machines (SVM) 0.350277 NaN
1 Support Vector Machines (SVM) 0.073777 NaN
2 Support Vector Machines (SVM) 0.814346 NaN
3 Support Vector Machines (SVM) 0.135296 NaN
4 Support Vector Machines (SVM) 0.609686 NaN
5 Support Vector Machines (SVM) 0.165270 NaN
6 Support Vector Machines (SVM) 0.219371 NaN
7 Support Vector Machines (SVM) NaN 1.0
8 Support Vector Machines (SVM) NaN False
9 Support Vector Machines (SVM) NaN 200
10 Support Vector Machines (SVM) NaN None
11 Support Vector Machines (SVM) NaN 0.0
12 Support Vector Machines (SVM) NaN ovr
13 Support Vector Machines (SVM) NaN 3
14 Support Vector Machines (SVM) NaN scale
15 Support Vector Machines (SVM) NaN rbf
16 Support Vector Machines (SVM) NaN -1
17 Support Vector Machines (SVM) NaN True
18 Support Vector Machines (SVM) NaN None
19 Support Vector Machines (SVM) NaN True
20 Support Vector Machines (SVM) NaN 0.001
21 Support Vector Machines (SVM) NaN False
22 Support Vector Machines (SVM) 0.325784 NaN
23 Support Vector Machines (SVM) 0.052651 NaN
24 Support Vector Machines (SVM) 0.859756 NaN
25 Support Vector Machines (SVM) 0.099226 NaN
26 Support Vector Machines (SVM) 0.650485 NaN
27 Support Vector Machines (SVM) 0.260560 NaN
28 Support Vector Machines (SVM) 0.300969 NaN
29 Support Vector Machines (SVM) NaN 1.0
30 Support Vector Machines (SVM) NaN False
31 Support Vector Machines (SVM) NaN 200
32 Support Vector Machines (SVM) NaN None
33 Support Vector Machines (SVM) NaN 0.0
34 Support Vector Machines (SVM) NaN ovr
35 Support Vector Machines (SVM) NaN 3
36 Support Vector Machines (SVM) NaN scale
37 Support Vector Machines (SVM) NaN rbf
38 Support Vector Machines (SVM) NaN -1
39 Support Vector Machines (SVM) NaN True
40 Support Vector Machines (SVM) NaN None
41 Support Vector Machines (SVM) NaN True
42 Support Vector Machines (SVM) NaN 0.001
43 Support Vector Machines (SVM) NaN False
44 Support Vector Machines (SVM) 0.339742 NaN
45 Support Vector Machines (SVM) 0.058264 NaN
46 Support Vector Machines (SVM) 0.818182 NaN
47 Support Vector Machines (SVM) 0.108781 NaN
48 Support Vector Machines (SVM) 0.622146 NaN
49 Support Vector Machines (SVM) 0.212454 NaN
50 Support Vector Machines (SVM) 0.244292 NaN
51 Support Vector Machines (SVM) NaN 1.0
52 Support Vector Machines (SVM) NaN False
53 Support Vector Machines (SVM) NaN 200
54 Support Vector Machines (SVM) NaN None
55 Support Vector Machines (SVM) NaN 0.0
56 Support Vector Machines (SVM) NaN ovr
57 Support Vector Machines (SVM) NaN 3
58 Support Vector Machines (SVM) NaN scale
59 Support Vector Machines (SVM) NaN rbf
60 Support Vector Machines (SVM) NaN -1
61 Support Vector Machines (SVM) NaN True
62 Support Vector Machines (SVM) NaN None
63 Support Vector Machines (SVM) NaN True
64 Support Vector Machines (SVM) NaN 0.001
65 Support Vector Machines (SVM) NaN False
66 Support Vector Machines (SVM) 0.304268 NaN
67 Support Vector Machines (SVM) 0.060410 NaN
68 Support Vector Machines (SVM) 0.857143 NaN
69 Support Vector Machines (SVM) 0.112865 NaN
70 Support Vector Machines (SVM) 0.626279 NaN
71 Support Vector Machines (SVM) 0.203997 NaN
72 Support Vector Machines (SVM) 0.252558 NaN
73 Support Vector Machines (SVM) NaN 1.0
74 Support Vector Machines (SVM) NaN False
75 Support Vector Machines (SVM) NaN 200
76 Support Vector Machines (SVM) NaN None
77 Support Vector Machines (SVM) NaN 0.0
78 Support Vector Machines (SVM) NaN ovr
79 Support Vector Machines (SVM) NaN 3
80 Support Vector Machines (SVM) NaN scale
81 Support Vector Machines (SVM) NaN rbf
82 Support Vector Machines (SVM) NaN -1
83 Support Vector Machines (SVM) NaN True
84 Support Vector Machines (SVM) NaN None
85 Support Vector Machines (SVM) NaN True
86 Support Vector Machines (SVM) NaN 0.001
87 Support Vector Machines (SVM) NaN False
88 Support Vector Machines (SVM) 0.397787 NaN
89 Support Vector Machines (SVM) 0.074074 NaN
90 Support Vector Machines (SVM) 0.833333 NaN
91 Support Vector Machines (SVM) 0.136054 NaN
92 Support Vector Machines (SVM) 0.638092 NaN
93 Support Vector Machines (SVM) 0.222714 NaN
94 Support Vector Machines (SVM) 0.276183 NaN
95 Support Vector Machines (SVM) NaN 1.0
96 Support Vector Machines (SVM) NaN False
97 Support Vector Machines (SVM) NaN 200
98 Support Vector Machines (SVM) NaN None
99 Support Vector Machines (SVM) NaN 0.0
100 Support Vector Machines (SVM) NaN ovr
101 Support Vector Machines (SVM) NaN 3
102 Support Vector Machines (SVM) NaN scale
103 Support Vector Machines (SVM) NaN rbf
104 Support Vector Machines (SVM) NaN -1
105 Support Vector Machines (SVM) NaN True
106 Support Vector Machines (SVM) NaN None
107 Support Vector Machines (SVM) NaN True
108 Support Vector Machines (SVM) NaN 0.001
109 Support Vector Machines (SVM) NaN False
Imputation Technique Imbalance Class Technique \
0 Mean Imputation Hybrid Sampling (SMOTETomek)
1 Mean Imputation Hybrid Sampling (SMOTETomek)
2 Mean Imputation Hybrid Sampling (SMOTETomek)
3 Mean Imputation Hybrid Sampling (SMOTETomek)
4 Mean Imputation Hybrid Sampling (SMOTETomek)
5 Mean Imputation Hybrid Sampling (SMOTETomek)
6 Mean Imputation Hybrid Sampling (SMOTETomek)
7 Mean Imputation Hybrid Sampling (SMOTETomek)
8 Mean Imputation Hybrid Sampling (SMOTETomek)
9 Mean Imputation Hybrid Sampling (SMOTETomek)
10 Mean Imputation Hybrid Sampling (SMOTETomek)
11 Mean Imputation Hybrid Sampling (SMOTETomek)
12 Mean Imputation Hybrid Sampling (SMOTETomek)
13 Mean Imputation Hybrid Sampling (SMOTETomek)
14 Mean Imputation Hybrid Sampling (SMOTETomek)
15 Mean Imputation Hybrid Sampling (SMOTETomek)
16 Mean Imputation Hybrid Sampling (SMOTETomek)
17 Mean Imputation Hybrid Sampling (SMOTETomek)
18 Mean Imputation Hybrid Sampling (SMOTETomek)
19 Mean Imputation Hybrid Sampling (SMOTETomek)
20 Mean Imputation Hybrid Sampling (SMOTETomek)
21 Mean Imputation Hybrid Sampling (SMOTETomek)
22 Mean Imputation Hybrid Sampling (SMOTETomek)
23 Mean Imputation Hybrid Sampling (SMOTETomek)
24 Mean Imputation Hybrid Sampling (SMOTETomek)
25 Mean Imputation Hybrid Sampling (SMOTETomek)
26 Mean Imputation Hybrid Sampling (SMOTETomek)
27 Mean Imputation Hybrid Sampling (SMOTETomek)
28 Mean Imputation Hybrid Sampling (SMOTETomek)
29 Mean Imputation Hybrid Sampling (SMOTETomek)
30 Mean Imputation Hybrid Sampling (SMOTETomek)
31 Mean Imputation Hybrid Sampling (SMOTETomek)
32 Mean Imputation Hybrid Sampling (SMOTETomek)
33 Mean Imputation Hybrid Sampling (SMOTETomek)
34 Mean Imputation Hybrid Sampling (SMOTETomek)
35 Mean Imputation Hybrid Sampling (SMOTETomek)
36 Mean Imputation Hybrid Sampling (SMOTETomek)
37 Mean Imputation Hybrid Sampling (SMOTETomek)
38 Mean Imputation Hybrid Sampling (SMOTETomek)
39 Mean Imputation Hybrid Sampling (SMOTETomek)
40 Mean Imputation Hybrid Sampling (SMOTETomek)
41 Mean Imputation Hybrid Sampling (SMOTETomek)
42 Mean Imputation Hybrid Sampling (SMOTETomek)
43 Mean Imputation Hybrid Sampling (SMOTETomek)
44 Mean Imputation Hybrid Sampling (SMOTETomek)
45 Mean Imputation Hybrid Sampling (SMOTETomek)
46 Mean Imputation Hybrid Sampling (SMOTETomek)
47 Mean Imputation Hybrid Sampling (SMOTETomek)
48 Mean Imputation Hybrid Sampling (SMOTETomek)
49 Mean Imputation Hybrid Sampling (SMOTETomek)
50 Mean Imputation Hybrid Sampling (SMOTETomek)
51 Mean Imputation Hybrid Sampling (SMOTETomek)
52 Mean Imputation Hybrid Sampling (SMOTETomek)
53 Mean Imputation Hybrid Sampling (SMOTETomek)
54 Mean Imputation Hybrid Sampling (SMOTETomek)
55 Mean Imputation Hybrid Sampling (SMOTETomek)
56 Mean Imputation Hybrid Sampling (SMOTETomek)
57 Mean Imputation Hybrid Sampling (SMOTETomek)
58 Mean Imputation Hybrid Sampling (SMOTETomek)
59 Mean Imputation Hybrid Sampling (SMOTETomek)
60 Mean Imputation Hybrid Sampling (SMOTETomek)
61 Mean Imputation Hybrid Sampling (SMOTETomek)
62 Mean Imputation Hybrid Sampling (SMOTETomek)
63 Mean Imputation Hybrid Sampling (SMOTETomek)
64 Mean Imputation Hybrid Sampling (SMOTETomek)
65 Mean Imputation Hybrid Sampling (SMOTETomek)
66 Mean Imputation Hybrid Sampling (SMOTETomek)
67 Mean Imputation Hybrid Sampling (SMOTETomek)
68 Mean Imputation Hybrid Sampling (SMOTETomek)
69 Mean Imputation Hybrid Sampling (SMOTETomek)
70 Mean Imputation Hybrid Sampling (SMOTETomek)
71 Mean Imputation Hybrid Sampling (SMOTETomek)
72 Mean Imputation Hybrid Sampling (SMOTETomek)
73 Mean Imputation Hybrid Sampling (SMOTETomek)
74 Mean Imputation Hybrid Sampling (SMOTETomek)
75 Mean Imputation Hybrid Sampling (SMOTETomek)
76 Mean Imputation Hybrid Sampling (SMOTETomek)
77 Mean Imputation Hybrid Sampling (SMOTETomek)
78 Mean Imputation Hybrid Sampling (SMOTETomek)
79 Mean Imputation Hybrid Sampling (SMOTETomek)
80 Mean Imputation Hybrid Sampling (SMOTETomek)
81 Mean Imputation Hybrid Sampling (SMOTETomek)
82 Mean Imputation Hybrid Sampling (SMOTETomek)
83 Mean Imputation Hybrid Sampling (SMOTETomek)
84 Mean Imputation Hybrid Sampling (SMOTETomek)
85 Mean Imputation Hybrid Sampling (SMOTETomek)
86 Mean Imputation Hybrid Sampling (SMOTETomek)
87 Mean Imputation Hybrid Sampling (SMOTETomek)
88 Mean Imputation Hybrid Sampling (SMOTETomek)
89 Mean Imputation Hybrid Sampling (SMOTETomek)
90 Mean Imputation Hybrid Sampling (SMOTETomek)
91 Mean Imputation Hybrid Sampling (SMOTETomek)
92 Mean Imputation Hybrid Sampling (SMOTETomek)
93 Mean Imputation Hybrid Sampling (SMOTETomek)
94 Mean Imputation Hybrid Sampling (SMOTETomek)
95 Mean Imputation Hybrid Sampling (SMOTETomek)
96 Mean Imputation Hybrid Sampling (SMOTETomek)
97 Mean Imputation Hybrid Sampling (SMOTETomek)
98 Mean Imputation Hybrid Sampling (SMOTETomek)
99 Mean Imputation Hybrid Sampling (SMOTETomek)
100 Mean Imputation Hybrid Sampling (SMOTETomek)
101 Mean Imputation Hybrid Sampling (SMOTETomek)
102 Mean Imputation Hybrid Sampling (SMOTETomek)
103 Mean Imputation Hybrid Sampling (SMOTETomek)
104 Mean Imputation Hybrid Sampling (SMOTETomek)
105 Mean Imputation Hybrid Sampling (SMOTETomek)
106 Mean Imputation Hybrid Sampling (SMOTETomek)
107 Mean Imputation Hybrid Sampling (SMOTETomek)
108 Mean Imputation Hybrid Sampling (SMOTETomek)
109 Mean Imputation Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Support Vector Machines (SVM)_Hybrid Sampling ...
1 Support Vector Machines (SVM)_Hybrid Sampling ...
2 Support Vector Machines (SVM)_Hybrid Sampling ...
3 Support Vector Machines (SVM)_Hybrid Sampling ...
4 Support Vector Machines (SVM)_Hybrid Sampling ...
5 Support Vector Machines (SVM)_Hybrid Sampling ...
6 Support Vector Machines (SVM)_Hybrid Sampling ...
7 Support Vector Machines (SVM)_Hybrid Sampling ...
8 Support Vector Machines (SVM)_Hybrid Sampling ...
9 Support Vector Machines (SVM)_Hybrid Sampling ...
10 Support Vector Machines (SVM)_Hybrid Sampling ...
11 Support Vector Machines (SVM)_Hybrid Sampling ...
12 Support Vector Machines (SVM)_Hybrid Sampling ...
13 Support Vector Machines (SVM)_Hybrid Sampling ...
14 Support Vector Machines (SVM)_Hybrid Sampling ...
15 Support Vector Machines (SVM)_Hybrid Sampling ...
16 Support Vector Machines (SVM)_Hybrid Sampling ...
17 Support Vector Machines (SVM)_Hybrid Sampling ...
18 Support Vector Machines (SVM)_Hybrid Sampling ...
19 Support Vector Machines (SVM)_Hybrid Sampling ...
20 Support Vector Machines (SVM)_Hybrid Sampling ...
21 Support Vector Machines (SVM)_Hybrid Sampling ...
22 Support Vector Machines (SVM)_Hybrid Sampling ...
23 Support Vector Machines (SVM)_Hybrid Sampling ...
24 Support Vector Machines (SVM)_Hybrid Sampling ...
25 Support Vector Machines (SVM)_Hybrid Sampling ...
26 Support Vector Machines (SVM)_Hybrid Sampling ...
27 Support Vector Machines (SVM)_Hybrid Sampling ...
28 Support Vector Machines (SVM)_Hybrid Sampling ...
29 Support Vector Machines (SVM)_Hybrid Sampling ...
30 Support Vector Machines (SVM)_Hybrid Sampling ...
31 Support Vector Machines (SVM)_Hybrid Sampling ...
32 Support Vector Machines (SVM)_Hybrid Sampling ...
33 Support Vector Machines (SVM)_Hybrid Sampling ...
34 Support Vector Machines (SVM)_Hybrid Sampling ...
35 Support Vector Machines (SVM)_Hybrid Sampling ...
36 Support Vector Machines (SVM)_Hybrid Sampling ...
37 Support Vector Machines (SVM)_Hybrid Sampling ...
38 Support Vector Machines (SVM)_Hybrid Sampling ...
39 Support Vector Machines (SVM)_Hybrid Sampling ...
40 Support Vector Machines (SVM)_Hybrid Sampling ...
41 Support Vector Machines (SVM)_Hybrid Sampling ...
42 Support Vector Machines (SVM)_Hybrid Sampling ...
43 Support Vector Machines (SVM)_Hybrid Sampling ...
44 Support Vector Machines (SVM)_Hybrid Sampling ...
45 Support Vector Machines (SVM)_Hybrid Sampling ...
46 Support Vector Machines (SVM)_Hybrid Sampling ...
47 Support Vector Machines (SVM)_Hybrid Sampling ...
48 Support Vector Machines (SVM)_Hybrid Sampling ...
49 Support Vector Machines (SVM)_Hybrid Sampling ...
50 Support Vector Machines (SVM)_Hybrid Sampling ...
51 Support Vector Machines (SVM)_Hybrid Sampling ...
52 Support Vector Machines (SVM)_Hybrid Sampling ...
53 Support Vector Machines (SVM)_Hybrid Sampling ...
54 Support Vector Machines (SVM)_Hybrid Sampling ...
55 Support Vector Machines (SVM)_Hybrid Sampling ...
56 Support Vector Machines (SVM)_Hybrid Sampling ...
57 Support Vector Machines (SVM)_Hybrid Sampling ...
58 Support Vector Machines (SVM)_Hybrid Sampling ...
59 Support Vector Machines (SVM)_Hybrid Sampling ...
60 Support Vector Machines (SVM)_Hybrid Sampling ...
61 Support Vector Machines (SVM)_Hybrid Sampling ...
62 Support Vector Machines (SVM)_Hybrid Sampling ...
63 Support Vector Machines (SVM)_Hybrid Sampling ...
64 Support Vector Machines (SVM)_Hybrid Sampling ...
65 Support Vector Machines (SVM)_Hybrid Sampling ...
66 Support Vector Machines (SVM)_Hybrid Sampling ...
67 Support Vector Machines (SVM)_Hybrid Sampling ...
68 Support Vector Machines (SVM)_Hybrid Sampling ...
69 Support Vector Machines (SVM)_Hybrid Sampling ...
70 Support Vector Machines (SVM)_Hybrid Sampling ...
71 Support Vector Machines (SVM)_Hybrid Sampling ...
72 Support Vector Machines (SVM)_Hybrid Sampling ...
73 Support Vector Machines (SVM)_Hybrid Sampling ...
74 Support Vector Machines (SVM)_Hybrid Sampling ...
75 Support Vector Machines (SVM)_Hybrid Sampling ...
76 Support Vector Machines (SVM)_Hybrid Sampling ...
77 Support Vector Machines (SVM)_Hybrid Sampling ...
78 Support Vector Machines (SVM)_Hybrid Sampling ...
79 Support Vector Machines (SVM)_Hybrid Sampling ...
80 Support Vector Machines (SVM)_Hybrid Sampling ...
81 Support Vector Machines (SVM)_Hybrid Sampling ...
82 Support Vector Machines (SVM)_Hybrid Sampling ...
83 Support Vector Machines (SVM)_Hybrid Sampling ...
84 Support Vector Machines (SVM)_Hybrid Sampling ...
85 Support Vector Machines (SVM)_Hybrid Sampling ...
86 Support Vector Machines (SVM)_Hybrid Sampling ...
87 Support Vector Machines (SVM)_Hybrid Sampling ...
88 Support Vector Machines (SVM)_Hybrid Sampling ...
89 Support Vector Machines (SVM)_Hybrid Sampling ...
90 Support Vector Machines (SVM)_Hybrid Sampling ...
91 Support Vector Machines (SVM)_Hybrid Sampling ...
92 Support Vector Machines (SVM)_Hybrid Sampling ...
93 Support Vector Machines (SVM)_Hybrid Sampling ...
94 Support Vector Machines (SVM)_Hybrid Sampling ...
95 Support Vector Machines (SVM)_Hybrid Sampling ...
96 Support Vector Machines (SVM)_Hybrid Sampling ...
97 Support Vector Machines (SVM)_Hybrid Sampling ...
98 Support Vector Machines (SVM)_Hybrid Sampling ...
99 Support Vector Machines (SVM)_Hybrid Sampling ...
100 Support Vector Machines (SVM)_Hybrid Sampling ...
101 Support Vector Machines (SVM)_Hybrid Sampling ...
102 Support Vector Machines (SVM)_Hybrid Sampling ...
103 Support Vector Machines (SVM)_Hybrid Sampling ...
104 Support Vector Machines (SVM)_Hybrid Sampling ...
105 Support Vector Machines (SVM)_Hybrid Sampling ...
106 Support Vector Machines (SVM)_Hybrid Sampling ...
107 Support Vector Machines (SVM)_Hybrid Sampling ...
108 Support Vector Machines (SVM)_Hybrid Sampling ...
109 Support Vector Machines (SVM)_Hybrid Sampling ... ,
Algorithm Metrics Hyperparameters \
0 Support Vector Machines (SVM) 0.412694 NaN
1 Support Vector Machines (SVM) 0.080421 NaN
2 Support Vector Machines (SVM) 0.805907 NaN
3 Support Vector Machines (SVM) 0.146248 NaN
4 Support Vector Machines (SVM) 0.638190 NaN
5 Support Vector Machines (SVM) 0.232900 NaN
6 Support Vector Machines (SVM) 0.276380 NaN
7 Support Vector Machines (SVM) NaN 1.0
8 Support Vector Machines (SVM) NaN False
9 Support Vector Machines (SVM) NaN 200
10 Support Vector Machines (SVM) NaN None
11 Support Vector Machines (SVM) NaN 0.0
12 Support Vector Machines (SVM) NaN ovr
13 Support Vector Machines (SVM) NaN 3
14 Support Vector Machines (SVM) NaN scale
15 Support Vector Machines (SVM) NaN rbf
16 Support Vector Machines (SVM) NaN -1
17 Support Vector Machines (SVM) NaN True
18 Support Vector Machines (SVM) NaN None
19 Support Vector Machines (SVM) NaN True
20 Support Vector Machines (SVM) NaN 0.001
21 Support Vector Machines (SVM) NaN False
22 Support Vector Machines (SVM) 0.391888 NaN
23 Support Vector Machines (SVM) 0.056268 NaN
24 Support Vector Machines (SVM) 0.829268 NaN
25 Support Vector Machines (SVM) 0.105386 NaN
26 Support Vector Machines (SVM) 0.658811 NaN
27 Support Vector Machines (SVM) 0.281176 NaN
28 Support Vector Machines (SVM) 0.317622 NaN
29 Support Vector Machines (SVM) NaN 1.0
30 Support Vector Machines (SVM) NaN False
31 Support Vector Machines (SVM) NaN 200
32 Support Vector Machines (SVM) NaN None
33 Support Vector Machines (SVM) NaN 0.0
34 Support Vector Machines (SVM) NaN ovr
35 Support Vector Machines (SVM) NaN 3
36 Support Vector Machines (SVM) NaN scale
37 Support Vector Machines (SVM) NaN rbf
38 Support Vector Machines (SVM) NaN -1
39 Support Vector Machines (SVM) NaN True
40 Support Vector Machines (SVM) NaN None
41 Support Vector Machines (SVM) NaN True
42 Support Vector Machines (SVM) NaN 0.001
43 Support Vector Machines (SVM) NaN False
44 Support Vector Machines (SVM) 0.387148 NaN
45 Support Vector Machines (SVM) 0.062193 NaN
46 Support Vector Machines (SVM) 0.812834 NaN
47 Support Vector Machines (SVM) 0.115545 NaN
48 Support Vector Machines (SVM) 0.646691 NaN
49 Support Vector Machines (SVM) 0.245792 NaN
50 Support Vector Machines (SVM) 0.293383 NaN
51 Support Vector Machines (SVM) NaN 1.0
52 Support Vector Machines (SVM) NaN False
53 Support Vector Machines (SVM) NaN 200
54 Support Vector Machines (SVM) NaN None
55 Support Vector Machines (SVM) NaN 0.0
56 Support Vector Machines (SVM) NaN ovr
57 Support Vector Machines (SVM) NaN 3
58 Support Vector Machines (SVM) NaN scale
59 Support Vector Machines (SVM) NaN rbf
60 Support Vector Machines (SVM) NaN -1
61 Support Vector Machines (SVM) NaN True
62 Support Vector Machines (SVM) NaN None
63 Support Vector Machines (SVM) NaN True
64 Support Vector Machines (SVM) NaN 0.001
65 Support Vector Machines (SVM) NaN False
66 Support Vector Machines (SVM) 0.376449 NaN
67 Support Vector Machines (SVM) 0.065278 NaN
68 Support Vector Machines (SVM) 0.831633 NaN
69 Support Vector Machines (SVM) 0.121055 NaN
70 Support Vector Machines (SVM) 0.638420 NaN
71 Support Vector Machines (SVM) 0.236134 NaN
72 Support Vector Machines (SVM) 0.276841 NaN
73 Support Vector Machines (SVM) NaN 1.0
74 Support Vector Machines (SVM) NaN False
75 Support Vector Machines (SVM) NaN 200
76 Support Vector Machines (SVM) NaN None
77 Support Vector Machines (SVM) NaN 0.0
78 Support Vector Machines (SVM) NaN ovr
79 Support Vector Machines (SVM) NaN 3
80 Support Vector Machines (SVM) NaN scale
81 Support Vector Machines (SVM) NaN rbf
82 Support Vector Machines (SVM) NaN -1
83 Support Vector Machines (SVM) NaN True
84 Support Vector Machines (SVM) NaN None
85 Support Vector Machines (SVM) NaN True
86 Support Vector Machines (SVM) NaN 0.001
87 Support Vector Machines (SVM) NaN False
88 Support Vector Machines (SVM) 0.406481 NaN
89 Support Vector Machines (SVM) 0.072956 NaN
90 Support Vector Machines (SVM) 0.805556 NaN
91 Support Vector Machines (SVM) 0.133795 NaN
92 Support Vector Machines (SVM) 0.666178 NaN
93 Support Vector Machines (SVM) 0.267794 NaN
94 Support Vector Machines (SVM) 0.332356 NaN
95 Support Vector Machines (SVM) NaN 1.0
96 Support Vector Machines (SVM) NaN False
97 Support Vector Machines (SVM) NaN 200
98 Support Vector Machines (SVM) NaN None
99 Support Vector Machines (SVM) NaN 0.0
100 Support Vector Machines (SVM) NaN ovr
101 Support Vector Machines (SVM) NaN 3
102 Support Vector Machines (SVM) NaN scale
103 Support Vector Machines (SVM) NaN rbf
104 Support Vector Machines (SVM) NaN -1
105 Support Vector Machines (SVM) NaN True
106 Support Vector Machines (SVM) NaN None
107 Support Vector Machines (SVM) NaN True
108 Support Vector Machines (SVM) NaN 0.001
109 Support Vector Machines (SVM) NaN False
Imputation Technique Imbalance Class Technique \
0 Median Imputation Hybrid Sampling (SMOTETomek)
1 Median Imputation Hybrid Sampling (SMOTETomek)
2 Median Imputation Hybrid Sampling (SMOTETomek)
3 Median Imputation Hybrid Sampling (SMOTETomek)
4 Median Imputation Hybrid Sampling (SMOTETomek)
5 Median Imputation Hybrid Sampling (SMOTETomek)
6 Median Imputation Hybrid Sampling (SMOTETomek)
7 Median Imputation Hybrid Sampling (SMOTETomek)
8 Median Imputation Hybrid Sampling (SMOTETomek)
9 Median Imputation Hybrid Sampling (SMOTETomek)
10 Median Imputation Hybrid Sampling (SMOTETomek)
11 Median Imputation Hybrid Sampling (SMOTETomek)
12 Median Imputation Hybrid Sampling (SMOTETomek)
13 Median Imputation Hybrid Sampling (SMOTETomek)
14 Median Imputation Hybrid Sampling (SMOTETomek)
15 Median Imputation Hybrid Sampling (SMOTETomek)
16 Median Imputation Hybrid Sampling (SMOTETomek)
17 Median Imputation Hybrid Sampling (SMOTETomek)
18 Median Imputation Hybrid Sampling (SMOTETomek)
19 Median Imputation Hybrid Sampling (SMOTETomek)
20 Median Imputation Hybrid Sampling (SMOTETomek)
21 Median Imputation Hybrid Sampling (SMOTETomek)
22 Median Imputation Hybrid Sampling (SMOTETomek)
23 Median Imputation Hybrid Sampling (SMOTETomek)
24 Median Imputation Hybrid Sampling (SMOTETomek)
25 Median Imputation Hybrid Sampling (SMOTETomek)
26 Median Imputation Hybrid Sampling (SMOTETomek)
27 Median Imputation Hybrid Sampling (SMOTETomek)
28 Median Imputation Hybrid Sampling (SMOTETomek)
29 Median Imputation Hybrid Sampling (SMOTETomek)
30 Median Imputation Hybrid Sampling (SMOTETomek)
31 Median Imputation Hybrid Sampling (SMOTETomek)
32 Median Imputation Hybrid Sampling (SMOTETomek)
33 Median Imputation Hybrid Sampling (SMOTETomek)
34 Median Imputation Hybrid Sampling (SMOTETomek)
35 Median Imputation Hybrid Sampling (SMOTETomek)
36 Median Imputation Hybrid Sampling (SMOTETomek)
37 Median Imputation Hybrid Sampling (SMOTETomek)
38 Median Imputation Hybrid Sampling (SMOTETomek)
39 Median Imputation Hybrid Sampling (SMOTETomek)
40 Median Imputation Hybrid Sampling (SMOTETomek)
41 Median Imputation Hybrid Sampling (SMOTETomek)
42 Median Imputation Hybrid Sampling (SMOTETomek)
43 Median Imputation Hybrid Sampling (SMOTETomek)
44 Median Imputation Hybrid Sampling (SMOTETomek)
45 Median Imputation Hybrid Sampling (SMOTETomek)
46 Median Imputation Hybrid Sampling (SMOTETomek)
47 Median Imputation Hybrid Sampling (SMOTETomek)
48 Median Imputation Hybrid Sampling (SMOTETomek)
49 Median Imputation Hybrid Sampling (SMOTETomek)
50 Median Imputation Hybrid Sampling (SMOTETomek)
51 Median Imputation Hybrid Sampling (SMOTETomek)
52 Median Imputation Hybrid Sampling (SMOTETomek)
53 Median Imputation Hybrid Sampling (SMOTETomek)
54 Median Imputation Hybrid Sampling (SMOTETomek)
55 Median Imputation Hybrid Sampling (SMOTETomek)
56 Median Imputation Hybrid Sampling (SMOTETomek)
57 Median Imputation Hybrid Sampling (SMOTETomek)
58 Median Imputation Hybrid Sampling (SMOTETomek)
59 Median Imputation Hybrid Sampling (SMOTETomek)
60 Median Imputation Hybrid Sampling (SMOTETomek)
61 Median Imputation Hybrid Sampling (SMOTETomek)
62 Median Imputation Hybrid Sampling (SMOTETomek)
63 Median Imputation Hybrid Sampling (SMOTETomek)
64 Median Imputation Hybrid Sampling (SMOTETomek)
65 Median Imputation Hybrid Sampling (SMOTETomek)
66 Median Imputation Hybrid Sampling (SMOTETomek)
67 Median Imputation Hybrid Sampling (SMOTETomek)
68 Median Imputation Hybrid Sampling (SMOTETomek)
69 Median Imputation Hybrid Sampling (SMOTETomek)
70 Median Imputation Hybrid Sampling (SMOTETomek)
71 Median Imputation Hybrid Sampling (SMOTETomek)
72 Median Imputation Hybrid Sampling (SMOTETomek)
73 Median Imputation Hybrid Sampling (SMOTETomek)
74 Median Imputation Hybrid Sampling (SMOTETomek)
75 Median Imputation Hybrid Sampling (SMOTETomek)
76 Median Imputation Hybrid Sampling (SMOTETomek)
77 Median Imputation Hybrid Sampling (SMOTETomek)
78 Median Imputation Hybrid Sampling (SMOTETomek)
79 Median Imputation Hybrid Sampling (SMOTETomek)
80 Median Imputation Hybrid Sampling (SMOTETomek)
81 Median Imputation Hybrid Sampling (SMOTETomek)
82 Median Imputation Hybrid Sampling (SMOTETomek)
83 Median Imputation Hybrid Sampling (SMOTETomek)
84 Median Imputation Hybrid Sampling (SMOTETomek)
85 Median Imputation Hybrid Sampling (SMOTETomek)
86 Median Imputation Hybrid Sampling (SMOTETomek)
87 Median Imputation Hybrid Sampling (SMOTETomek)
88 Median Imputation Hybrid Sampling (SMOTETomek)
89 Median Imputation Hybrid Sampling (SMOTETomek)
90 Median Imputation Hybrid Sampling (SMOTETomek)
91 Median Imputation Hybrid Sampling (SMOTETomek)
92 Median Imputation Hybrid Sampling (SMOTETomek)
93 Median Imputation Hybrid Sampling (SMOTETomek)
94 Median Imputation Hybrid Sampling (SMOTETomek)
95 Median Imputation Hybrid Sampling (SMOTETomek)
96 Median Imputation Hybrid Sampling (SMOTETomek)
97 Median Imputation Hybrid Sampling (SMOTETomek)
98 Median Imputation Hybrid Sampling (SMOTETomek)
99 Median Imputation Hybrid Sampling (SMOTETomek)
100 Median Imputation Hybrid Sampling (SMOTETomek)
101 Median Imputation Hybrid Sampling (SMOTETomek)
102 Median Imputation Hybrid Sampling (SMOTETomek)
103 Median Imputation Hybrid Sampling (SMOTETomek)
104 Median Imputation Hybrid Sampling (SMOTETomek)
105 Median Imputation Hybrid Sampling (SMOTETomek)
106 Median Imputation Hybrid Sampling (SMOTETomek)
107 Median Imputation Hybrid Sampling (SMOTETomek)
108 Median Imputation Hybrid Sampling (SMOTETomek)
109 Median Imputation Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Support Vector Machines (SVM)_Hybrid Sampling ...
1 Support Vector Machines (SVM)_Hybrid Sampling ...
2 Support Vector Machines (SVM)_Hybrid Sampling ...
3 Support Vector Machines (SVM)_Hybrid Sampling ...
4 Support Vector Machines (SVM)_Hybrid Sampling ...
5 Support Vector Machines (SVM)_Hybrid Sampling ...
6 Support Vector Machines (SVM)_Hybrid Sampling ...
7 Support Vector Machines (SVM)_Hybrid Sampling ...
8 Support Vector Machines (SVM)_Hybrid Sampling ...
9 Support Vector Machines (SVM)_Hybrid Sampling ...
10 Support Vector Machines (SVM)_Hybrid Sampling ...
11 Support Vector Machines (SVM)_Hybrid Sampling ...
12 Support Vector Machines (SVM)_Hybrid Sampling ...
13 Support Vector Machines (SVM)_Hybrid Sampling ...
14 Support Vector Machines (SVM)_Hybrid Sampling ...
15 Support Vector Machines (SVM)_Hybrid Sampling ...
16 Support Vector Machines (SVM)_Hybrid Sampling ...
17 Support Vector Machines (SVM)_Hybrid Sampling ...
18 Support Vector Machines (SVM)_Hybrid Sampling ...
19 Support Vector Machines (SVM)_Hybrid Sampling ...
20 Support Vector Machines (SVM)_Hybrid Sampling ...
21 Support Vector Machines (SVM)_Hybrid Sampling ...
22 Support Vector Machines (SVM)_Hybrid Sampling ...
23 Support Vector Machines (SVM)_Hybrid Sampling ...
24 Support Vector Machines (SVM)_Hybrid Sampling ...
25 Support Vector Machines (SVM)_Hybrid Sampling ...
26 Support Vector Machines (SVM)_Hybrid Sampling ...
27 Support Vector Machines (SVM)_Hybrid Sampling ...
28 Support Vector Machines (SVM)_Hybrid Sampling ...
29 Support Vector Machines (SVM)_Hybrid Sampling ...
30 Support Vector Machines (SVM)_Hybrid Sampling ...
31 Support Vector Machines (SVM)_Hybrid Sampling ...
32 Support Vector Machines (SVM)_Hybrid Sampling ...
33 Support Vector Machines (SVM)_Hybrid Sampling ...
34 Support Vector Machines (SVM)_Hybrid Sampling ...
35 Support Vector Machines (SVM)_Hybrid Sampling ...
36 Support Vector Machines (SVM)_Hybrid Sampling ...
37 Support Vector Machines (SVM)_Hybrid Sampling ...
38 Support Vector Machines (SVM)_Hybrid Sampling ...
39 Support Vector Machines (SVM)_Hybrid Sampling ...
40 Support Vector Machines (SVM)_Hybrid Sampling ...
41 Support Vector Machines (SVM)_Hybrid Sampling ...
42 Support Vector Machines (SVM)_Hybrid Sampling ...
43 Support Vector Machines (SVM)_Hybrid Sampling ...
44 Support Vector Machines (SVM)_Hybrid Sampling ...
45 Support Vector Machines (SVM)_Hybrid Sampling ...
46 Support Vector Machines (SVM)_Hybrid Sampling ...
47 Support Vector Machines (SVM)_Hybrid Sampling ...
48 Support Vector Machines (SVM)_Hybrid Sampling ...
49 Support Vector Machines (SVM)_Hybrid Sampling ...
50 Support Vector Machines (SVM)_Hybrid Sampling ...
51 Support Vector Machines (SVM)_Hybrid Sampling ...
52 Support Vector Machines (SVM)_Hybrid Sampling ...
53 Support Vector Machines (SVM)_Hybrid Sampling ...
54 Support Vector Machines (SVM)_Hybrid Sampling ...
55 Support Vector Machines (SVM)_Hybrid Sampling ...
56 Support Vector Machines (SVM)_Hybrid Sampling ...
57 Support Vector Machines (SVM)_Hybrid Sampling ...
58 Support Vector Machines (SVM)_Hybrid Sampling ...
59 Support Vector Machines (SVM)_Hybrid Sampling ...
60 Support Vector Machines (SVM)_Hybrid Sampling ...
61 Support Vector Machines (SVM)_Hybrid Sampling ...
62 Support Vector Machines (SVM)_Hybrid Sampling ...
63 Support Vector Machines (SVM)_Hybrid Sampling ...
64 Support Vector Machines (SVM)_Hybrid Sampling ...
65 Support Vector Machines (SVM)_Hybrid Sampling ...
66 Support Vector Machines (SVM)_Hybrid Sampling ...
67 Support Vector Machines (SVM)_Hybrid Sampling ...
68 Support Vector Machines (SVM)_Hybrid Sampling ...
69 Support Vector Machines (SVM)_Hybrid Sampling ...
70 Support Vector Machines (SVM)_Hybrid Sampling ...
71 Support Vector Machines (SVM)_Hybrid Sampling ...
72 Support Vector Machines (SVM)_Hybrid Sampling ...
73 Support Vector Machines (SVM)_Hybrid Sampling ...
74 Support Vector Machines (SVM)_Hybrid Sampling ...
75 Support Vector Machines (SVM)_Hybrid Sampling ...
76 Support Vector Machines (SVM)_Hybrid Sampling ...
77 Support Vector Machines (SVM)_Hybrid Sampling ...
78 Support Vector Machines (SVM)_Hybrid Sampling ...
79 Support Vector Machines (SVM)_Hybrid Sampling ...
80 Support Vector Machines (SVM)_Hybrid Sampling ...
81 Support Vector Machines (SVM)_Hybrid Sampling ...
82 Support Vector Machines (SVM)_Hybrid Sampling ...
83 Support Vector Machines (SVM)_Hybrid Sampling ...
84 Support Vector Machines (SVM)_Hybrid Sampling ...
85 Support Vector Machines (SVM)_Hybrid Sampling ...
86 Support Vector Machines (SVM)_Hybrid Sampling ...
87 Support Vector Machines (SVM)_Hybrid Sampling ...
88 Support Vector Machines (SVM)_Hybrid Sampling ...
89 Support Vector Machines (SVM)_Hybrid Sampling ...
90 Support Vector Machines (SVM)_Hybrid Sampling ...
91 Support Vector Machines (SVM)_Hybrid Sampling ...
92 Support Vector Machines (SVM)_Hybrid Sampling ...
93 Support Vector Machines (SVM)_Hybrid Sampling ...
94 Support Vector Machines (SVM)_Hybrid Sampling ...
95 Support Vector Machines (SVM)_Hybrid Sampling ...
96 Support Vector Machines (SVM)_Hybrid Sampling ...
97 Support Vector Machines (SVM)_Hybrid Sampling ...
98 Support Vector Machines (SVM)_Hybrid Sampling ...
99 Support Vector Machines (SVM)_Hybrid Sampling ...
100 Support Vector Machines (SVM)_Hybrid Sampling ...
101 Support Vector Machines (SVM)_Hybrid Sampling ...
102 Support Vector Machines (SVM)_Hybrid Sampling ...
103 Support Vector Machines (SVM)_Hybrid Sampling ...
104 Support Vector Machines (SVM)_Hybrid Sampling ...
105 Support Vector Machines (SVM)_Hybrid Sampling ...
106 Support Vector Machines (SVM)_Hybrid Sampling ...
107 Support Vector Machines (SVM)_Hybrid Sampling ...
108 Support Vector Machines (SVM)_Hybrid Sampling ...
109 Support Vector Machines (SVM)_Hybrid Sampling ... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Neural Networks 0.674480 NaN Zero Imputation
1 Neural Networks 0.100080 NaN Zero Imputation
2 Neural Networks 0.527426 NaN Zero Imputation
3 Neural Networks 0.168237 NaN Zero Imputation
4 Neural Networks 0.613141 NaN Zero Imputation
5 Neural Networks 0.226182 NaN Zero Imputation
6 Neural Networks 0.226282 NaN Zero Imputation
7 Neural Networks NaN relu Zero Imputation
8 Neural Networks NaN 0.0001 Zero Imputation
9 Neural Networks NaN auto Zero Imputation
10 Neural Networks NaN 0.9 Zero Imputation
11 Neural Networks NaN 0.999 Zero Imputation
12 Neural Networks NaN False Zero Imputation
13 Neural Networks NaN 0.0 Zero Imputation
14 Neural Networks NaN (100,) Zero Imputation
15 Neural Networks NaN constant Zero Imputation
16 Neural Networks NaN 0.001 Zero Imputation
17 Neural Networks NaN 15000 Zero Imputation
18 Neural Networks NaN 200 Zero Imputation
19 Neural Networks NaN 0.9 Zero Imputation
20 Neural Networks NaN 10 Zero Imputation
21 Neural Networks NaN True Zero Imputation
22 Neural Networks NaN 0.5 Zero Imputation
23 Neural Networks NaN None Zero Imputation
24 Neural Networks NaN True Zero Imputation
25 Neural Networks NaN adam Zero Imputation
26 Neural Networks NaN 0.0001 Zero Imputation
27 Neural Networks NaN 0.1 Zero Imputation
28 Neural Networks NaN False Zero Imputation
29 Neural Networks NaN False Zero Imputation
30 Neural Networks 0.718462 NaN Zero Imputation
31 Neural Networks 0.075117 NaN Zero Imputation
32 Neural Networks 0.487805 NaN Zero Imputation
33 Neural Networks 0.130187 NaN Zero Imputation
34 Neural Networks 0.640110 NaN Zero Imputation
35 Neural Networks 0.246737 NaN Zero Imputation
36 Neural Networks 0.280219 NaN Zero Imputation
37 Neural Networks NaN relu Zero Imputation
38 Neural Networks NaN 0.0001 Zero Imputation
39 Neural Networks NaN auto Zero Imputation
40 Neural Networks NaN 0.9 Zero Imputation
41 Neural Networks NaN 0.999 Zero Imputation
42 Neural Networks NaN False Zero Imputation
43 Neural Networks NaN 0.0 Zero Imputation
44 Neural Networks NaN (100,) Zero Imputation
45 Neural Networks NaN constant Zero Imputation
46 Neural Networks NaN 0.001 Zero Imputation
47 Neural Networks NaN 15000 Zero Imputation
48 Neural Networks NaN 200 Zero Imputation
49 Neural Networks NaN 0.9 Zero Imputation
50 Neural Networks NaN 10 Zero Imputation
51 Neural Networks NaN True Zero Imputation
52 Neural Networks NaN 0.5 Zero Imputation
53 Neural Networks NaN None Zero Imputation
54 Neural Networks NaN True Zero Imputation
55 Neural Networks NaN adam Zero Imputation
56 Neural Networks NaN 0.0001 Zero Imputation
57 Neural Networks NaN 0.1 Zero Imputation
58 Neural Networks NaN False Zero Imputation
59 Neural Networks NaN False Zero Imputation
60 Neural Networks 0.809323 NaN Zero Imputation
61 Neural Networks 0.105727 NaN Zero Imputation
62 Neural Networks 0.385027 NaN Zero Imputation
63 Neural Networks 0.165899 NaN Zero Imputation
64 Neural Networks 0.636890 NaN Zero Imputation
65 Neural Networks 0.223230 NaN Zero Imputation
66 Neural Networks 0.273779 NaN Zero Imputation
67 Neural Networks NaN relu Zero Imputation
68 Neural Networks NaN 0.0001 Zero Imputation
69 Neural Networks NaN auto Zero Imputation
70 Neural Networks NaN 0.9 Zero Imputation
71 Neural Networks NaN 0.999 Zero Imputation
72 Neural Networks NaN False Zero Imputation
73 Neural Networks NaN 0.0 Zero Imputation
74 Neural Networks NaN (100,) Zero Imputation
75 Neural Networks NaN constant Zero Imputation
76 Neural Networks NaN 0.001 Zero Imputation
77 Neural Networks NaN 15000 Zero Imputation
78 Neural Networks NaN 200 Zero Imputation
79 Neural Networks NaN 0.9 Zero Imputation
80 Neural Networks NaN 10 Zero Imputation
81 Neural Networks NaN True Zero Imputation
82 Neural Networks NaN 0.5 Zero Imputation
83 Neural Networks NaN None Zero Imputation
84 Neural Networks NaN True Zero Imputation
85 Neural Networks NaN adam Zero Imputation
86 Neural Networks NaN 0.0001 Zero Imputation
87 Neural Networks NaN 0.1 Zero Imputation
88 Neural Networks NaN False Zero Imputation
89 Neural Networks NaN False Zero Imputation
90 Neural Networks 0.756849 NaN Zero Imputation
91 Neural Networks 0.086462 NaN Zero Imputation
92 Neural Networks 0.387755 NaN Zero Imputation
93 Neural Networks 0.141395 NaN Zero Imputation
94 Neural Networks 0.616377 NaN Zero Imputation
95 Neural Networks 0.223611 NaN Zero Imputation
96 Neural Networks 0.232754 NaN Zero Imputation
97 Neural Networks NaN relu Zero Imputation
98 Neural Networks NaN 0.0001 Zero Imputation
99 Neural Networks NaN auto Zero Imputation
100 Neural Networks NaN 0.9 Zero Imputation
101 Neural Networks NaN 0.999 Zero Imputation
102 Neural Networks NaN False Zero Imputation
103 Neural Networks NaN 0.0 Zero Imputation
104 Neural Networks NaN (100,) Zero Imputation
105 Neural Networks NaN constant Zero Imputation
106 Neural Networks NaN 0.001 Zero Imputation
107 Neural Networks NaN 15000 Zero Imputation
108 Neural Networks NaN 200 Zero Imputation
109 Neural Networks NaN 0.9 Zero Imputation
110 Neural Networks NaN 10 Zero Imputation
111 Neural Networks NaN True Zero Imputation
112 Neural Networks NaN 0.5 Zero Imputation
113 Neural Networks NaN None Zero Imputation
114 Neural Networks NaN True Zero Imputation
115 Neural Networks NaN adam Zero Imputation
116 Neural Networks NaN 0.0001 Zero Imputation
117 Neural Networks NaN 0.1 Zero Imputation
118 Neural Networks NaN False Zero Imputation
119 Neural Networks NaN False Zero Imputation
120 Neural Networks 0.711012 NaN Zero Imputation
121 Neural Networks 0.109832 NaN Zero Imputation
122 Neural Networks 0.574074 NaN Zero Imputation
123 Neural Networks 0.184387 NaN Zero Imputation
124 Neural Networks 0.661795 NaN Zero Imputation
125 Neural Networks 0.311034 NaN Zero Imputation
126 Neural Networks 0.323590 NaN Zero Imputation
127 Neural Networks NaN relu Zero Imputation
128 Neural Networks NaN 0.0001 Zero Imputation
129 Neural Networks NaN auto Zero Imputation
130 Neural Networks NaN 0.9 Zero Imputation
131 Neural Networks NaN 0.999 Zero Imputation
132 Neural Networks NaN False Zero Imputation
133 Neural Networks NaN 0.0 Zero Imputation
134 Neural Networks NaN (100,) Zero Imputation
135 Neural Networks NaN constant Zero Imputation
136 Neural Networks NaN 0.001 Zero Imputation
137 Neural Networks NaN 15000 Zero Imputation
138 Neural Networks NaN 200 Zero Imputation
139 Neural Networks NaN 0.9 Zero Imputation
140 Neural Networks NaN 10 Zero Imputation
141 Neural Networks NaN True Zero Imputation
142 Neural Networks NaN 0.5 Zero Imputation
143 Neural Networks NaN None Zero Imputation
144 Neural Networks NaN True Zero Imputation
145 Neural Networks NaN adam Zero Imputation
146 Neural Networks NaN 0.0001 Zero Imputation
147 Neural Networks NaN 0.1 Zero Imputation
148 Neural Networks NaN False Zero Imputation
149 Neural Networks NaN False Zero Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
45 Oversampling
46 Oversampling
47 Oversampling
48 Oversampling
49 Oversampling
50 Oversampling
51 Oversampling
52 Oversampling
53 Oversampling
54 Oversampling
55 Oversampling
56 Oversampling
57 Oversampling
58 Oversampling
59 Oversampling
60 Oversampling
61 Oversampling
62 Oversampling
63 Oversampling
64 Oversampling
65 Oversampling
66 Oversampling
67 Oversampling
68 Oversampling
69 Oversampling
70 Oversampling
71 Oversampling
72 Oversampling
73 Oversampling
74 Oversampling
75 Oversampling
76 Oversampling
77 Oversampling
78 Oversampling
79 Oversampling
80 Oversampling
81 Oversampling
82 Oversampling
83 Oversampling
84 Oversampling
85 Oversampling
86 Oversampling
87 Oversampling
88 Oversampling
89 Oversampling
90 Oversampling
91 Oversampling
92 Oversampling
93 Oversampling
94 Oversampling
95 Oversampling
96 Oversampling
97 Oversampling
98 Oversampling
99 Oversampling
100 Oversampling
101 Oversampling
102 Oversampling
103 Oversampling
104 Oversampling
105 Oversampling
106 Oversampling
107 Oversampling
108 Oversampling
109 Oversampling
110 Oversampling
111 Oversampling
112 Oversampling
113 Oversampling
114 Oversampling
115 Oversampling
116 Oversampling
117 Oversampling
118 Oversampling
119 Oversampling
120 Oversampling
121 Oversampling
122 Oversampling
123 Oversampling
124 Oversampling
125 Oversampling
126 Oversampling
127 Oversampling
128 Oversampling
129 Oversampling
130 Oversampling
131 Oversampling
132 Oversampling
133 Oversampling
134 Oversampling
135 Oversampling
136 Oversampling
137 Oversampling
138 Oversampling
139 Oversampling
140 Oversampling
141 Oversampling
142 Oversampling
143 Oversampling
144 Oversampling
145 Oversampling
146 Oversampling
147 Oversampling
148 Oversampling
149 Oversampling
Model Unique Code
0 Neural Networks_Oversampling_Zero Imputation_f...
1 Neural Networks_Oversampling_Zero Imputation_f...
2 Neural Networks_Oversampling_Zero Imputation_f...
3 Neural Networks_Oversampling_Zero Imputation_f...
4 Neural Networks_Oversampling_Zero Imputation_f...
5 Neural Networks_Oversampling_Zero Imputation_f...
6 Neural Networks_Oversampling_Zero Imputation_f...
7 Neural Networks_Oversampling_Zero Imputation_f...
8 Neural Networks_Oversampling_Zero Imputation_f...
9 Neural Networks_Oversampling_Zero Imputation_f...
10 Neural Networks_Oversampling_Zero Imputation_f...
11 Neural Networks_Oversampling_Zero Imputation_f...
12 Neural Networks_Oversampling_Zero Imputation_f...
13 Neural Networks_Oversampling_Zero Imputation_f...
14 Neural Networks_Oversampling_Zero Imputation_f...
15 Neural Networks_Oversampling_Zero Imputation_f...
16 Neural Networks_Oversampling_Zero Imputation_f...
17 Neural Networks_Oversampling_Zero Imputation_f...
18 Neural Networks_Oversampling_Zero Imputation_f...
19 Neural Networks_Oversampling_Zero Imputation_f...
20 Neural Networks_Oversampling_Zero Imputation_f...
21 Neural Networks_Oversampling_Zero Imputation_f...
22 Neural Networks_Oversampling_Zero Imputation_f...
23 Neural Networks_Oversampling_Zero Imputation_f...
24 Neural Networks_Oversampling_Zero Imputation_f...
25 Neural Networks_Oversampling_Zero Imputation_f...
26 Neural Networks_Oversampling_Zero Imputation_f...
27 Neural Networks_Oversampling_Zero Imputation_f...
28 Neural Networks_Oversampling_Zero Imputation_f...
29 Neural Networks_Oversampling_Zero Imputation_f...
30 Neural Networks_Oversampling_Zero Imputation_f...
31 Neural Networks_Oversampling_Zero Imputation_f...
32 Neural Networks_Oversampling_Zero Imputation_f...
33 Neural Networks_Oversampling_Zero Imputation_f...
34 Neural Networks_Oversampling_Zero Imputation_f...
35 Neural Networks_Oversampling_Zero Imputation_f...
36 Neural Networks_Oversampling_Zero Imputation_f...
37 Neural Networks_Oversampling_Zero Imputation_f...
38 Neural Networks_Oversampling_Zero Imputation_f...
39 Neural Networks_Oversampling_Zero Imputation_f...
40 Neural Networks_Oversampling_Zero Imputation_f...
41 Neural Networks_Oversampling_Zero Imputation_f...
42 Neural Networks_Oversampling_Zero Imputation_f...
43 Neural Networks_Oversampling_Zero Imputation_f...
44 Neural Networks_Oversampling_Zero Imputation_f...
45 Neural Networks_Oversampling_Zero Imputation_f...
46 Neural Networks_Oversampling_Zero Imputation_f...
47 Neural Networks_Oversampling_Zero Imputation_f...
48 Neural Networks_Oversampling_Zero Imputation_f...
49 Neural Networks_Oversampling_Zero Imputation_f...
50 Neural Networks_Oversampling_Zero Imputation_f...
51 Neural Networks_Oversampling_Zero Imputation_f...
52 Neural Networks_Oversampling_Zero Imputation_f...
53 Neural Networks_Oversampling_Zero Imputation_f...
54 Neural Networks_Oversampling_Zero Imputation_f...
55 Neural Networks_Oversampling_Zero Imputation_f...
56 Neural Networks_Oversampling_Zero Imputation_f...
57 Neural Networks_Oversampling_Zero Imputation_f...
58 Neural Networks_Oversampling_Zero Imputation_f...
59 Neural Networks_Oversampling_Zero Imputation_f...
60 Neural Networks_Oversampling_Zero Imputation_f...
61 Neural Networks_Oversampling_Zero Imputation_f...
62 Neural Networks_Oversampling_Zero Imputation_f...
63 Neural Networks_Oversampling_Zero Imputation_f...
64 Neural Networks_Oversampling_Zero Imputation_f...
65 Neural Networks_Oversampling_Zero Imputation_f...
66 Neural Networks_Oversampling_Zero Imputation_f...
67 Neural Networks_Oversampling_Zero Imputation_f...
68 Neural Networks_Oversampling_Zero Imputation_f...
69 Neural Networks_Oversampling_Zero Imputation_f...
70 Neural Networks_Oversampling_Zero Imputation_f...
71 Neural Networks_Oversampling_Zero Imputation_f...
72 Neural Networks_Oversampling_Zero Imputation_f...
73 Neural Networks_Oversampling_Zero Imputation_f...
74 Neural Networks_Oversampling_Zero Imputation_f...
75 Neural Networks_Oversampling_Zero Imputation_f...
76 Neural Networks_Oversampling_Zero Imputation_f...
77 Neural Networks_Oversampling_Zero Imputation_f...
78 Neural Networks_Oversampling_Zero Imputation_f...
79 Neural Networks_Oversampling_Zero Imputation_f...
80 Neural Networks_Oversampling_Zero Imputation_f...
81 Neural Networks_Oversampling_Zero Imputation_f...
82 Neural Networks_Oversampling_Zero Imputation_f...
83 Neural Networks_Oversampling_Zero Imputation_f...
84 Neural Networks_Oversampling_Zero Imputation_f...
85 Neural Networks_Oversampling_Zero Imputation_f...
86 Neural Networks_Oversampling_Zero Imputation_f...
87 Neural Networks_Oversampling_Zero Imputation_f...
88 Neural Networks_Oversampling_Zero Imputation_f...
89 Neural Networks_Oversampling_Zero Imputation_f...
90 Neural Networks_Oversampling_Zero Imputation_f...
91 Neural Networks_Oversampling_Zero Imputation_f...
92 Neural Networks_Oversampling_Zero Imputation_f...
93 Neural Networks_Oversampling_Zero Imputation_f...
94 Neural Networks_Oversampling_Zero Imputation_f...
95 Neural Networks_Oversampling_Zero Imputation_f...
96 Neural Networks_Oversampling_Zero Imputation_f...
97 Neural Networks_Oversampling_Zero Imputation_f...
98 Neural Networks_Oversampling_Zero Imputation_f...
99 Neural Networks_Oversampling_Zero Imputation_f...
100 Neural Networks_Oversampling_Zero Imputation_f...
101 Neural Networks_Oversampling_Zero Imputation_f...
102 Neural Networks_Oversampling_Zero Imputation_f...
103 Neural Networks_Oversampling_Zero Imputation_f...
104 Neural Networks_Oversampling_Zero Imputation_f...
105 Neural Networks_Oversampling_Zero Imputation_f...
106 Neural Networks_Oversampling_Zero Imputation_f...
107 Neural Networks_Oversampling_Zero Imputation_f...
108 Neural Networks_Oversampling_Zero Imputation_f...
109 Neural Networks_Oversampling_Zero Imputation_f...
110 Neural Networks_Oversampling_Zero Imputation_f...
111 Neural Networks_Oversampling_Zero Imputation_f...
112 Neural Networks_Oversampling_Zero Imputation_f...
113 Neural Networks_Oversampling_Zero Imputation_f...
114 Neural Networks_Oversampling_Zero Imputation_f...
115 Neural Networks_Oversampling_Zero Imputation_f...
116 Neural Networks_Oversampling_Zero Imputation_f...
117 Neural Networks_Oversampling_Zero Imputation_f...
118 Neural Networks_Oversampling_Zero Imputation_f...
119 Neural Networks_Oversampling_Zero Imputation_f...
120 Neural Networks_Oversampling_Zero Imputation_f...
121 Neural Networks_Oversampling_Zero Imputation_f...
122 Neural Networks_Oversampling_Zero Imputation_f...
123 Neural Networks_Oversampling_Zero Imputation_f...
124 Neural Networks_Oversampling_Zero Imputation_f...
125 Neural Networks_Oversampling_Zero Imputation_f...
126 Neural Networks_Oversampling_Zero Imputation_f...
127 Neural Networks_Oversampling_Zero Imputation_f...
128 Neural Networks_Oversampling_Zero Imputation_f...
129 Neural Networks_Oversampling_Zero Imputation_f...
130 Neural Networks_Oversampling_Zero Imputation_f...
131 Neural Networks_Oversampling_Zero Imputation_f...
132 Neural Networks_Oversampling_Zero Imputation_f...
133 Neural Networks_Oversampling_Zero Imputation_f...
134 Neural Networks_Oversampling_Zero Imputation_f...
135 Neural Networks_Oversampling_Zero Imputation_f...
136 Neural Networks_Oversampling_Zero Imputation_f...
137 Neural Networks_Oversampling_Zero Imputation_f...
138 Neural Networks_Oversampling_Zero Imputation_f...
139 Neural Networks_Oversampling_Zero Imputation_f...
140 Neural Networks_Oversampling_Zero Imputation_f...
141 Neural Networks_Oversampling_Zero Imputation_f...
142 Neural Networks_Oversampling_Zero Imputation_f...
143 Neural Networks_Oversampling_Zero Imputation_f...
144 Neural Networks_Oversampling_Zero Imputation_f...
145 Neural Networks_Oversampling_Zero Imputation_f...
146 Neural Networks_Oversampling_Zero Imputation_f...
147 Neural Networks_Oversampling_Zero Imputation_f...
148 Neural Networks_Oversampling_Zero Imputation_f...
149 Neural Networks_Oversampling_Zero Imputation_f... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Neural Networks 0.580985 NaN Mean Imputation
1 Neural Networks 0.077403 NaN Mean Imputation
2 Neural Networks 0.523207 NaN Mean Imputation
3 Neural Networks 0.134856 NaN Mean Imputation
4 Neural Networks 0.579217 NaN Mean Imputation
5 Neural Networks 0.147069 NaN Mean Imputation
6 Neural Networks 0.158434 NaN Mean Imputation
7 Neural Networks NaN relu Mean Imputation
8 Neural Networks NaN 0.0001 Mean Imputation
9 Neural Networks NaN auto Mean Imputation
10 Neural Networks NaN 0.9 Mean Imputation
11 Neural Networks NaN 0.999 Mean Imputation
12 Neural Networks NaN False Mean Imputation
13 Neural Networks NaN 0.0 Mean Imputation
14 Neural Networks NaN (100,) Mean Imputation
15 Neural Networks NaN constant Mean Imputation
16 Neural Networks NaN 0.001 Mean Imputation
17 Neural Networks NaN 15000 Mean Imputation
18 Neural Networks NaN 200 Mean Imputation
19 Neural Networks NaN 0.9 Mean Imputation
20 Neural Networks NaN 10 Mean Imputation
21 Neural Networks NaN True Mean Imputation
22 Neural Networks NaN 0.5 Mean Imputation
23 Neural Networks NaN None Mean Imputation
24 Neural Networks NaN True Mean Imputation
25 Neural Networks NaN adam Mean Imputation
26 Neural Networks NaN 0.0001 Mean Imputation
27 Neural Networks NaN 0.1 Mean Imputation
28 Neural Networks NaN False Mean Imputation
29 Neural Networks NaN False Mean Imputation
30 Neural Networks 0.921254 NaN Mean Imputation
31 Neural Networks 0.090909 NaN Mean Imputation
32 Neural Networks 0.091463 NaN Mean Imputation
33 Neural Networks 0.091185 NaN Mean Imputation
34 Neural Networks 0.651277 NaN Mean Imputation
35 Neural Networks 0.287792 NaN Mean Imputation
36 Neural Networks 0.302553 NaN Mean Imputation
37 Neural Networks NaN relu Mean Imputation
38 Neural Networks NaN 0.0001 Mean Imputation
39 Neural Networks NaN auto Mean Imputation
40 Neural Networks NaN 0.9 Mean Imputation
41 Neural Networks NaN 0.999 Mean Imputation
42 Neural Networks NaN False Mean Imputation
43 Neural Networks NaN 0.0 Mean Imputation
44 Neural Networks NaN (100,) Mean Imputation
45 Neural Networks NaN constant Mean Imputation
46 Neural Networks NaN 0.001 Mean Imputation
47 Neural Networks NaN 15000 Mean Imputation
48 Neural Networks NaN 200 Mean Imputation
49 Neural Networks NaN 0.9 Mean Imputation
50 Neural Networks NaN 10 Mean Imputation
51 Neural Networks NaN True Mean Imputation
52 Neural Networks NaN 0.5 Mean Imputation
53 Neural Networks NaN None Mean Imputation
54 Neural Networks NaN True Mean Imputation
55 Neural Networks NaN adam Mean Imputation
56 Neural Networks NaN 0.0001 Mean Imputation
57 Neural Networks NaN 0.1 Mean Imputation
58 Neural Networks NaN False Mean Imputation
59 Neural Networks NaN False Mean Imputation
60 Neural Networks 0.648670 NaN Mean Imputation
61 Neural Networks 0.074870 NaN Mean Imputation
62 Neural Networks 0.540107 NaN Mean Imputation
63 Neural Networks 0.131510 NaN Mean Imputation
64 Neural Networks 0.635684 NaN Mean Imputation
65 Neural Networks 0.230736 NaN Mean Imputation
66 Neural Networks 0.271369 NaN Mean Imputation
67 Neural Networks NaN relu Mean Imputation
68 Neural Networks NaN 0.0001 Mean Imputation
69 Neural Networks NaN auto Mean Imputation
70 Neural Networks NaN 0.9 Mean Imputation
71 Neural Networks NaN 0.999 Mean Imputation
72 Neural Networks NaN False Mean Imputation
73 Neural Networks NaN 0.0 Mean Imputation
74 Neural Networks NaN (100,) Mean Imputation
75 Neural Networks NaN constant Mean Imputation
76 Neural Networks NaN 0.001 Mean Imputation
77 Neural Networks NaN 15000 Mean Imputation
78 Neural Networks NaN 200 Mean Imputation
79 Neural Networks NaN 0.9 Mean Imputation
80 Neural Networks NaN 10 Mean Imputation
81 Neural Networks NaN True Mean Imputation
82 Neural Networks NaN 0.5 Mean Imputation
83 Neural Networks NaN None Mean Imputation
84 Neural Networks NaN True Mean Imputation
85 Neural Networks NaN adam Mean Imputation
86 Neural Networks NaN 0.0001 Mean Imputation
87 Neural Networks NaN 0.1 Mean Imputation
88 Neural Networks NaN False Mean Imputation
89 Neural Networks NaN False Mean Imputation
90 Neural Networks 0.920969 NaN Mean Imputation
91 Neural Networks 0.106061 NaN Mean Imputation
92 Neural Networks 0.071429 NaN Mean Imputation
93 Neural Networks 0.085366 NaN Mean Imputation
94 Neural Networks 0.643456 NaN Mean Imputation
95 Neural Networks 0.270765 NaN Mean Imputation
96 Neural Networks 0.286912 NaN Mean Imputation
97 Neural Networks NaN relu Mean Imputation
98 Neural Networks NaN 0.0001 Mean Imputation
99 Neural Networks NaN auto Mean Imputation
100 Neural Networks NaN 0.9 Mean Imputation
101 Neural Networks NaN 0.999 Mean Imputation
102 Neural Networks NaN False Mean Imputation
103 Neural Networks NaN 0.0 Mean Imputation
104 Neural Networks NaN (100,) Mean Imputation
105 Neural Networks NaN constant Mean Imputation
106 Neural Networks NaN 0.001 Mean Imputation
107 Neural Networks NaN 15000 Mean Imputation
108 Neural Networks NaN 200 Mean Imputation
109 Neural Networks NaN 0.9 Mean Imputation
110 Neural Networks NaN 10 Mean Imputation
111 Neural Networks NaN True Mean Imputation
112 Neural Networks NaN 0.5 Mean Imputation
113 Neural Networks NaN None Mean Imputation
114 Neural Networks NaN True Mean Imputation
115 Neural Networks NaN adam Mean Imputation
116 Neural Networks NaN 0.0001 Mean Imputation
117 Neural Networks NaN 0.1 Mean Imputation
118 Neural Networks NaN False Mean Imputation
119 Neural Networks NaN False Mean Imputation
120 Neural Networks 0.640674 NaN Mean Imputation
121 Neural Networks 0.087644 NaN Mean Imputation
122 Neural Networks 0.564815 NaN Mean Imputation
123 Neural Networks 0.151741 NaN Mean Imputation
124 Neural Networks 0.648949 NaN Mean Imputation
125 Neural Networks 0.233240 NaN Mean Imputation
126 Neural Networks 0.297899 NaN Mean Imputation
127 Neural Networks NaN relu Mean Imputation
128 Neural Networks NaN 0.0001 Mean Imputation
129 Neural Networks NaN auto Mean Imputation
130 Neural Networks NaN 0.9 Mean Imputation
131 Neural Networks NaN 0.999 Mean Imputation
132 Neural Networks NaN False Mean Imputation
133 Neural Networks NaN 0.0 Mean Imputation
134 Neural Networks NaN (100,) Mean Imputation
135 Neural Networks NaN constant Mean Imputation
136 Neural Networks NaN 0.001 Mean Imputation
137 Neural Networks NaN 15000 Mean Imputation
138 Neural Networks NaN 200 Mean Imputation
139 Neural Networks NaN 0.9 Mean Imputation
140 Neural Networks NaN 10 Mean Imputation
141 Neural Networks NaN True Mean Imputation
142 Neural Networks NaN 0.5 Mean Imputation
143 Neural Networks NaN None Mean Imputation
144 Neural Networks NaN True Mean Imputation
145 Neural Networks NaN adam Mean Imputation
146 Neural Networks NaN 0.0001 Mean Imputation
147 Neural Networks NaN 0.1 Mean Imputation
148 Neural Networks NaN False Mean Imputation
149 Neural Networks NaN False Mean Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
45 Oversampling
46 Oversampling
47 Oversampling
48 Oversampling
49 Oversampling
50 Oversampling
51 Oversampling
52 Oversampling
53 Oversampling
54 Oversampling
55 Oversampling
56 Oversampling
57 Oversampling
58 Oversampling
59 Oversampling
60 Oversampling
61 Oversampling
62 Oversampling
63 Oversampling
64 Oversampling
65 Oversampling
66 Oversampling
67 Oversampling
68 Oversampling
69 Oversampling
70 Oversampling
71 Oversampling
72 Oversampling
73 Oversampling
74 Oversampling
75 Oversampling
76 Oversampling
77 Oversampling
78 Oversampling
79 Oversampling
80 Oversampling
81 Oversampling
82 Oversampling
83 Oversampling
84 Oversampling
85 Oversampling
86 Oversampling
87 Oversampling
88 Oversampling
89 Oversampling
90 Oversampling
91 Oversampling
92 Oversampling
93 Oversampling
94 Oversampling
95 Oversampling
96 Oversampling
97 Oversampling
98 Oversampling
99 Oversampling
100 Oversampling
101 Oversampling
102 Oversampling
103 Oversampling
104 Oversampling
105 Oversampling
106 Oversampling
107 Oversampling
108 Oversampling
109 Oversampling
110 Oversampling
111 Oversampling
112 Oversampling
113 Oversampling
114 Oversampling
115 Oversampling
116 Oversampling
117 Oversampling
118 Oversampling
119 Oversampling
120 Oversampling
121 Oversampling
122 Oversampling
123 Oversampling
124 Oversampling
125 Oversampling
126 Oversampling
127 Oversampling
128 Oversampling
129 Oversampling
130 Oversampling
131 Oversampling
132 Oversampling
133 Oversampling
134 Oversampling
135 Oversampling
136 Oversampling
137 Oversampling
138 Oversampling
139 Oversampling
140 Oversampling
141 Oversampling
142 Oversampling
143 Oversampling
144 Oversampling
145 Oversampling
146 Oversampling
147 Oversampling
148 Oversampling
149 Oversampling
Model Unique Code
0 Neural Networks_Oversampling_Mean Imputation_f...
1 Neural Networks_Oversampling_Mean Imputation_f...
2 Neural Networks_Oversampling_Mean Imputation_f...
3 Neural Networks_Oversampling_Mean Imputation_f...
4 Neural Networks_Oversampling_Mean Imputation_f...
5 Neural Networks_Oversampling_Mean Imputation_f...
6 Neural Networks_Oversampling_Mean Imputation_f...
7 Neural Networks_Oversampling_Mean Imputation_f...
8 Neural Networks_Oversampling_Mean Imputation_f...
9 Neural Networks_Oversampling_Mean Imputation_f...
10 Neural Networks_Oversampling_Mean Imputation_f...
11 Neural Networks_Oversampling_Mean Imputation_f...
12 Neural Networks_Oversampling_Mean Imputation_f...
13 Neural Networks_Oversampling_Mean Imputation_f...
14 Neural Networks_Oversampling_Mean Imputation_f...
15 Neural Networks_Oversampling_Mean Imputation_f...
16 Neural Networks_Oversampling_Mean Imputation_f...
17 Neural Networks_Oversampling_Mean Imputation_f...
18 Neural Networks_Oversampling_Mean Imputation_f...
19 Neural Networks_Oversampling_Mean Imputation_f...
20 Neural Networks_Oversampling_Mean Imputation_f...
21 Neural Networks_Oversampling_Mean Imputation_f...
22 Neural Networks_Oversampling_Mean Imputation_f...
23 Neural Networks_Oversampling_Mean Imputation_f...
24 Neural Networks_Oversampling_Mean Imputation_f...
25 Neural Networks_Oversampling_Mean Imputation_f...
26 Neural Networks_Oversampling_Mean Imputation_f...
27 Neural Networks_Oversampling_Mean Imputation_f...
28 Neural Networks_Oversampling_Mean Imputation_f...
29 Neural Networks_Oversampling_Mean Imputation_f...
30 Neural Networks_Oversampling_Mean Imputation_f...
31 Neural Networks_Oversampling_Mean Imputation_f...
32 Neural Networks_Oversampling_Mean Imputation_f...
33 Neural Networks_Oversampling_Mean Imputation_f...
34 Neural Networks_Oversampling_Mean Imputation_f...
35 Neural Networks_Oversampling_Mean Imputation_f...
36 Neural Networks_Oversampling_Mean Imputation_f...
37 Neural Networks_Oversampling_Mean Imputation_f...
38 Neural Networks_Oversampling_Mean Imputation_f...
39 Neural Networks_Oversampling_Mean Imputation_f...
40 Neural Networks_Oversampling_Mean Imputation_f...
41 Neural Networks_Oversampling_Mean Imputation_f...
42 Neural Networks_Oversampling_Mean Imputation_f...
43 Neural Networks_Oversampling_Mean Imputation_f...
44 Neural Networks_Oversampling_Mean Imputation_f...
45 Neural Networks_Oversampling_Mean Imputation_f...
46 Neural Networks_Oversampling_Mean Imputation_f...
47 Neural Networks_Oversampling_Mean Imputation_f...
48 Neural Networks_Oversampling_Mean Imputation_f...
49 Neural Networks_Oversampling_Mean Imputation_f...
50 Neural Networks_Oversampling_Mean Imputation_f...
51 Neural Networks_Oversampling_Mean Imputation_f...
52 Neural Networks_Oversampling_Mean Imputation_f...
53 Neural Networks_Oversampling_Mean Imputation_f...
54 Neural Networks_Oversampling_Mean Imputation_f...
55 Neural Networks_Oversampling_Mean Imputation_f...
56 Neural Networks_Oversampling_Mean Imputation_f...
57 Neural Networks_Oversampling_Mean Imputation_f...
58 Neural Networks_Oversampling_Mean Imputation_f...
59 Neural Networks_Oversampling_Mean Imputation_f...
60 Neural Networks_Oversampling_Mean Imputation_f...
61 Neural Networks_Oversampling_Mean Imputation_f...
62 Neural Networks_Oversampling_Mean Imputation_f...
63 Neural Networks_Oversampling_Mean Imputation_f...
64 Neural Networks_Oversampling_Mean Imputation_f...
65 Neural Networks_Oversampling_Mean Imputation_f...
66 Neural Networks_Oversampling_Mean Imputation_f...
67 Neural Networks_Oversampling_Mean Imputation_f...
68 Neural Networks_Oversampling_Mean Imputation_f...
69 Neural Networks_Oversampling_Mean Imputation_f...
70 Neural Networks_Oversampling_Mean Imputation_f...
71 Neural Networks_Oversampling_Mean Imputation_f...
72 Neural Networks_Oversampling_Mean Imputation_f...
73 Neural Networks_Oversampling_Mean Imputation_f...
74 Neural Networks_Oversampling_Mean Imputation_f...
75 Neural Networks_Oversampling_Mean Imputation_f...
76 Neural Networks_Oversampling_Mean Imputation_f...
77 Neural Networks_Oversampling_Mean Imputation_f...
78 Neural Networks_Oversampling_Mean Imputation_f...
79 Neural Networks_Oversampling_Mean Imputation_f...
80 Neural Networks_Oversampling_Mean Imputation_f...
81 Neural Networks_Oversampling_Mean Imputation_f...
82 Neural Networks_Oversampling_Mean Imputation_f...
83 Neural Networks_Oversampling_Mean Imputation_f...
84 Neural Networks_Oversampling_Mean Imputation_f...
85 Neural Networks_Oversampling_Mean Imputation_f...
86 Neural Networks_Oversampling_Mean Imputation_f...
87 Neural Networks_Oversampling_Mean Imputation_f...
88 Neural Networks_Oversampling_Mean Imputation_f...
89 Neural Networks_Oversampling_Mean Imputation_f...
90 Neural Networks_Oversampling_Mean Imputation_f...
91 Neural Networks_Oversampling_Mean Imputation_f...
92 Neural Networks_Oversampling_Mean Imputation_f...
93 Neural Networks_Oversampling_Mean Imputation_f...
94 Neural Networks_Oversampling_Mean Imputation_f...
95 Neural Networks_Oversampling_Mean Imputation_f...
96 Neural Networks_Oversampling_Mean Imputation_f...
97 Neural Networks_Oversampling_Mean Imputation_f...
98 Neural Networks_Oversampling_Mean Imputation_f...
99 Neural Networks_Oversampling_Mean Imputation_f...
100 Neural Networks_Oversampling_Mean Imputation_f...
101 Neural Networks_Oversampling_Mean Imputation_f...
102 Neural Networks_Oversampling_Mean Imputation_f...
103 Neural Networks_Oversampling_Mean Imputation_f...
104 Neural Networks_Oversampling_Mean Imputation_f...
105 Neural Networks_Oversampling_Mean Imputation_f...
106 Neural Networks_Oversampling_Mean Imputation_f...
107 Neural Networks_Oversampling_Mean Imputation_f...
108 Neural Networks_Oversampling_Mean Imputation_f...
109 Neural Networks_Oversampling_Mean Imputation_f...
110 Neural Networks_Oversampling_Mean Imputation_f...
111 Neural Networks_Oversampling_Mean Imputation_f...
112 Neural Networks_Oversampling_Mean Imputation_f...
113 Neural Networks_Oversampling_Mean Imputation_f...
114 Neural Networks_Oversampling_Mean Imputation_f...
115 Neural Networks_Oversampling_Mean Imputation_f...
116 Neural Networks_Oversampling_Mean Imputation_f...
117 Neural Networks_Oversampling_Mean Imputation_f...
118 Neural Networks_Oversampling_Mean Imputation_f...
119 Neural Networks_Oversampling_Mean Imputation_f...
120 Neural Networks_Oversampling_Mean Imputation_f...
121 Neural Networks_Oversampling_Mean Imputation_f...
122 Neural Networks_Oversampling_Mean Imputation_f...
123 Neural Networks_Oversampling_Mean Imputation_f...
124 Neural Networks_Oversampling_Mean Imputation_f...
125 Neural Networks_Oversampling_Mean Imputation_f...
126 Neural Networks_Oversampling_Mean Imputation_f...
127 Neural Networks_Oversampling_Mean Imputation_f...
128 Neural Networks_Oversampling_Mean Imputation_f...
129 Neural Networks_Oversampling_Mean Imputation_f...
130 Neural Networks_Oversampling_Mean Imputation_f...
131 Neural Networks_Oversampling_Mean Imputation_f...
132 Neural Networks_Oversampling_Mean Imputation_f...
133 Neural Networks_Oversampling_Mean Imputation_f...
134 Neural Networks_Oversampling_Mean Imputation_f...
135 Neural Networks_Oversampling_Mean Imputation_f...
136 Neural Networks_Oversampling_Mean Imputation_f...
137 Neural Networks_Oversampling_Mean Imputation_f...
138 Neural Networks_Oversampling_Mean Imputation_f...
139 Neural Networks_Oversampling_Mean Imputation_f...
140 Neural Networks_Oversampling_Mean Imputation_f...
141 Neural Networks_Oversampling_Mean Imputation_f...
142 Neural Networks_Oversampling_Mean Imputation_f...
143 Neural Networks_Oversampling_Mean Imputation_f...
144 Neural Networks_Oversampling_Mean Imputation_f...
145 Neural Networks_Oversampling_Mean Imputation_f...
146 Neural Networks_Oversampling_Mean Imputation_f...
147 Neural Networks_Oversampling_Mean Imputation_f...
148 Neural Networks_Oversampling_Mean Imputation_f...
149 Neural Networks_Oversampling_Mean Imputation_f... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Neural Networks 0.699763 NaN Median Imputation
1 Neural Networks 0.095792 NaN Median Imputation
2 Neural Networks 0.451477 NaN Median Imputation
3 Neural Networks 0.158050 NaN Median Imputation
4 Neural Networks 0.636304 NaN Median Imputation
5 Neural Networks 0.214517 NaN Median Imputation
6 Neural Networks 0.272608 NaN Median Imputation
7 Neural Networks NaN relu Median Imputation
8 Neural Networks NaN 0.0001 Median Imputation
9 Neural Networks NaN auto Median Imputation
10 Neural Networks NaN 0.9 Median Imputation
11 Neural Networks NaN 0.999 Median Imputation
12 Neural Networks NaN False Median Imputation
13 Neural Networks NaN 0.0 Median Imputation
14 Neural Networks NaN (100,) Median Imputation
15 Neural Networks NaN constant Median Imputation
16 Neural Networks NaN 0.001 Median Imputation
17 Neural Networks NaN 15000 Median Imputation
18 Neural Networks NaN 200 Median Imputation
19 Neural Networks NaN 0.9 Median Imputation
20 Neural Networks NaN 10 Median Imputation
21 Neural Networks NaN True Median Imputation
22 Neural Networks NaN 0.5 Median Imputation
23 Neural Networks NaN None Median Imputation
24 Neural Networks NaN True Median Imputation
25 Neural Networks NaN adam Median Imputation
26 Neural Networks NaN 0.0001 Median Imputation
27 Neural Networks NaN 0.1 Median Imputation
28 Neural Networks NaN False Median Imputation
29 Neural Networks NaN False Median Imputation
30 Neural Networks 0.608902 NaN Median Imputation
31 Neural Networks 0.065174 NaN Median Imputation
32 Neural Networks 0.603659 NaN Median Imputation
33 Neural Networks 0.117647 NaN Median Imputation
34 Neural Networks 0.622682 NaN Median Imputation
35 Neural Networks 0.223233 NaN Median Imputation
36 Neural Networks 0.245364 NaN Median Imputation
37 Neural Networks NaN relu Median Imputation
38 Neural Networks NaN 0.0001 Median Imputation
39 Neural Networks NaN auto Median Imputation
40 Neural Networks NaN 0.9 Median Imputation
41 Neural Networks NaN 0.999 Median Imputation
42 Neural Networks NaN False Median Imputation
43 Neural Networks NaN 0.0 Median Imputation
44 Neural Networks NaN (100,) Median Imputation
45 Neural Networks NaN constant Median Imputation
46 Neural Networks NaN 0.001 Median Imputation
47 Neural Networks NaN 15000 Median Imputation
48 Neural Networks NaN 200 Median Imputation
49 Neural Networks NaN 0.9 Median Imputation
50 Neural Networks NaN 10 Median Imputation
51 Neural Networks NaN True Median Imputation
52 Neural Networks NaN 0.5 Median Imputation
53 Neural Networks NaN None Median Imputation
54 Neural Networks NaN True Median Imputation
55 Neural Networks NaN adam Median Imputation
56 Neural Networks NaN 0.0001 Median Imputation
57 Neural Networks NaN 0.1 Median Imputation
58 Neural Networks NaN False Median Imputation
59 Neural Networks NaN False Median Imputation
60 Neural Networks 0.595207 NaN Median Imputation
61 Neural Networks 0.070611 NaN Median Imputation
62 Neural Networks 0.593583 NaN Median Imputation
63 Neural Networks 0.126208 NaN Median Imputation
64 Neural Networks 0.640814 NaN Median Imputation
65 Neural Networks 0.218094 NaN Median Imputation
66 Neural Networks 0.281627 NaN Median Imputation
67 Neural Networks NaN relu Median Imputation
68 Neural Networks NaN 0.0001 Median Imputation
69 Neural Networks NaN auto Median Imputation
70 Neural Networks NaN 0.9 Median Imputation
71 Neural Networks NaN 0.999 Median Imputation
72 Neural Networks NaN False Median Imputation
73 Neural Networks NaN 0.0 Median Imputation
74 Neural Networks NaN (100,) Median Imputation
75 Neural Networks NaN constant Median Imputation
76 Neural Networks NaN 0.001 Median Imputation
77 Neural Networks NaN 15000 Median Imputation
78 Neural Networks NaN 200 Median Imputation
79 Neural Networks NaN 0.9 Median Imputation
80 Neural Networks NaN 10 Median Imputation
81 Neural Networks NaN True Median Imputation
82 Neural Networks NaN 0.5 Median Imputation
83 Neural Networks NaN None Median Imputation
84 Neural Networks NaN True Median Imputation
85 Neural Networks NaN adam Median Imputation
86 Neural Networks NaN 0.0001 Median Imputation
87 Neural Networks NaN 0.1 Median Imputation
88 Neural Networks NaN False Median Imputation
89 Neural Networks NaN False Median Imputation
90 Neural Networks 0.896997 NaN Median Imputation
91 Neural Networks 0.108434 NaN Median Imputation
92 Neural Networks 0.137755 NaN Median Imputation
93 Neural Networks 0.121348 NaN Median Imputation
94 Neural Networks 0.638045 NaN Median Imputation
95 Neural Networks 0.236973 NaN Median Imputation
96 Neural Networks 0.276090 NaN Median Imputation
97 Neural Networks NaN relu Median Imputation
98 Neural Networks NaN 0.0001 Median Imputation
99 Neural Networks NaN auto Median Imputation
100 Neural Networks NaN 0.9 Median Imputation
101 Neural Networks NaN 0.999 Median Imputation
102 Neural Networks NaN False Median Imputation
103 Neural Networks NaN 0.0 Median Imputation
104 Neural Networks NaN (100,) Median Imputation
105 Neural Networks NaN constant Median Imputation
106 Neural Networks NaN 0.001 Median Imputation
107 Neural Networks NaN 15000 Median Imputation
108 Neural Networks NaN 200 Median Imputation
109 Neural Networks NaN 0.9 Median Imputation
110 Neural Networks NaN 10 Median Imputation
111 Neural Networks NaN True Median Imputation
112 Neural Networks NaN 0.5 Median Imputation
113 Neural Networks NaN None Median Imputation
114 Neural Networks NaN True Median Imputation
115 Neural Networks NaN adam Median Imputation
116 Neural Networks NaN 0.0001 Median Imputation
117 Neural Networks NaN 0.1 Median Imputation
118 Neural Networks NaN False Median Imputation
119 Neural Networks NaN False Median Imputation
120 Neural Networks 0.904110 NaN Median Imputation
121 Neural Networks 0.084270 NaN Median Imputation
122 Neural Networks 0.069444 NaN Median Imputation
123 Neural Networks 0.076142 NaN Median Imputation
124 Neural Networks 0.666552 NaN Median Imputation
125 Neural Networks 0.313377 NaN Median Imputation
126 Neural Networks 0.333104 NaN Median Imputation
127 Neural Networks NaN relu Median Imputation
128 Neural Networks NaN 0.0001 Median Imputation
129 Neural Networks NaN auto Median Imputation
130 Neural Networks NaN 0.9 Median Imputation
131 Neural Networks NaN 0.999 Median Imputation
132 Neural Networks NaN False Median Imputation
133 Neural Networks NaN 0.0 Median Imputation
134 Neural Networks NaN (100,) Median Imputation
135 Neural Networks NaN constant Median Imputation
136 Neural Networks NaN 0.001 Median Imputation
137 Neural Networks NaN 15000 Median Imputation
138 Neural Networks NaN 200 Median Imputation
139 Neural Networks NaN 0.9 Median Imputation
140 Neural Networks NaN 10 Median Imputation
141 Neural Networks NaN True Median Imputation
142 Neural Networks NaN 0.5 Median Imputation
143 Neural Networks NaN None Median Imputation
144 Neural Networks NaN True Median Imputation
145 Neural Networks NaN adam Median Imputation
146 Neural Networks NaN 0.0001 Median Imputation
147 Neural Networks NaN 0.1 Median Imputation
148 Neural Networks NaN False Median Imputation
149 Neural Networks NaN False Median Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
45 Oversampling
46 Oversampling
47 Oversampling
48 Oversampling
49 Oversampling
50 Oversampling
51 Oversampling
52 Oversampling
53 Oversampling
54 Oversampling
55 Oversampling
56 Oversampling
57 Oversampling
58 Oversampling
59 Oversampling
60 Oversampling
61 Oversampling
62 Oversampling
63 Oversampling
64 Oversampling
65 Oversampling
66 Oversampling
67 Oversampling
68 Oversampling
69 Oversampling
70 Oversampling
71 Oversampling
72 Oversampling
73 Oversampling
74 Oversampling
75 Oversampling
76 Oversampling
77 Oversampling
78 Oversampling
79 Oversampling
80 Oversampling
81 Oversampling
82 Oversampling
83 Oversampling
84 Oversampling
85 Oversampling
86 Oversampling
87 Oversampling
88 Oversampling
89 Oversampling
90 Oversampling
91 Oversampling
92 Oversampling
93 Oversampling
94 Oversampling
95 Oversampling
96 Oversampling
97 Oversampling
98 Oversampling
99 Oversampling
100 Oversampling
101 Oversampling
102 Oversampling
103 Oversampling
104 Oversampling
105 Oversampling
106 Oversampling
107 Oversampling
108 Oversampling
109 Oversampling
110 Oversampling
111 Oversampling
112 Oversampling
113 Oversampling
114 Oversampling
115 Oversampling
116 Oversampling
117 Oversampling
118 Oversampling
119 Oversampling
120 Oversampling
121 Oversampling
122 Oversampling
123 Oversampling
124 Oversampling
125 Oversampling
126 Oversampling
127 Oversampling
128 Oversampling
129 Oversampling
130 Oversampling
131 Oversampling
132 Oversampling
133 Oversampling
134 Oversampling
135 Oversampling
136 Oversampling
137 Oversampling
138 Oversampling
139 Oversampling
140 Oversampling
141 Oversampling
142 Oversampling
143 Oversampling
144 Oversampling
145 Oversampling
146 Oversampling
147 Oversampling
148 Oversampling
149 Oversampling
Model Unique Code
0 Neural Networks_Oversampling_Median Imputation...
1 Neural Networks_Oversampling_Median Imputation...
2 Neural Networks_Oversampling_Median Imputation...
3 Neural Networks_Oversampling_Median Imputation...
4 Neural Networks_Oversampling_Median Imputation...
5 Neural Networks_Oversampling_Median Imputation...
6 Neural Networks_Oversampling_Median Imputation...
7 Neural Networks_Oversampling_Median Imputation...
8 Neural Networks_Oversampling_Median Imputation...
9 Neural Networks_Oversampling_Median Imputation...
10 Neural Networks_Oversampling_Median Imputation...
11 Neural Networks_Oversampling_Median Imputation...
12 Neural Networks_Oversampling_Median Imputation...
13 Neural Networks_Oversampling_Median Imputation...
14 Neural Networks_Oversampling_Median Imputation...
15 Neural Networks_Oversampling_Median Imputation...
16 Neural Networks_Oversampling_Median Imputation...
17 Neural Networks_Oversampling_Median Imputation...
18 Neural Networks_Oversampling_Median Imputation...
19 Neural Networks_Oversampling_Median Imputation...
20 Neural Networks_Oversampling_Median Imputation...
21 Neural Networks_Oversampling_Median Imputation...
22 Neural Networks_Oversampling_Median Imputation...
23 Neural Networks_Oversampling_Median Imputation...
24 Neural Networks_Oversampling_Median Imputation...
25 Neural Networks_Oversampling_Median Imputation...
26 Neural Networks_Oversampling_Median Imputation...
27 Neural Networks_Oversampling_Median Imputation...
28 Neural Networks_Oversampling_Median Imputation...
29 Neural Networks_Oversampling_Median Imputation...
30 Neural Networks_Oversampling_Median Imputation...
31 Neural Networks_Oversampling_Median Imputation...
32 Neural Networks_Oversampling_Median Imputation...
33 Neural Networks_Oversampling_Median Imputation...
34 Neural Networks_Oversampling_Median Imputation...
35 Neural Networks_Oversampling_Median Imputation...
36 Neural Networks_Oversampling_Median Imputation...
37 Neural Networks_Oversampling_Median Imputation...
38 Neural Networks_Oversampling_Median Imputation...
39 Neural Networks_Oversampling_Median Imputation...
40 Neural Networks_Oversampling_Median Imputation...
41 Neural Networks_Oversampling_Median Imputation...
42 Neural Networks_Oversampling_Median Imputation...
43 Neural Networks_Oversampling_Median Imputation...
44 Neural Networks_Oversampling_Median Imputation...
45 Neural Networks_Oversampling_Median Imputation...
46 Neural Networks_Oversampling_Median Imputation...
47 Neural Networks_Oversampling_Median Imputation...
48 Neural Networks_Oversampling_Median Imputation...
49 Neural Networks_Oversampling_Median Imputation...
50 Neural Networks_Oversampling_Median Imputation...
51 Neural Networks_Oversampling_Median Imputation...
52 Neural Networks_Oversampling_Median Imputation...
53 Neural Networks_Oversampling_Median Imputation...
54 Neural Networks_Oversampling_Median Imputation...
55 Neural Networks_Oversampling_Median Imputation...
56 Neural Networks_Oversampling_Median Imputation...
57 Neural Networks_Oversampling_Median Imputation...
58 Neural Networks_Oversampling_Median Imputation...
59 Neural Networks_Oversampling_Median Imputation...
60 Neural Networks_Oversampling_Median Imputation...
61 Neural Networks_Oversampling_Median Imputation...
62 Neural Networks_Oversampling_Median Imputation...
63 Neural Networks_Oversampling_Median Imputation...
64 Neural Networks_Oversampling_Median Imputation...
65 Neural Networks_Oversampling_Median Imputation...
66 Neural Networks_Oversampling_Median Imputation...
67 Neural Networks_Oversampling_Median Imputation...
68 Neural Networks_Oversampling_Median Imputation...
69 Neural Networks_Oversampling_Median Imputation...
70 Neural Networks_Oversampling_Median Imputation...
71 Neural Networks_Oversampling_Median Imputation...
72 Neural Networks_Oversampling_Median Imputation...
73 Neural Networks_Oversampling_Median Imputation...
74 Neural Networks_Oversampling_Median Imputation...
75 Neural Networks_Oversampling_Median Imputation...
76 Neural Networks_Oversampling_Median Imputation...
77 Neural Networks_Oversampling_Median Imputation...
78 Neural Networks_Oversampling_Median Imputation...
79 Neural Networks_Oversampling_Median Imputation...
80 Neural Networks_Oversampling_Median Imputation...
81 Neural Networks_Oversampling_Median Imputation...
82 Neural Networks_Oversampling_Median Imputation...
83 Neural Networks_Oversampling_Median Imputation...
84 Neural Networks_Oversampling_Median Imputation...
85 Neural Networks_Oversampling_Median Imputation...
86 Neural Networks_Oversampling_Median Imputation...
87 Neural Networks_Oversampling_Median Imputation...
88 Neural Networks_Oversampling_Median Imputation...
89 Neural Networks_Oversampling_Median Imputation...
90 Neural Networks_Oversampling_Median Imputation...
91 Neural Networks_Oversampling_Median Imputation...
92 Neural Networks_Oversampling_Median Imputation...
93 Neural Networks_Oversampling_Median Imputation...
94 Neural Networks_Oversampling_Median Imputation...
95 Neural Networks_Oversampling_Median Imputation...
96 Neural Networks_Oversampling_Median Imputation...
97 Neural Networks_Oversampling_Median Imputation...
98 Neural Networks_Oversampling_Median Imputation...
99 Neural Networks_Oversampling_Median Imputation...
100 Neural Networks_Oversampling_Median Imputation...
101 Neural Networks_Oversampling_Median Imputation...
102 Neural Networks_Oversampling_Median Imputation...
103 Neural Networks_Oversampling_Median Imputation...
104 Neural Networks_Oversampling_Median Imputation...
105 Neural Networks_Oversampling_Median Imputation...
106 Neural Networks_Oversampling_Median Imputation...
107 Neural Networks_Oversampling_Median Imputation...
108 Neural Networks_Oversampling_Median Imputation...
109 Neural Networks_Oversampling_Median Imputation...
110 Neural Networks_Oversampling_Median Imputation...
111 Neural Networks_Oversampling_Median Imputation...
112 Neural Networks_Oversampling_Median Imputation...
113 Neural Networks_Oversampling_Median Imputation...
114 Neural Networks_Oversampling_Median Imputation...
115 Neural Networks_Oversampling_Median Imputation...
116 Neural Networks_Oversampling_Median Imputation...
117 Neural Networks_Oversampling_Median Imputation...
118 Neural Networks_Oversampling_Median Imputation...
119 Neural Networks_Oversampling_Median Imputation...
120 Neural Networks_Oversampling_Median Imputation...
121 Neural Networks_Oversampling_Median Imputation...
122 Neural Networks_Oversampling_Median Imputation...
123 Neural Networks_Oversampling_Median Imputation...
124 Neural Networks_Oversampling_Median Imputation...
125 Neural Networks_Oversampling_Median Imputation...
126 Neural Networks_Oversampling_Median Imputation...
127 Neural Networks_Oversampling_Median Imputation...
128 Neural Networks_Oversampling_Median Imputation...
129 Neural Networks_Oversampling_Median Imputation...
130 Neural Networks_Oversampling_Median Imputation...
131 Neural Networks_Oversampling_Median Imputation...
132 Neural Networks_Oversampling_Median Imputation...
133 Neural Networks_Oversampling_Median Imputation...
134 Neural Networks_Oversampling_Median Imputation...
135 Neural Networks_Oversampling_Median Imputation...
136 Neural Networks_Oversampling_Median Imputation...
137 Neural Networks_Oversampling_Median Imputation...
138 Neural Networks_Oversampling_Median Imputation...
139 Neural Networks_Oversampling_Median Imputation...
140 Neural Networks_Oversampling_Median Imputation...
141 Neural Networks_Oversampling_Median Imputation...
142 Neural Networks_Oversampling_Median Imputation...
143 Neural Networks_Oversampling_Median Imputation...
144 Neural Networks_Oversampling_Median Imputation...
145 Neural Networks_Oversampling_Median Imputation...
146 Neural Networks_Oversampling_Median Imputation...
147 Neural Networks_Oversampling_Median Imputation...
148 Neural Networks_Oversampling_Median Imputation...
149 Neural Networks_Oversampling_Median Imputation... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Neural Networks 0.481169 NaN Zero Imputation
1 Neural Networks 0.082007 NaN Zero Imputation
2 Neural Networks 0.717300 NaN Zero Imputation
3 Neural Networks 0.147186 NaN Zero Imputation
4 Neural Networks 0.583420 NaN Zero Imputation
5 Neural Networks 0.206691 NaN Zero Imputation
6 Neural Networks 0.166841 NaN Zero Imputation
7 Neural Networks NaN relu Zero Imputation
8 Neural Networks NaN 0.0001 Zero Imputation
9 Neural Networks NaN auto Zero Imputation
10 Neural Networks NaN 0.9 Zero Imputation
11 Neural Networks NaN 0.999 Zero Imputation
12 Neural Networks NaN False Zero Imputation
13 Neural Networks NaN 0.0 Zero Imputation
14 Neural Networks NaN (100,) Zero Imputation
15 Neural Networks NaN constant Zero Imputation
16 Neural Networks NaN 0.001 Zero Imputation
17 Neural Networks NaN 15000 Zero Imputation
18 Neural Networks NaN 200 Zero Imputation
19 Neural Networks NaN 0.9 Zero Imputation
20 Neural Networks NaN 10 Zero Imputation
21 Neural Networks NaN True Zero Imputation
22 Neural Networks NaN 0.5 Zero Imputation
23 Neural Networks NaN None Zero Imputation
24 Neural Networks NaN True Zero Imputation
25 Neural Networks NaN adam Zero Imputation
26 Neural Networks NaN 0.0001 Zero Imputation
27 Neural Networks NaN 0.1 Zero Imputation
28 Neural Networks NaN False Zero Imputation
29 Neural Networks NaN False Zero Imputation
30 Neural Networks 0.755333 NaN Zero Imputation
31 Neural Networks 0.083787 NaN Zero Imputation
32 Neural Networks 0.469512 NaN Zero Imputation
33 Neural Networks 0.142198 NaN Zero Imputation
34 Neural Networks 0.659736 NaN Zero Imputation
35 Neural Networks 0.295882 NaN Zero Imputation
36 Neural Networks 0.319472 NaN Zero Imputation
37 Neural Networks NaN relu Zero Imputation
38 Neural Networks NaN 0.0001 Zero Imputation
39 Neural Networks NaN auto Zero Imputation
40 Neural Networks NaN 0.9 Zero Imputation
41 Neural Networks NaN 0.999 Zero Imputation
42 Neural Networks NaN False Zero Imputation
43 Neural Networks NaN 0.0 Zero Imputation
44 Neural Networks NaN (100,) Zero Imputation
45 Neural Networks NaN constant Zero Imputation
46 Neural Networks NaN 0.001 Zero Imputation
47 Neural Networks NaN 15000 Zero Imputation
48 Neural Networks NaN 200 Zero Imputation
49 Neural Networks NaN 0.9 Zero Imputation
50 Neural Networks NaN 10 Zero Imputation
51 Neural Networks NaN True Zero Imputation
52 Neural Networks NaN 0.5 Zero Imputation
53 Neural Networks NaN None Zero Imputation
54 Neural Networks NaN True Zero Imputation
55 Neural Networks NaN adam Zero Imputation
56 Neural Networks NaN 0.0001 Zero Imputation
57 Neural Networks NaN 0.1 Zero Imputation
58 Neural Networks NaN False Zero Imputation
59 Neural Networks NaN False Zero Imputation
60 Neural Networks 0.402686 NaN Zero Imputation
61 Neural Networks 0.057799 NaN Zero Imputation
62 Neural Networks 0.727273 NaN Zero Imputation
63 Neural Networks 0.107087 NaN Zero Imputation
64 Neural Networks 0.526794 NaN Zero Imputation
65 Neural Networks 0.137846 NaN Zero Imputation
66 Neural Networks 0.053589 NaN Zero Imputation
67 Neural Networks NaN relu Zero Imputation
68 Neural Networks NaN 0.0001 Zero Imputation
69 Neural Networks NaN auto Zero Imputation
70 Neural Networks NaN 0.9 Zero Imputation
71 Neural Networks NaN 0.999 Zero Imputation
72 Neural Networks NaN False Zero Imputation
73 Neural Networks NaN 0.0 Zero Imputation
74 Neural Networks NaN (100,) Zero Imputation
75 Neural Networks NaN constant Zero Imputation
76 Neural Networks NaN 0.001 Zero Imputation
77 Neural Networks NaN 15000 Zero Imputation
78 Neural Networks NaN 200 Zero Imputation
79 Neural Networks NaN 0.9 Zero Imputation
80 Neural Networks NaN 10 Zero Imputation
81 Neural Networks NaN True Zero Imputation
82 Neural Networks NaN 0.5 Zero Imputation
83 Neural Networks NaN None Zero Imputation
84 Neural Networks NaN True Zero Imputation
85 Neural Networks NaN adam Zero Imputation
86 Neural Networks NaN 0.0001 Zero Imputation
87 Neural Networks NaN 0.1 Zero Imputation
88 Neural Networks NaN False Zero Imputation
89 Neural Networks NaN False Zero Imputation
90 Neural Networks 0.183614 NaN Zero Imputation
91 Neural Networks 0.055436 NaN Zero Imputation
92 Neural Networks 0.923469 NaN Zero Imputation
93 Neural Networks 0.104594 NaN Zero Imputation
94 Neural Networks 0.474583 NaN Zero Imputation
95 Neural Networks 0.077885 NaN Zero Imputation
96 Neural Networks -0.050833 NaN Zero Imputation
97 Neural Networks NaN relu Zero Imputation
98 Neural Networks NaN 0.0001 Zero Imputation
99 Neural Networks NaN auto Zero Imputation
100 Neural Networks NaN 0.9 Zero Imputation
101 Neural Networks NaN 0.999 Zero Imputation
102 Neural Networks NaN False Zero Imputation
103 Neural Networks NaN 0.0 Zero Imputation
104 Neural Networks NaN (100,) Zero Imputation
105 Neural Networks NaN constant Zero Imputation
106 Neural Networks NaN 0.001 Zero Imputation
107 Neural Networks NaN 15000 Zero Imputation
108 Neural Networks NaN 200 Zero Imputation
109 Neural Networks NaN 0.9 Zero Imputation
110 Neural Networks NaN 10 Zero Imputation
111 Neural Networks NaN True Zero Imputation
112 Neural Networks NaN 0.5 Zero Imputation
113 Neural Networks NaN None Zero Imputation
114 Neural Networks NaN True Zero Imputation
115 Neural Networks NaN adam Zero Imputation
116 Neural Networks NaN 0.0001 Zero Imputation
117 Neural Networks NaN 0.1 Zero Imputation
118 Neural Networks NaN False Zero Imputation
119 Neural Networks NaN False Zero Imputation
120 Neural Networks 0.556375 NaN Zero Imputation
121 Neural Networks 0.086246 NaN Zero Imputation
122 Neural Networks 0.708333 NaN Zero Imputation
123 Neural Networks 0.153769 NaN Zero Imputation
124 Neural Networks 0.622655 NaN Zero Imputation
125 Neural Networks 0.268979 NaN Zero Imputation
126 Neural Networks 0.245310 NaN Zero Imputation
127 Neural Networks NaN relu Zero Imputation
128 Neural Networks NaN 0.0001 Zero Imputation
129 Neural Networks NaN auto Zero Imputation
130 Neural Networks NaN 0.9 Zero Imputation
131 Neural Networks NaN 0.999 Zero Imputation
132 Neural Networks NaN False Zero Imputation
133 Neural Networks NaN 0.0 Zero Imputation
134 Neural Networks NaN (100,) Zero Imputation
135 Neural Networks NaN constant Zero Imputation
136 Neural Networks NaN 0.001 Zero Imputation
137 Neural Networks NaN 15000 Zero Imputation
138 Neural Networks NaN 200 Zero Imputation
139 Neural Networks NaN 0.9 Zero Imputation
140 Neural Networks NaN 10 Zero Imputation
141 Neural Networks NaN True Zero Imputation
142 Neural Networks NaN 0.5 Zero Imputation
143 Neural Networks NaN None Zero Imputation
144 Neural Networks NaN True Zero Imputation
145 Neural Networks NaN adam Zero Imputation
146 Neural Networks NaN 0.0001 Zero Imputation
147 Neural Networks NaN 0.1 Zero Imputation
148 Neural Networks NaN False Zero Imputation
149 Neural Networks NaN False Zero Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
45 Undersampling
46 Undersampling
47 Undersampling
48 Undersampling
49 Undersampling
50 Undersampling
51 Undersampling
52 Undersampling
53 Undersampling
54 Undersampling
55 Undersampling
56 Undersampling
57 Undersampling
58 Undersampling
59 Undersampling
60 Undersampling
61 Undersampling
62 Undersampling
63 Undersampling
64 Undersampling
65 Undersampling
66 Undersampling
67 Undersampling
68 Undersampling
69 Undersampling
70 Undersampling
71 Undersampling
72 Undersampling
73 Undersampling
74 Undersampling
75 Undersampling
76 Undersampling
77 Undersampling
78 Undersampling
79 Undersampling
80 Undersampling
81 Undersampling
82 Undersampling
83 Undersampling
84 Undersampling
85 Undersampling
86 Undersampling
87 Undersampling
88 Undersampling
89 Undersampling
90 Undersampling
91 Undersampling
92 Undersampling
93 Undersampling
94 Undersampling
95 Undersampling
96 Undersampling
97 Undersampling
98 Undersampling
99 Undersampling
100 Undersampling
101 Undersampling
102 Undersampling
103 Undersampling
104 Undersampling
105 Undersampling
106 Undersampling
107 Undersampling
108 Undersampling
109 Undersampling
110 Undersampling
111 Undersampling
112 Undersampling
113 Undersampling
114 Undersampling
115 Undersampling
116 Undersampling
117 Undersampling
118 Undersampling
119 Undersampling
120 Undersampling
121 Undersampling
122 Undersampling
123 Undersampling
124 Undersampling
125 Undersampling
126 Undersampling
127 Undersampling
128 Undersampling
129 Undersampling
130 Undersampling
131 Undersampling
132 Undersampling
133 Undersampling
134 Undersampling
135 Undersampling
136 Undersampling
137 Undersampling
138 Undersampling
139 Undersampling
140 Undersampling
141 Undersampling
142 Undersampling
143 Undersampling
144 Undersampling
145 Undersampling
146 Undersampling
147 Undersampling
148 Undersampling
149 Undersampling
Model Unique Code
0 Neural Networks_Undersampling_Zero Imputation_...
1 Neural Networks_Undersampling_Zero Imputation_...
2 Neural Networks_Undersampling_Zero Imputation_...
3 Neural Networks_Undersampling_Zero Imputation_...
4 Neural Networks_Undersampling_Zero Imputation_...
5 Neural Networks_Undersampling_Zero Imputation_...
6 Neural Networks_Undersampling_Zero Imputation_...
7 Neural Networks_Undersampling_Zero Imputation_...
8 Neural Networks_Undersampling_Zero Imputation_...
9 Neural Networks_Undersampling_Zero Imputation_...
10 Neural Networks_Undersampling_Zero Imputation_...
11 Neural Networks_Undersampling_Zero Imputation_...
12 Neural Networks_Undersampling_Zero Imputation_...
13 Neural Networks_Undersampling_Zero Imputation_...
14 Neural Networks_Undersampling_Zero Imputation_...
15 Neural Networks_Undersampling_Zero Imputation_...
16 Neural Networks_Undersampling_Zero Imputation_...
17 Neural Networks_Undersampling_Zero Imputation_...
18 Neural Networks_Undersampling_Zero Imputation_...
19 Neural Networks_Undersampling_Zero Imputation_...
20 Neural Networks_Undersampling_Zero Imputation_...
21 Neural Networks_Undersampling_Zero Imputation_...
22 Neural Networks_Undersampling_Zero Imputation_...
23 Neural Networks_Undersampling_Zero Imputation_...
24 Neural Networks_Undersampling_Zero Imputation_...
25 Neural Networks_Undersampling_Zero Imputation_...
26 Neural Networks_Undersampling_Zero Imputation_...
27 Neural Networks_Undersampling_Zero Imputation_...
28 Neural Networks_Undersampling_Zero Imputation_...
29 Neural Networks_Undersampling_Zero Imputation_...
30 Neural Networks_Undersampling_Zero Imputation_...
31 Neural Networks_Undersampling_Zero Imputation_...
32 Neural Networks_Undersampling_Zero Imputation_...
33 Neural Networks_Undersampling_Zero Imputation_...
34 Neural Networks_Undersampling_Zero Imputation_...
35 Neural Networks_Undersampling_Zero Imputation_...
36 Neural Networks_Undersampling_Zero Imputation_...
37 Neural Networks_Undersampling_Zero Imputation_...
38 Neural Networks_Undersampling_Zero Imputation_...
39 Neural Networks_Undersampling_Zero Imputation_...
40 Neural Networks_Undersampling_Zero Imputation_...
41 Neural Networks_Undersampling_Zero Imputation_...
42 Neural Networks_Undersampling_Zero Imputation_...
43 Neural Networks_Undersampling_Zero Imputation_...
44 Neural Networks_Undersampling_Zero Imputation_...
45 Neural Networks_Undersampling_Zero Imputation_...
46 Neural Networks_Undersampling_Zero Imputation_...
47 Neural Networks_Undersampling_Zero Imputation_...
48 Neural Networks_Undersampling_Zero Imputation_...
49 Neural Networks_Undersampling_Zero Imputation_...
50 Neural Networks_Undersampling_Zero Imputation_...
51 Neural Networks_Undersampling_Zero Imputation_...
52 Neural Networks_Undersampling_Zero Imputation_...
53 Neural Networks_Undersampling_Zero Imputation_...
54 Neural Networks_Undersampling_Zero Imputation_...
55 Neural Networks_Undersampling_Zero Imputation_...
56 Neural Networks_Undersampling_Zero Imputation_...
57 Neural Networks_Undersampling_Zero Imputation_...
58 Neural Networks_Undersampling_Zero Imputation_...
59 Neural Networks_Undersampling_Zero Imputation_...
60 Neural Networks_Undersampling_Zero Imputation_...
61 Neural Networks_Undersampling_Zero Imputation_...
62 Neural Networks_Undersampling_Zero Imputation_...
63 Neural Networks_Undersampling_Zero Imputation_...
64 Neural Networks_Undersampling_Zero Imputation_...
65 Neural Networks_Undersampling_Zero Imputation_...
66 Neural Networks_Undersampling_Zero Imputation_...
67 Neural Networks_Undersampling_Zero Imputation_...
68 Neural Networks_Undersampling_Zero Imputation_...
69 Neural Networks_Undersampling_Zero Imputation_...
70 Neural Networks_Undersampling_Zero Imputation_...
71 Neural Networks_Undersampling_Zero Imputation_...
72 Neural Networks_Undersampling_Zero Imputation_...
73 Neural Networks_Undersampling_Zero Imputation_...
74 Neural Networks_Undersampling_Zero Imputation_...
75 Neural Networks_Undersampling_Zero Imputation_...
76 Neural Networks_Undersampling_Zero Imputation_...
77 Neural Networks_Undersampling_Zero Imputation_...
78 Neural Networks_Undersampling_Zero Imputation_...
79 Neural Networks_Undersampling_Zero Imputation_...
80 Neural Networks_Undersampling_Zero Imputation_...
81 Neural Networks_Undersampling_Zero Imputation_...
82 Neural Networks_Undersampling_Zero Imputation_...
83 Neural Networks_Undersampling_Zero Imputation_...
84 Neural Networks_Undersampling_Zero Imputation_...
85 Neural Networks_Undersampling_Zero Imputation_...
86 Neural Networks_Undersampling_Zero Imputation_...
87 Neural Networks_Undersampling_Zero Imputation_...
88 Neural Networks_Undersampling_Zero Imputation_...
89 Neural Networks_Undersampling_Zero Imputation_...
90 Neural Networks_Undersampling_Zero Imputation_...
91 Neural Networks_Undersampling_Zero Imputation_...
92 Neural Networks_Undersampling_Zero Imputation_...
93 Neural Networks_Undersampling_Zero Imputation_...
94 Neural Networks_Undersampling_Zero Imputation_...
95 Neural Networks_Undersampling_Zero Imputation_...
96 Neural Networks_Undersampling_Zero Imputation_...
97 Neural Networks_Undersampling_Zero Imputation_...
98 Neural Networks_Undersampling_Zero Imputation_...
99 Neural Networks_Undersampling_Zero Imputation_...
100 Neural Networks_Undersampling_Zero Imputation_...
101 Neural Networks_Undersampling_Zero Imputation_...
102 Neural Networks_Undersampling_Zero Imputation_...
103 Neural Networks_Undersampling_Zero Imputation_...
104 Neural Networks_Undersampling_Zero Imputation_...
105 Neural Networks_Undersampling_Zero Imputation_...
106 Neural Networks_Undersampling_Zero Imputation_...
107 Neural Networks_Undersampling_Zero Imputation_...
108 Neural Networks_Undersampling_Zero Imputation_...
109 Neural Networks_Undersampling_Zero Imputation_...
110 Neural Networks_Undersampling_Zero Imputation_...
111 Neural Networks_Undersampling_Zero Imputation_...
112 Neural Networks_Undersampling_Zero Imputation_...
113 Neural Networks_Undersampling_Zero Imputation_...
114 Neural Networks_Undersampling_Zero Imputation_...
115 Neural Networks_Undersampling_Zero Imputation_...
116 Neural Networks_Undersampling_Zero Imputation_...
117 Neural Networks_Undersampling_Zero Imputation_...
118 Neural Networks_Undersampling_Zero Imputation_...
119 Neural Networks_Undersampling_Zero Imputation_...
120 Neural Networks_Undersampling_Zero Imputation_...
121 Neural Networks_Undersampling_Zero Imputation_...
122 Neural Networks_Undersampling_Zero Imputation_...
123 Neural Networks_Undersampling_Zero Imputation_...
124 Neural Networks_Undersampling_Zero Imputation_...
125 Neural Networks_Undersampling_Zero Imputation_...
126 Neural Networks_Undersampling_Zero Imputation_...
127 Neural Networks_Undersampling_Zero Imputation_...
128 Neural Networks_Undersampling_Zero Imputation_...
129 Neural Networks_Undersampling_Zero Imputation_...
130 Neural Networks_Undersampling_Zero Imputation_...
131 Neural Networks_Undersampling_Zero Imputation_...
132 Neural Networks_Undersampling_Zero Imputation_...
133 Neural Networks_Undersampling_Zero Imputation_...
134 Neural Networks_Undersampling_Zero Imputation_...
135 Neural Networks_Undersampling_Zero Imputation_...
136 Neural Networks_Undersampling_Zero Imputation_...
137 Neural Networks_Undersampling_Zero Imputation_...
138 Neural Networks_Undersampling_Zero Imputation_...
139 Neural Networks_Undersampling_Zero Imputation_...
140 Neural Networks_Undersampling_Zero Imputation_...
141 Neural Networks_Undersampling_Zero Imputation_...
142 Neural Networks_Undersampling_Zero Imputation_...
143 Neural Networks_Undersampling_Zero Imputation_...
144 Neural Networks_Undersampling_Zero Imputation_...
145 Neural Networks_Undersampling_Zero Imputation_...
146 Neural Networks_Undersampling_Zero Imputation_...
147 Neural Networks_Undersampling_Zero Imputation_...
148 Neural Networks_Undersampling_Zero Imputation_...
149 Neural Networks_Undersampling_Zero Imputation_... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Neural Networks 0.819068 NaN Mean Imputation
1 Neural Networks 0.092391 NaN Mean Imputation
2 Neural Networks 0.215190 NaN Mean Imputation
3 Neural Networks 0.129278 NaN Mean Imputation
4 Neural Networks 0.635950 NaN Mean Imputation
5 Neural Networks 0.256894 NaN Mean Imputation
6 Neural Networks 0.271901 NaN Mean Imputation
7 Neural Networks NaN relu Mean Imputation
8 Neural Networks NaN 0.0001 Mean Imputation
9 Neural Networks NaN auto Mean Imputation
10 Neural Networks NaN 0.9 Mean Imputation
11 Neural Networks NaN 0.999 Mean Imputation
12 Neural Networks NaN False Mean Imputation
13 Neural Networks NaN 0.0 Mean Imputation
14 Neural Networks NaN (100,) Mean Imputation
15 Neural Networks NaN constant Mean Imputation
16 Neural Networks NaN 0.001 Mean Imputation
17 Neural Networks NaN 15000 Mean Imputation
18 Neural Networks NaN 200 Mean Imputation
19 Neural Networks NaN 0.9 Mean Imputation
20 Neural Networks NaN 10 Mean Imputation
21 Neural Networks NaN True Mean Imputation
22 Neural Networks NaN 0.5 Mean Imputation
23 Neural Networks NaN None Mean Imputation
24 Neural Networks NaN True Mean Imputation
25 Neural Networks NaN adam Mean Imputation
26 Neural Networks NaN 0.0001 Mean Imputation
27 Neural Networks NaN 0.1 Mean Imputation
28 Neural Networks NaN False Mean Imputation
29 Neural Networks NaN False Mean Imputation
30 Neural Networks 0.391625 NaN Mean Imputation
31 Neural Networks 0.046108 NaN Mean Imputation
32 Neural Networks 0.664634 NaN Mean Imputation
33 Neural Networks 0.086234 NaN Mean Imputation
34 Neural Networks 0.537689 NaN Mean Imputation
35 Neural Networks 0.071306 NaN Mean Imputation
36 Neural Networks 0.075378 NaN Mean Imputation
37 Neural Networks NaN relu Mean Imputation
38 Neural Networks NaN 0.0001 Mean Imputation
39 Neural Networks NaN auto Mean Imputation
40 Neural Networks NaN 0.9 Mean Imputation
41 Neural Networks NaN 0.999 Mean Imputation
42 Neural Networks NaN False Mean Imputation
43 Neural Networks NaN 0.0 Mean Imputation
44 Neural Networks NaN (100,) Mean Imputation
45 Neural Networks NaN constant Mean Imputation
46 Neural Networks NaN 0.001 Mean Imputation
47 Neural Networks NaN 15000 Mean Imputation
48 Neural Networks NaN 200 Mean Imputation
49 Neural Networks NaN 0.9 Mean Imputation
50 Neural Networks NaN 10 Mean Imputation
51 Neural Networks NaN True Mean Imputation
52 Neural Networks NaN 0.5 Mean Imputation
53 Neural Networks NaN None Mean Imputation
54 Neural Networks NaN True Mean Imputation
55 Neural Networks NaN adam Mean Imputation
56 Neural Networks NaN 0.0001 Mean Imputation
57 Neural Networks NaN 0.1 Mean Imputation
58 Neural Networks NaN False Mean Imputation
59 Neural Networks NaN False Mean Imputation
60 Neural Networks 0.763761 NaN Mean Imputation
61 Neural Networks 0.064951 NaN Mean Imputation
62 Neural Networks 0.283422 NaN Mean Imputation
63 Neural Networks 0.105683 NaN Mean Imputation
64 Neural Networks 0.622312 NaN Mean Imputation
65 Neural Networks 0.267740 NaN Mean Imputation
66 Neural Networks 0.244624 NaN Mean Imputation
67 Neural Networks NaN relu Mean Imputation
68 Neural Networks NaN 0.0001 Mean Imputation
69 Neural Networks NaN auto Mean Imputation
70 Neural Networks NaN 0.9 Mean Imputation
71 Neural Networks NaN 0.999 Mean Imputation
72 Neural Networks NaN False Mean Imputation
73 Neural Networks NaN 0.0 Mean Imputation
74 Neural Networks NaN (100,) Mean Imputation
75 Neural Networks NaN constant Mean Imputation
76 Neural Networks NaN 0.001 Mean Imputation
77 Neural Networks NaN 15000 Mean Imputation
78 Neural Networks NaN 200 Mean Imputation
79 Neural Networks NaN 0.9 Mean Imputation
80 Neural Networks NaN 10 Mean Imputation
81 Neural Networks NaN True Mean Imputation
82 Neural Networks NaN 0.5 Mean Imputation
83 Neural Networks NaN None Mean Imputation
84 Neural Networks NaN True Mean Imputation
85 Neural Networks NaN adam Mean Imputation
86 Neural Networks NaN 0.0001 Mean Imputation
87 Neural Networks NaN 0.1 Mean Imputation
88 Neural Networks NaN False Mean Imputation
89 Neural Networks NaN False Mean Imputation
90 Neural Networks 0.728135 NaN Mean Imputation
91 Neural Networks 0.060000 NaN Mean Imputation
92 Neural Networks 0.290816 NaN Mean Imputation
93 Neural Networks 0.099476 NaN Mean Imputation
94 Neural Networks 0.589539 NaN Mean Imputation
95 Neural Networks 0.199240 NaN Mean Imputation
96 Neural Networks 0.179079 NaN Mean Imputation
97 Neural Networks NaN relu Mean Imputation
98 Neural Networks NaN 0.0001 Mean Imputation
99 Neural Networks NaN auto Mean Imputation
100 Neural Networks NaN 0.9 Mean Imputation
101 Neural Networks NaN 0.999 Mean Imputation
102 Neural Networks NaN False Mean Imputation
103 Neural Networks NaN 0.0 Mean Imputation
104 Neural Networks NaN (100,) Mean Imputation
105 Neural Networks NaN constant Mean Imputation
106 Neural Networks NaN 0.001 Mean Imputation
107 Neural Networks NaN 15000 Mean Imputation
108 Neural Networks NaN 200 Mean Imputation
109 Neural Networks NaN 0.9 Mean Imputation
110 Neural Networks NaN 10 Mean Imputation
111 Neural Networks NaN True Mean Imputation
112 Neural Networks NaN 0.5 Mean Imputation
113 Neural Networks NaN None Mean Imputation
114 Neural Networks NaN True Mean Imputation
115 Neural Networks NaN adam Mean Imputation
116 Neural Networks NaN 0.0001 Mean Imputation
117 Neural Networks NaN 0.1 Mean Imputation
118 Neural Networks NaN False Mean Imputation
119 Neural Networks NaN False Mean Imputation
120 Neural Networks 0.265016 NaN Mean Imputation
121 Neural Networks 0.064024 NaN Mean Imputation
122 Neural Networks 0.875000 NaN Mean Imputation
123 Neural Networks 0.119318 NaN Mean Imputation
124 Neural Networks 0.559148 NaN Mean Imputation
125 Neural Networks 0.110227 NaN Mean Imputation
126 Neural Networks 0.118296 NaN Mean Imputation
127 Neural Networks NaN relu Mean Imputation
128 Neural Networks NaN 0.0001 Mean Imputation
129 Neural Networks NaN auto Mean Imputation
130 Neural Networks NaN 0.9 Mean Imputation
131 Neural Networks NaN 0.999 Mean Imputation
132 Neural Networks NaN False Mean Imputation
133 Neural Networks NaN 0.0 Mean Imputation
134 Neural Networks NaN (100,) Mean Imputation
135 Neural Networks NaN constant Mean Imputation
136 Neural Networks NaN 0.001 Mean Imputation
137 Neural Networks NaN 15000 Mean Imputation
138 Neural Networks NaN 200 Mean Imputation
139 Neural Networks NaN 0.9 Mean Imputation
140 Neural Networks NaN 10 Mean Imputation
141 Neural Networks NaN True Mean Imputation
142 Neural Networks NaN 0.5 Mean Imputation
143 Neural Networks NaN None Mean Imputation
144 Neural Networks NaN True Mean Imputation
145 Neural Networks NaN adam Mean Imputation
146 Neural Networks NaN 0.0001 Mean Imputation
147 Neural Networks NaN 0.1 Mean Imputation
148 Neural Networks NaN False Mean Imputation
149 Neural Networks NaN False Mean Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
45 Undersampling
46 Undersampling
47 Undersampling
48 Undersampling
49 Undersampling
50 Undersampling
51 Undersampling
52 Undersampling
53 Undersampling
54 Undersampling
55 Undersampling
56 Undersampling
57 Undersampling
58 Undersampling
59 Undersampling
60 Undersampling
61 Undersampling
62 Undersampling
63 Undersampling
64 Undersampling
65 Undersampling
66 Undersampling
67 Undersampling
68 Undersampling
69 Undersampling
70 Undersampling
71 Undersampling
72 Undersampling
73 Undersampling
74 Undersampling
75 Undersampling
76 Undersampling
77 Undersampling
78 Undersampling
79 Undersampling
80 Undersampling
81 Undersampling
82 Undersampling
83 Undersampling
84 Undersampling
85 Undersampling
86 Undersampling
87 Undersampling
88 Undersampling
89 Undersampling
90 Undersampling
91 Undersampling
92 Undersampling
93 Undersampling
94 Undersampling
95 Undersampling
96 Undersampling
97 Undersampling
98 Undersampling
99 Undersampling
100 Undersampling
101 Undersampling
102 Undersampling
103 Undersampling
104 Undersampling
105 Undersampling
106 Undersampling
107 Undersampling
108 Undersampling
109 Undersampling
110 Undersampling
111 Undersampling
112 Undersampling
113 Undersampling
114 Undersampling
115 Undersampling
116 Undersampling
117 Undersampling
118 Undersampling
119 Undersampling
120 Undersampling
121 Undersampling
122 Undersampling
123 Undersampling
124 Undersampling
125 Undersampling
126 Undersampling
127 Undersampling
128 Undersampling
129 Undersampling
130 Undersampling
131 Undersampling
132 Undersampling
133 Undersampling
134 Undersampling
135 Undersampling
136 Undersampling
137 Undersampling
138 Undersampling
139 Undersampling
140 Undersampling
141 Undersampling
142 Undersampling
143 Undersampling
144 Undersampling
145 Undersampling
146 Undersampling
147 Undersampling
148 Undersampling
149 Undersampling
Model Unique Code
0 Neural Networks_Undersampling_Mean Imputation_...
1 Neural Networks_Undersampling_Mean Imputation_...
2 Neural Networks_Undersampling_Mean Imputation_...
3 Neural Networks_Undersampling_Mean Imputation_...
4 Neural Networks_Undersampling_Mean Imputation_...
5 Neural Networks_Undersampling_Mean Imputation_...
6 Neural Networks_Undersampling_Mean Imputation_...
7 Neural Networks_Undersampling_Mean Imputation_...
8 Neural Networks_Undersampling_Mean Imputation_...
9 Neural Networks_Undersampling_Mean Imputation_...
10 Neural Networks_Undersampling_Mean Imputation_...
11 Neural Networks_Undersampling_Mean Imputation_...
12 Neural Networks_Undersampling_Mean Imputation_...
13 Neural Networks_Undersampling_Mean Imputation_...
14 Neural Networks_Undersampling_Mean Imputation_...
15 Neural Networks_Undersampling_Mean Imputation_...
16 Neural Networks_Undersampling_Mean Imputation_...
17 Neural Networks_Undersampling_Mean Imputation_...
18 Neural Networks_Undersampling_Mean Imputation_...
19 Neural Networks_Undersampling_Mean Imputation_...
20 Neural Networks_Undersampling_Mean Imputation_...
21 Neural Networks_Undersampling_Mean Imputation_...
22 Neural Networks_Undersampling_Mean Imputation_...
23 Neural Networks_Undersampling_Mean Imputation_...
24 Neural Networks_Undersampling_Mean Imputation_...
25 Neural Networks_Undersampling_Mean Imputation_...
26 Neural Networks_Undersampling_Mean Imputation_...
27 Neural Networks_Undersampling_Mean Imputation_...
28 Neural Networks_Undersampling_Mean Imputation_...
29 Neural Networks_Undersampling_Mean Imputation_...
30 Neural Networks_Undersampling_Mean Imputation_...
31 Neural Networks_Undersampling_Mean Imputation_...
32 Neural Networks_Undersampling_Mean Imputation_...
33 Neural Networks_Undersampling_Mean Imputation_...
34 Neural Networks_Undersampling_Mean Imputation_...
35 Neural Networks_Undersampling_Mean Imputation_...
36 Neural Networks_Undersampling_Mean Imputation_...
37 Neural Networks_Undersampling_Mean Imputation_...
38 Neural Networks_Undersampling_Mean Imputation_...
39 Neural Networks_Undersampling_Mean Imputation_...
40 Neural Networks_Undersampling_Mean Imputation_...
41 Neural Networks_Undersampling_Mean Imputation_...
42 Neural Networks_Undersampling_Mean Imputation_...
43 Neural Networks_Undersampling_Mean Imputation_...
44 Neural Networks_Undersampling_Mean Imputation_...
45 Neural Networks_Undersampling_Mean Imputation_...
46 Neural Networks_Undersampling_Mean Imputation_...
47 Neural Networks_Undersampling_Mean Imputation_...
48 Neural Networks_Undersampling_Mean Imputation_...
49 Neural Networks_Undersampling_Mean Imputation_...
50 Neural Networks_Undersampling_Mean Imputation_...
51 Neural Networks_Undersampling_Mean Imputation_...
52 Neural Networks_Undersampling_Mean Imputation_...
53 Neural Networks_Undersampling_Mean Imputation_...
54 Neural Networks_Undersampling_Mean Imputation_...
55 Neural Networks_Undersampling_Mean Imputation_...
56 Neural Networks_Undersampling_Mean Imputation_...
57 Neural Networks_Undersampling_Mean Imputation_...
58 Neural Networks_Undersampling_Mean Imputation_...
59 Neural Networks_Undersampling_Mean Imputation_...
60 Neural Networks_Undersampling_Mean Imputation_...
61 Neural Networks_Undersampling_Mean Imputation_...
62 Neural Networks_Undersampling_Mean Imputation_...
63 Neural Networks_Undersampling_Mean Imputation_...
64 Neural Networks_Undersampling_Mean Imputation_...
65 Neural Networks_Undersampling_Mean Imputation_...
66 Neural Networks_Undersampling_Mean Imputation_...
67 Neural Networks_Undersampling_Mean Imputation_...
68 Neural Networks_Undersampling_Mean Imputation_...
69 Neural Networks_Undersampling_Mean Imputation_...
70 Neural Networks_Undersampling_Mean Imputation_...
71 Neural Networks_Undersampling_Mean Imputation_...
72 Neural Networks_Undersampling_Mean Imputation_...
73 Neural Networks_Undersampling_Mean Imputation_...
74 Neural Networks_Undersampling_Mean Imputation_...
75 Neural Networks_Undersampling_Mean Imputation_...
76 Neural Networks_Undersampling_Mean Imputation_...
77 Neural Networks_Undersampling_Mean Imputation_...
78 Neural Networks_Undersampling_Mean Imputation_...
79 Neural Networks_Undersampling_Mean Imputation_...
80 Neural Networks_Undersampling_Mean Imputation_...
81 Neural Networks_Undersampling_Mean Imputation_...
82 Neural Networks_Undersampling_Mean Imputation_...
83 Neural Networks_Undersampling_Mean Imputation_...
84 Neural Networks_Undersampling_Mean Imputation_...
85 Neural Networks_Undersampling_Mean Imputation_...
86 Neural Networks_Undersampling_Mean Imputation_...
87 Neural Networks_Undersampling_Mean Imputation_...
88 Neural Networks_Undersampling_Mean Imputation_...
89 Neural Networks_Undersampling_Mean Imputation_...
90 Neural Networks_Undersampling_Mean Imputation_...
91 Neural Networks_Undersampling_Mean Imputation_...
92 Neural Networks_Undersampling_Mean Imputation_...
93 Neural Networks_Undersampling_Mean Imputation_...
94 Neural Networks_Undersampling_Mean Imputation_...
95 Neural Networks_Undersampling_Mean Imputation_...
96 Neural Networks_Undersampling_Mean Imputation_...
97 Neural Networks_Undersampling_Mean Imputation_...
98 Neural Networks_Undersampling_Mean Imputation_...
99 Neural Networks_Undersampling_Mean Imputation_...
100 Neural Networks_Undersampling_Mean Imputation_...
101 Neural Networks_Undersampling_Mean Imputation_...
102 Neural Networks_Undersampling_Mean Imputation_...
103 Neural Networks_Undersampling_Mean Imputation_...
104 Neural Networks_Undersampling_Mean Imputation_...
105 Neural Networks_Undersampling_Mean Imputation_...
106 Neural Networks_Undersampling_Mean Imputation_...
107 Neural Networks_Undersampling_Mean Imputation_...
108 Neural Networks_Undersampling_Mean Imputation_...
109 Neural Networks_Undersampling_Mean Imputation_...
110 Neural Networks_Undersampling_Mean Imputation_...
111 Neural Networks_Undersampling_Mean Imputation_...
112 Neural Networks_Undersampling_Mean Imputation_...
113 Neural Networks_Undersampling_Mean Imputation_...
114 Neural Networks_Undersampling_Mean Imputation_...
115 Neural Networks_Undersampling_Mean Imputation_...
116 Neural Networks_Undersampling_Mean Imputation_...
117 Neural Networks_Undersampling_Mean Imputation_...
118 Neural Networks_Undersampling_Mean Imputation_...
119 Neural Networks_Undersampling_Mean Imputation_...
120 Neural Networks_Undersampling_Mean Imputation_...
121 Neural Networks_Undersampling_Mean Imputation_...
122 Neural Networks_Undersampling_Mean Imputation_...
123 Neural Networks_Undersampling_Mean Imputation_...
124 Neural Networks_Undersampling_Mean Imputation_...
125 Neural Networks_Undersampling_Mean Imputation_...
126 Neural Networks_Undersampling_Mean Imputation_...
127 Neural Networks_Undersampling_Mean Imputation_...
128 Neural Networks_Undersampling_Mean Imputation_...
129 Neural Networks_Undersampling_Mean Imputation_...
130 Neural Networks_Undersampling_Mean Imputation_...
131 Neural Networks_Undersampling_Mean Imputation_...
132 Neural Networks_Undersampling_Mean Imputation_...
133 Neural Networks_Undersampling_Mean Imputation_...
134 Neural Networks_Undersampling_Mean Imputation_...
135 Neural Networks_Undersampling_Mean Imputation_...
136 Neural Networks_Undersampling_Mean Imputation_...
137 Neural Networks_Undersampling_Mean Imputation_...
138 Neural Networks_Undersampling_Mean Imputation_...
139 Neural Networks_Undersampling_Mean Imputation_...
140 Neural Networks_Undersampling_Mean Imputation_...
141 Neural Networks_Undersampling_Mean Imputation_...
142 Neural Networks_Undersampling_Mean Imputation_...
143 Neural Networks_Undersampling_Mean Imputation_...
144 Neural Networks_Undersampling_Mean Imputation_...
145 Neural Networks_Undersampling_Mean Imputation_...
146 Neural Networks_Undersampling_Mean Imputation_...
147 Neural Networks_Undersampling_Mean Imputation_...
148 Neural Networks_Undersampling_Mean Imputation_...
149 Neural Networks_Undersampling_Mean Imputation_... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Neural Networks 0.347906 NaN Median Imputation
1 Neural Networks 0.071237 NaN Median Imputation
2 Neural Networks 0.784810 NaN Median Imputation
3 Neural Networks 0.130618 NaN Median Imputation
4 Neural Networks 0.557143 NaN Median Imputation
5 Neural Networks 0.111578 NaN Median Imputation
6 Neural Networks 0.114286 NaN Median Imputation
7 Neural Networks NaN relu Median Imputation
8 Neural Networks NaN 0.0001 Median Imputation
9 Neural Networks NaN auto Median Imputation
10 Neural Networks NaN 0.9 Median Imputation
11 Neural Networks NaN 0.999 Median Imputation
12 Neural Networks NaN False Median Imputation
13 Neural Networks NaN 0.0 Median Imputation
14 Neural Networks NaN (100,) Median Imputation
15 Neural Networks NaN constant Median Imputation
16 Neural Networks NaN 0.001 Median Imputation
17 Neural Networks NaN 15000 Median Imputation
18 Neural Networks NaN 200 Median Imputation
19 Neural Networks NaN 0.9 Median Imputation
20 Neural Networks NaN 10 Median Imputation
21 Neural Networks NaN True Median Imputation
22 Neural Networks NaN 0.5 Median Imputation
23 Neural Networks NaN None Median Imputation
24 Neural Networks NaN True Median Imputation
25 Neural Networks NaN adam Median Imputation
26 Neural Networks NaN 0.0001 Median Imputation
27 Neural Networks NaN 0.1 Median Imputation
28 Neural Networks NaN False Median Imputation
29 Neural Networks NaN False Median Imputation
30 Neural Networks 0.489860 NaN Median Imputation
31 Neural Networks 0.058296 NaN Median Imputation
32 Neural Networks 0.713415 NaN Median Imputation
33 Neural Networks 0.107784 NaN Median Imputation
34 Neural Networks 0.614279 NaN Median Imputation
35 Neural Networks 0.200023 NaN Median Imputation
36 Neural Networks 0.228559 NaN Median Imputation
37 Neural Networks NaN relu Median Imputation
38 Neural Networks NaN 0.0001 Median Imputation
39 Neural Networks NaN auto Median Imputation
40 Neural Networks NaN 0.9 Median Imputation
41 Neural Networks NaN 0.999 Median Imputation
42 Neural Networks NaN False Median Imputation
43 Neural Networks NaN 0.0 Median Imputation
44 Neural Networks NaN (100,) Median Imputation
45 Neural Networks NaN constant Median Imputation
46 Neural Networks NaN 0.001 Median Imputation
47 Neural Networks NaN 15000 Median Imputation
48 Neural Networks NaN 200 Median Imputation
49 Neural Networks NaN 0.9 Median Imputation
50 Neural Networks NaN 10 Median Imputation
51 Neural Networks NaN True Median Imputation
52 Neural Networks NaN 0.5 Median Imputation
53 Neural Networks NaN None Median Imputation
54 Neural Networks NaN True Median Imputation
55 Neural Networks NaN adam Median Imputation
56 Neural Networks NaN 0.0001 Median Imputation
57 Neural Networks NaN 0.1 Median Imputation
58 Neural Networks NaN False Median Imputation
59 Neural Networks NaN False Median Imputation
60 Neural Networks 0.575191 NaN Median Imputation
61 Neural Networks 0.071514 NaN Median Imputation
62 Neural Networks 0.636364 NaN Median Imputation
63 Neural Networks 0.128579 NaN Median Imputation
64 Neural Networks 0.648829 NaN Median Imputation
65 Neural Networks 0.240617 NaN Median Imputation
66 Neural Networks 0.297658 NaN Median Imputation
67 Neural Networks NaN relu Median Imputation
68 Neural Networks NaN 0.0001 Median Imputation
69 Neural Networks NaN auto Median Imputation
70 Neural Networks NaN 0.9 Median Imputation
71 Neural Networks NaN 0.999 Median Imputation
72 Neural Networks NaN False Median Imputation
73 Neural Networks NaN 0.0 Median Imputation
74 Neural Networks NaN (100,) Median Imputation
75 Neural Networks NaN constant Median Imputation
76 Neural Networks NaN 0.001 Median Imputation
77 Neural Networks NaN 15000 Median Imputation
78 Neural Networks NaN 200 Median Imputation
79 Neural Networks NaN 0.9 Median Imputation
80 Neural Networks NaN 10 Median Imputation
81 Neural Networks NaN True Median Imputation
82 Neural Networks NaN 0.5 Median Imputation
83 Neural Networks NaN None Median Imputation
84 Neural Networks NaN True Median Imputation
85 Neural Networks NaN adam Median Imputation
86 Neural Networks NaN 0.0001 Median Imputation
87 Neural Networks NaN 0.1 Median Imputation
88 Neural Networks NaN False Median Imputation
89 Neural Networks NaN False Median Imputation
90 Neural Networks 0.699157 NaN Median Imputation
91 Neural Networks 0.059590 NaN Median Imputation
92 Neural Networks 0.326531 NaN Median Imputation
93 Neural Networks 0.100787 NaN Median Imputation
94 Neural Networks 0.593848 NaN Median Imputation
95 Neural Networks 0.200181 NaN Median Imputation
96 Neural Networks 0.187697 NaN Median Imputation
97 Neural Networks NaN relu Median Imputation
98 Neural Networks NaN 0.0001 Median Imputation
99 Neural Networks NaN auto Median Imputation
100 Neural Networks NaN 0.9 Median Imputation
101 Neural Networks NaN 0.999 Median Imputation
102 Neural Networks NaN False Median Imputation
103 Neural Networks NaN 0.0 Median Imputation
104 Neural Networks NaN (100,) Median Imputation
105 Neural Networks NaN constant Median Imputation
106 Neural Networks NaN 0.001 Median Imputation
107 Neural Networks NaN 15000 Median Imputation
108 Neural Networks NaN 200 Median Imputation
109 Neural Networks NaN 0.9 Median Imputation
110 Neural Networks NaN 10 Median Imputation
111 Neural Networks NaN True Median Imputation
112 Neural Networks NaN 0.5 Median Imputation
113 Neural Networks NaN None Median Imputation
114 Neural Networks NaN True Median Imputation
115 Neural Networks NaN adam Median Imputation
116 Neural Networks NaN 0.0001 Median Imputation
117 Neural Networks NaN 0.1 Median Imputation
118 Neural Networks NaN False Median Imputation
119 Neural Networks NaN False Median Imputation
120 Neural Networks 0.572445 NaN Median Imputation
121 Neural Networks 0.073895 NaN Median Imputation
122 Neural Networks 0.564815 NaN Median Imputation
123 Neural Networks 0.130691 NaN Median Imputation
124 Neural Networks 0.606509 NaN Median Imputation
125 Neural Networks 0.227343 NaN Median Imputation
126 Neural Networks 0.213019 NaN Median Imputation
127 Neural Networks NaN relu Median Imputation
128 Neural Networks NaN 0.0001 Median Imputation
129 Neural Networks NaN auto Median Imputation
130 Neural Networks NaN 0.9 Median Imputation
131 Neural Networks NaN 0.999 Median Imputation
132 Neural Networks NaN False Median Imputation
133 Neural Networks NaN 0.0 Median Imputation
134 Neural Networks NaN (100,) Median Imputation
135 Neural Networks NaN constant Median Imputation
136 Neural Networks NaN 0.001 Median Imputation
137 Neural Networks NaN 15000 Median Imputation
138 Neural Networks NaN 200 Median Imputation
139 Neural Networks NaN 0.9 Median Imputation
140 Neural Networks NaN 10 Median Imputation
141 Neural Networks NaN True Median Imputation
142 Neural Networks NaN 0.5 Median Imputation
143 Neural Networks NaN None Median Imputation
144 Neural Networks NaN True Median Imputation
145 Neural Networks NaN adam Median Imputation
146 Neural Networks NaN 0.0001 Median Imputation
147 Neural Networks NaN 0.1 Median Imputation
148 Neural Networks NaN False Median Imputation
149 Neural Networks NaN False Median Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
45 Undersampling
46 Undersampling
47 Undersampling
48 Undersampling
49 Undersampling
50 Undersampling
51 Undersampling
52 Undersampling
53 Undersampling
54 Undersampling
55 Undersampling
56 Undersampling
57 Undersampling
58 Undersampling
59 Undersampling
60 Undersampling
61 Undersampling
62 Undersampling
63 Undersampling
64 Undersampling
65 Undersampling
66 Undersampling
67 Undersampling
68 Undersampling
69 Undersampling
70 Undersampling
71 Undersampling
72 Undersampling
73 Undersampling
74 Undersampling
75 Undersampling
76 Undersampling
77 Undersampling
78 Undersampling
79 Undersampling
80 Undersampling
81 Undersampling
82 Undersampling
83 Undersampling
84 Undersampling
85 Undersampling
86 Undersampling
87 Undersampling
88 Undersampling
89 Undersampling
90 Undersampling
91 Undersampling
92 Undersampling
93 Undersampling
94 Undersampling
95 Undersampling
96 Undersampling
97 Undersampling
98 Undersampling
99 Undersampling
100 Undersampling
101 Undersampling
102 Undersampling
103 Undersampling
104 Undersampling
105 Undersampling
106 Undersampling
107 Undersampling
108 Undersampling
109 Undersampling
110 Undersampling
111 Undersampling
112 Undersampling
113 Undersampling
114 Undersampling
115 Undersampling
116 Undersampling
117 Undersampling
118 Undersampling
119 Undersampling
120 Undersampling
121 Undersampling
122 Undersampling
123 Undersampling
124 Undersampling
125 Undersampling
126 Undersampling
127 Undersampling
128 Undersampling
129 Undersampling
130 Undersampling
131 Undersampling
132 Undersampling
133 Undersampling
134 Undersampling
135 Undersampling
136 Undersampling
137 Undersampling
138 Undersampling
139 Undersampling
140 Undersampling
141 Undersampling
142 Undersampling
143 Undersampling
144 Undersampling
145 Undersampling
146 Undersampling
147 Undersampling
148 Undersampling
149 Undersampling
Model Unique Code
0 Neural Networks_Undersampling_Median Imputatio...
1 Neural Networks_Undersampling_Median Imputatio...
2 Neural Networks_Undersampling_Median Imputatio...
3 Neural Networks_Undersampling_Median Imputatio...
4 Neural Networks_Undersampling_Median Imputatio...
5 Neural Networks_Undersampling_Median Imputatio...
6 Neural Networks_Undersampling_Median Imputatio...
7 Neural Networks_Undersampling_Median Imputatio...
8 Neural Networks_Undersampling_Median Imputatio...
9 Neural Networks_Undersampling_Median Imputatio...
10 Neural Networks_Undersampling_Median Imputatio...
11 Neural Networks_Undersampling_Median Imputatio...
12 Neural Networks_Undersampling_Median Imputatio...
13 Neural Networks_Undersampling_Median Imputatio...
14 Neural Networks_Undersampling_Median Imputatio...
15 Neural Networks_Undersampling_Median Imputatio...
16 Neural Networks_Undersampling_Median Imputatio...
17 Neural Networks_Undersampling_Median Imputatio...
18 Neural Networks_Undersampling_Median Imputatio...
19 Neural Networks_Undersampling_Median Imputatio...
20 Neural Networks_Undersampling_Median Imputatio...
21 Neural Networks_Undersampling_Median Imputatio...
22 Neural Networks_Undersampling_Median Imputatio...
23 Neural Networks_Undersampling_Median Imputatio...
24 Neural Networks_Undersampling_Median Imputatio...
25 Neural Networks_Undersampling_Median Imputatio...
26 Neural Networks_Undersampling_Median Imputatio...
27 Neural Networks_Undersampling_Median Imputatio...
28 Neural Networks_Undersampling_Median Imputatio...
29 Neural Networks_Undersampling_Median Imputatio...
30 Neural Networks_Undersampling_Median Imputatio...
31 Neural Networks_Undersampling_Median Imputatio...
32 Neural Networks_Undersampling_Median Imputatio...
33 Neural Networks_Undersampling_Median Imputatio...
34 Neural Networks_Undersampling_Median Imputatio...
35 Neural Networks_Undersampling_Median Imputatio...
36 Neural Networks_Undersampling_Median Imputatio...
37 Neural Networks_Undersampling_Median Imputatio...
38 Neural Networks_Undersampling_Median Imputatio...
39 Neural Networks_Undersampling_Median Imputatio...
40 Neural Networks_Undersampling_Median Imputatio...
41 Neural Networks_Undersampling_Median Imputatio...
42 Neural Networks_Undersampling_Median Imputatio...
43 Neural Networks_Undersampling_Median Imputatio...
44 Neural Networks_Undersampling_Median Imputatio...
45 Neural Networks_Undersampling_Median Imputatio...
46 Neural Networks_Undersampling_Median Imputatio...
47 Neural Networks_Undersampling_Median Imputatio...
48 Neural Networks_Undersampling_Median Imputatio...
49 Neural Networks_Undersampling_Median Imputatio...
50 Neural Networks_Undersampling_Median Imputatio...
51 Neural Networks_Undersampling_Median Imputatio...
52 Neural Networks_Undersampling_Median Imputatio...
53 Neural Networks_Undersampling_Median Imputatio...
54 Neural Networks_Undersampling_Median Imputatio...
55 Neural Networks_Undersampling_Median Imputatio...
56 Neural Networks_Undersampling_Median Imputatio...
57 Neural Networks_Undersampling_Median Imputatio...
58 Neural Networks_Undersampling_Median Imputatio...
59 Neural Networks_Undersampling_Median Imputatio...
60 Neural Networks_Undersampling_Median Imputatio...
61 Neural Networks_Undersampling_Median Imputatio...
62 Neural Networks_Undersampling_Median Imputatio...
63 Neural Networks_Undersampling_Median Imputatio...
64 Neural Networks_Undersampling_Median Imputatio...
65 Neural Networks_Undersampling_Median Imputatio...
66 Neural Networks_Undersampling_Median Imputatio...
67 Neural Networks_Undersampling_Median Imputatio...
68 Neural Networks_Undersampling_Median Imputatio...
69 Neural Networks_Undersampling_Median Imputatio...
70 Neural Networks_Undersampling_Median Imputatio...
71 Neural Networks_Undersampling_Median Imputatio...
72 Neural Networks_Undersampling_Median Imputatio...
73 Neural Networks_Undersampling_Median Imputatio...
74 Neural Networks_Undersampling_Median Imputatio...
75 Neural Networks_Undersampling_Median Imputatio...
76 Neural Networks_Undersampling_Median Imputatio...
77 Neural Networks_Undersampling_Median Imputatio...
78 Neural Networks_Undersampling_Median Imputatio...
79 Neural Networks_Undersampling_Median Imputatio...
80 Neural Networks_Undersampling_Median Imputatio...
81 Neural Networks_Undersampling_Median Imputatio...
82 Neural Networks_Undersampling_Median Imputatio...
83 Neural Networks_Undersampling_Median Imputatio...
84 Neural Networks_Undersampling_Median Imputatio...
85 Neural Networks_Undersampling_Median Imputatio...
86 Neural Networks_Undersampling_Median Imputatio...
87 Neural Networks_Undersampling_Median Imputatio...
88 Neural Networks_Undersampling_Median Imputatio...
89 Neural Networks_Undersampling_Median Imputatio...
90 Neural Networks_Undersampling_Median Imputatio...
91 Neural Networks_Undersampling_Median Imputatio...
92 Neural Networks_Undersampling_Median Imputatio...
93 Neural Networks_Undersampling_Median Imputatio...
94 Neural Networks_Undersampling_Median Imputatio...
95 Neural Networks_Undersampling_Median Imputatio...
96 Neural Networks_Undersampling_Median Imputatio...
97 Neural Networks_Undersampling_Median Imputatio...
98 Neural Networks_Undersampling_Median Imputatio...
99 Neural Networks_Undersampling_Median Imputatio...
100 Neural Networks_Undersampling_Median Imputatio...
101 Neural Networks_Undersampling_Median Imputatio...
102 Neural Networks_Undersampling_Median Imputatio...
103 Neural Networks_Undersampling_Median Imputatio...
104 Neural Networks_Undersampling_Median Imputatio...
105 Neural Networks_Undersampling_Median Imputatio...
106 Neural Networks_Undersampling_Median Imputatio...
107 Neural Networks_Undersampling_Median Imputatio...
108 Neural Networks_Undersampling_Median Imputatio...
109 Neural Networks_Undersampling_Median Imputatio...
110 Neural Networks_Undersampling_Median Imputatio...
111 Neural Networks_Undersampling_Median Imputatio...
112 Neural Networks_Undersampling_Median Imputatio...
113 Neural Networks_Undersampling_Median Imputatio...
114 Neural Networks_Undersampling_Median Imputatio...
115 Neural Networks_Undersampling_Median Imputatio...
116 Neural Networks_Undersampling_Median Imputatio...
117 Neural Networks_Undersampling_Median Imputatio...
118 Neural Networks_Undersampling_Median Imputatio...
119 Neural Networks_Undersampling_Median Imputatio...
120 Neural Networks_Undersampling_Median Imputatio...
121 Neural Networks_Undersampling_Median Imputatio...
122 Neural Networks_Undersampling_Median Imputatio...
123 Neural Networks_Undersampling_Median Imputatio...
124 Neural Networks_Undersampling_Median Imputatio...
125 Neural Networks_Undersampling_Median Imputatio...
126 Neural Networks_Undersampling_Median Imputatio...
127 Neural Networks_Undersampling_Median Imputatio...
128 Neural Networks_Undersampling_Median Imputatio...
129 Neural Networks_Undersampling_Median Imputatio...
130 Neural Networks_Undersampling_Median Imputatio...
131 Neural Networks_Undersampling_Median Imputatio...
132 Neural Networks_Undersampling_Median Imputatio...
133 Neural Networks_Undersampling_Median Imputatio...
134 Neural Networks_Undersampling_Median Imputatio...
135 Neural Networks_Undersampling_Median Imputatio...
136 Neural Networks_Undersampling_Median Imputatio...
137 Neural Networks_Undersampling_Median Imputatio...
138 Neural Networks_Undersampling_Median Imputatio...
139 Neural Networks_Undersampling_Median Imputatio...
140 Neural Networks_Undersampling_Median Imputatio...
141 Neural Networks_Undersampling_Median Imputatio...
142 Neural Networks_Undersampling_Median Imputatio...
143 Neural Networks_Undersampling_Median Imputatio...
144 Neural Networks_Undersampling_Median Imputatio...
145 Neural Networks_Undersampling_Median Imputatio...
146 Neural Networks_Undersampling_Median Imputatio...
147 Neural Networks_Undersampling_Median Imputatio...
148 Neural Networks_Undersampling_Median Imputatio...
149 Neural Networks_Undersampling_Median Imputatio... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Neural Networks 0.741902 NaN Zero Imputation
1 Neural Networks 0.107709 NaN Zero Imputation
2 Neural Networks 0.430380 NaN Zero Imputation
3 Neural Networks 0.172297 NaN Zero Imputation
4 Neural Networks 0.628136 NaN Zero Imputation
5 Neural Networks 0.226666 NaN Zero Imputation
6 Neural Networks 0.256271 NaN Zero Imputation
7 Neural Networks NaN relu Zero Imputation
8 Neural Networks NaN 0.0001 Zero Imputation
9 Neural Networks NaN auto Zero Imputation
10 Neural Networks NaN 0.9 Zero Imputation
11 Neural Networks NaN 0.999 Zero Imputation
12 Neural Networks NaN False Zero Imputation
13 Neural Networks NaN 0.0 Zero Imputation
14 Neural Networks NaN (100,) Zero Imputation
15 Neural Networks NaN constant Zero Imputation
16 Neural Networks NaN 0.001 Zero Imputation
17 Neural Networks NaN 15000 Zero Imputation
18 Neural Networks NaN 200 Zero Imputation
19 Neural Networks NaN 0.9 Zero Imputation
20 Neural Networks NaN 10 Zero Imputation
21 Neural Networks NaN True Zero Imputation
22 Neural Networks NaN 0.5 Zero Imputation
23 Neural Networks NaN None Zero Imputation
24 Neural Networks NaN True Zero Imputation
25 Neural Networks NaN adam Zero Imputation
26 Neural Networks NaN 0.0001 Zero Imputation
27 Neural Networks NaN 0.1 Zero Imputation
28 Neural Networks NaN False Zero Imputation
29 Neural Networks NaN False Zero Imputation
30 Neural Networks 0.750066 NaN Zero Imputation
31 Neural Networks 0.081110 NaN Zero Imputation
32 Neural Networks 0.463415 NaN Zero Imputation
33 Neural Networks 0.138056 NaN Zero Imputation
34 Neural Networks 0.663222 NaN Zero Imputation
35 Neural Networks 0.299903 NaN Zero Imputation
36 Neural Networks 0.326444 NaN Zero Imputation
37 Neural Networks NaN relu Zero Imputation
38 Neural Networks NaN 0.0001 Zero Imputation
39 Neural Networks NaN auto Zero Imputation
40 Neural Networks NaN 0.9 Zero Imputation
41 Neural Networks NaN 0.999 Zero Imputation
42 Neural Networks NaN False Zero Imputation
43 Neural Networks NaN 0.0 Zero Imputation
44 Neural Networks NaN (100,) Zero Imputation
45 Neural Networks NaN constant Zero Imputation
46 Neural Networks NaN 0.001 Zero Imputation
47 Neural Networks NaN 15000 Zero Imputation
48 Neural Networks NaN 200 Zero Imputation
49 Neural Networks NaN 0.9 Zero Imputation
50 Neural Networks NaN 10 Zero Imputation
51 Neural Networks NaN True Zero Imputation
52 Neural Networks NaN 0.5 Zero Imputation
53 Neural Networks NaN None Zero Imputation
54 Neural Networks NaN True Zero Imputation
55 Neural Networks NaN adam Zero Imputation
56 Neural Networks NaN 0.0001 Zero Imputation
57 Neural Networks NaN 0.1 Zero Imputation
58 Neural Networks NaN False Zero Imputation
59 Neural Networks NaN False Zero Imputation
60 Neural Networks 0.834343 NaN Zero Imputation
61 Neural Networks 0.096715 NaN Zero Imputation
62 Neural Networks 0.283422 NaN Zero Imputation
63 Neural Networks 0.144218 NaN Zero Imputation
64 Neural Networks 0.649138 NaN Zero Imputation
65 Neural Networks 0.269494 NaN Zero Imputation
66 Neural Networks 0.298276 NaN Zero Imputation
67 Neural Networks NaN relu Zero Imputation
68 Neural Networks NaN 0.0001 Zero Imputation
69 Neural Networks NaN auto Zero Imputation
70 Neural Networks NaN 0.9 Zero Imputation
71 Neural Networks NaN 0.999 Zero Imputation
72 Neural Networks NaN False Zero Imputation
73 Neural Networks NaN 0.0 Zero Imputation
74 Neural Networks NaN (100,) Zero Imputation
75 Neural Networks NaN constant Zero Imputation
76 Neural Networks NaN 0.001 Zero Imputation
77 Neural Networks NaN 15000 Zero Imputation
78 Neural Networks NaN 200 Zero Imputation
79 Neural Networks NaN 0.9 Zero Imputation
80 Neural Networks NaN 10 Zero Imputation
81 Neural Networks NaN True Zero Imputation
82 Neural Networks NaN 0.5 Zero Imputation
83 Neural Networks NaN None Zero Imputation
84 Neural Networks NaN True Zero Imputation
85 Neural Networks NaN adam Zero Imputation
86 Neural Networks NaN 0.0001 Zero Imputation
87 Neural Networks NaN 0.1 Zero Imputation
88 Neural Networks NaN False Zero Imputation
89 Neural Networks NaN False Zero Imputation
90 Neural Networks 0.723920 NaN Zero Imputation
91 Neural Networks 0.064417 NaN Zero Imputation
92 Neural Networks 0.321429 NaN Zero Imputation
93 Neural Networks 0.107325 NaN Zero Imputation
94 Neural Networks 0.609005 NaN Zero Imputation
95 Neural Networks 0.222500 NaN Zero Imputation
96 Neural Networks 0.218010 NaN Zero Imputation
97 Neural Networks NaN relu Zero Imputation
98 Neural Networks NaN 0.0001 Zero Imputation
99 Neural Networks NaN auto Zero Imputation
100 Neural Networks NaN 0.9 Zero Imputation
101 Neural Networks NaN 0.999 Zero Imputation
102 Neural Networks NaN False Zero Imputation
103 Neural Networks NaN 0.0 Zero Imputation
104 Neural Networks NaN (100,) Zero Imputation
105 Neural Networks NaN constant Zero Imputation
106 Neural Networks NaN 0.001 Zero Imputation
107 Neural Networks NaN 15000 Zero Imputation
108 Neural Networks NaN 200 Zero Imputation
109 Neural Networks NaN 0.9 Zero Imputation
110 Neural Networks NaN 10 Zero Imputation
111 Neural Networks NaN True Zero Imputation
112 Neural Networks NaN 0.5 Zero Imputation
113 Neural Networks NaN None Zero Imputation
114 Neural Networks NaN True Zero Imputation
115 Neural Networks NaN adam Zero Imputation
116 Neural Networks NaN 0.0001 Zero Imputation
117 Neural Networks NaN 0.1 Zero Imputation
118 Neural Networks NaN False Zero Imputation
119 Neural Networks NaN False Zero Imputation
120 Neural Networks 0.721286 NaN Zero Imputation
121 Neural Networks 0.094412 NaN Zero Imputation
122 Neural Networks 0.453704 NaN Zero Imputation
123 Neural Networks 0.156300 NaN Zero Imputation
124 Neural Networks 0.639874 NaN Zero Imputation
125 Neural Networks 0.294920 NaN Zero Imputation
126 Neural Networks 0.279747 NaN Zero Imputation
127 Neural Networks NaN relu Zero Imputation
128 Neural Networks NaN 0.0001 Zero Imputation
129 Neural Networks NaN auto Zero Imputation
130 Neural Networks NaN 0.9 Zero Imputation
131 Neural Networks NaN 0.999 Zero Imputation
132 Neural Networks NaN False Zero Imputation
133 Neural Networks NaN 0.0 Zero Imputation
134 Neural Networks NaN (100,) Zero Imputation
135 Neural Networks NaN constant Zero Imputation
136 Neural Networks NaN 0.001 Zero Imputation
137 Neural Networks NaN 15000 Zero Imputation
138 Neural Networks NaN 200 Zero Imputation
139 Neural Networks NaN 0.9 Zero Imputation
140 Neural Networks NaN 10 Zero Imputation
141 Neural Networks NaN True Zero Imputation
142 Neural Networks NaN 0.5 Zero Imputation
143 Neural Networks NaN None Zero Imputation
144 Neural Networks NaN True Zero Imputation
145 Neural Networks NaN adam Zero Imputation
146 Neural Networks NaN 0.0001 Zero Imputation
147 Neural Networks NaN 0.1 Zero Imputation
148 Neural Networks NaN False Zero Imputation
149 Neural Networks NaN False Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
95 Hybrid Sampling (SMOTEENN)
96 Hybrid Sampling (SMOTEENN)
97 Hybrid Sampling (SMOTEENN)
98 Hybrid Sampling (SMOTEENN)
99 Hybrid Sampling (SMOTEENN)
100 Hybrid Sampling (SMOTEENN)
101 Hybrid Sampling (SMOTEENN)
102 Hybrid Sampling (SMOTEENN)
103 Hybrid Sampling (SMOTEENN)
104 Hybrid Sampling (SMOTEENN)
105 Hybrid Sampling (SMOTEENN)
106 Hybrid Sampling (SMOTEENN)
107 Hybrid Sampling (SMOTEENN)
108 Hybrid Sampling (SMOTEENN)
109 Hybrid Sampling (SMOTEENN)
110 Hybrid Sampling (SMOTEENN)
111 Hybrid Sampling (SMOTEENN)
112 Hybrid Sampling (SMOTEENN)
113 Hybrid Sampling (SMOTEENN)
114 Hybrid Sampling (SMOTEENN)
115 Hybrid Sampling (SMOTEENN)
116 Hybrid Sampling (SMOTEENN)
117 Hybrid Sampling (SMOTEENN)
118 Hybrid Sampling (SMOTEENN)
119 Hybrid Sampling (SMOTEENN)
120 Hybrid Sampling (SMOTEENN)
121 Hybrid Sampling (SMOTEENN)
122 Hybrid Sampling (SMOTEENN)
123 Hybrid Sampling (SMOTEENN)
124 Hybrid Sampling (SMOTEENN)
125 Hybrid Sampling (SMOTEENN)
126 Hybrid Sampling (SMOTEENN)
127 Hybrid Sampling (SMOTEENN)
128 Hybrid Sampling (SMOTEENN)
129 Hybrid Sampling (SMOTEENN)
130 Hybrid Sampling (SMOTEENN)
131 Hybrid Sampling (SMOTEENN)
132 Hybrid Sampling (SMOTEENN)
133 Hybrid Sampling (SMOTEENN)
134 Hybrid Sampling (SMOTEENN)
135 Hybrid Sampling (SMOTEENN)
136 Hybrid Sampling (SMOTEENN)
137 Hybrid Sampling (SMOTEENN)
138 Hybrid Sampling (SMOTEENN)
139 Hybrid Sampling (SMOTEENN)
140 Hybrid Sampling (SMOTEENN)
141 Hybrid Sampling (SMOTEENN)
142 Hybrid Sampling (SMOTEENN)
143 Hybrid Sampling (SMOTEENN)
144 Hybrid Sampling (SMOTEENN)
145 Hybrid Sampling (SMOTEENN)
146 Hybrid Sampling (SMOTEENN)
147 Hybrid Sampling (SMOTEENN)
148 Hybrid Sampling (SMOTEENN)
149 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
1 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
2 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
3 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
4 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
5 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
6 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
7 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
8 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
9 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
10 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
11 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
12 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
13 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
14 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
15 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
16 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
17 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
18 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
19 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
20 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
21 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
22 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
23 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
24 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
25 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
26 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
27 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
28 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
29 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
30 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
31 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
32 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
33 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
34 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
35 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
36 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
37 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
38 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
39 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
40 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
41 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
42 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
43 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
44 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
45 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
46 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
47 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
48 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
49 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
50 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
51 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
52 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
53 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
54 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
55 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
56 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
57 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
58 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
59 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
60 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
61 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
62 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
63 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
64 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
65 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
66 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
67 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
68 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
69 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
70 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
71 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
72 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
73 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
74 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
75 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
76 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
77 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
78 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
79 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
80 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
81 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
82 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
83 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
84 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
85 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
86 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
87 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
88 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
89 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
90 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
91 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
92 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
93 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
94 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
95 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
96 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
97 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
98 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
99 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
100 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
101 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
102 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
103 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
104 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
105 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
106 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
107 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
108 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
109 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
110 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
111 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
112 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
113 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
114 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
115 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
116 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
117 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
118 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
119 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
120 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
121 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
122 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
123 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
124 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
125 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
126 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
127 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
128 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
129 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
130 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
131 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
132 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
133 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
134 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
135 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
136 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
137 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
138 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
139 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
140 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
141 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
142 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
143 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
144 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
145 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
146 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
147 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
148 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer...
149 Neural Networks_Hybrid Sampling (SMOTEENN)_Zer... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Neural Networks 0.533579 NaN Mean Imputation
1 Neural Networks 0.082244 NaN Mean Imputation
2 Neural Networks 0.637131 NaN Mean Imputation
3 Neural Networks 0.145683 NaN Mean Imputation
4 Neural Networks 0.604780 NaN Mean Imputation
5 Neural Networks 0.183414 NaN Mean Imputation
6 Neural Networks 0.209560 NaN Mean Imputation
7 Neural Networks NaN relu Mean Imputation
8 Neural Networks NaN 0.0001 Mean Imputation
9 Neural Networks NaN auto Mean Imputation
10 Neural Networks NaN 0.9 Mean Imputation
11 Neural Networks NaN 0.999 Mean Imputation
12 Neural Networks NaN False Mean Imputation
13 Neural Networks NaN 0.0 Mean Imputation
14 Neural Networks NaN (100,) Mean Imputation
15 Neural Networks NaN constant Mean Imputation
16 Neural Networks NaN 0.001 Mean Imputation
17 Neural Networks NaN 15000 Mean Imputation
18 Neural Networks NaN 200 Mean Imputation
19 Neural Networks NaN 0.9 Mean Imputation
20 Neural Networks NaN 10 Mean Imputation
21 Neural Networks NaN True Mean Imputation
22 Neural Networks NaN 0.5 Mean Imputation
23 Neural Networks NaN None Mean Imputation
24 Neural Networks NaN True Mean Imputation
25 Neural Networks NaN adam Mean Imputation
26 Neural Networks NaN 0.0001 Mean Imputation
27 Neural Networks NaN 0.1 Mean Imputation
28 Neural Networks NaN False Mean Imputation
29 Neural Networks NaN False Mean Imputation
30 Neural Networks 0.658151 NaN Mean Imputation
31 Neural Networks 0.071104 NaN Mean Imputation
32 Neural Networks 0.573171 NaN Mean Imputation
33 Neural Networks 0.126514 NaN Mean Imputation
34 Neural Networks 0.648593 NaN Mean Imputation
35 Neural Networks 0.265184 NaN Mean Imputation
36 Neural Networks 0.297186 NaN Mean Imputation
37 Neural Networks NaN relu Mean Imputation
38 Neural Networks NaN 0.0001 Mean Imputation
39 Neural Networks NaN auto Mean Imputation
40 Neural Networks NaN 0.9 Mean Imputation
41 Neural Networks NaN 0.999 Mean Imputation
42 Neural Networks NaN False Mean Imputation
43 Neural Networks NaN 0.0 Mean Imputation
44 Neural Networks NaN (100,) Mean Imputation
45 Neural Networks NaN constant Mean Imputation
46 Neural Networks NaN 0.001 Mean Imputation
47 Neural Networks NaN 15000 Mean Imputation
48 Neural Networks NaN 200 Mean Imputation
49 Neural Networks NaN 0.9 Mean Imputation
50 Neural Networks NaN 10 Mean Imputation
51 Neural Networks NaN True Mean Imputation
52 Neural Networks NaN 0.5 Mean Imputation
53 Neural Networks NaN None Mean Imputation
54 Neural Networks NaN True Mean Imputation
55 Neural Networks NaN adam Mean Imputation
56 Neural Networks NaN 0.0001 Mean Imputation
57 Neural Networks NaN 0.1 Mean Imputation
58 Neural Networks NaN False Mean Imputation
59 Neural Networks NaN False Mean Imputation
60 Neural Networks 0.610482 NaN Mean Imputation
61 Neural Networks 0.069333 NaN Mean Imputation
62 Neural Networks 0.556150 NaN Mean Imputation
63 Neural Networks 0.123296 NaN Mean Imputation
64 Neural Networks 0.625584 NaN Mean Imputation
65 Neural Networks 0.222173 NaN Mean Imputation
66 Neural Networks 0.251168 NaN Mean Imputation
67 Neural Networks NaN relu Mean Imputation
68 Neural Networks NaN 0.0001 Mean Imputation
69 Neural Networks NaN auto Mean Imputation
70 Neural Networks NaN 0.9 Mean Imputation
71 Neural Networks NaN 0.999 Mean Imputation
72 Neural Networks NaN False Mean Imputation
73 Neural Networks NaN 0.0 Mean Imputation
74 Neural Networks NaN (100,) Mean Imputation
75 Neural Networks NaN constant Mean Imputation
76 Neural Networks NaN 0.001 Mean Imputation
77 Neural Networks NaN 15000 Mean Imputation
78 Neural Networks NaN 200 Mean Imputation
79 Neural Networks NaN 0.9 Mean Imputation
80 Neural Networks NaN 10 Mean Imputation
81 Neural Networks NaN True Mean Imputation
82 Neural Networks NaN 0.5 Mean Imputation
83 Neural Networks NaN None Mean Imputation
84 Neural Networks NaN True Mean Imputation
85 Neural Networks NaN adam Mean Imputation
86 Neural Networks NaN 0.0001 Mean Imputation
87 Neural Networks NaN 0.1 Mean Imputation
88 Neural Networks NaN False Mean Imputation
89 Neural Networks NaN False Mean Imputation
90 Neural Networks 0.625132 NaN Mean Imputation
91 Neural Networks 0.081229 NaN Mean Imputation
92 Neural Networks 0.607143 NaN Mean Imputation
93 Neural Networks 0.143287 NaN Mean Imputation
94 Neural Networks 0.650699 NaN Mean Imputation
95 Neural Networks 0.273724 NaN Mean Imputation
96 Neural Networks 0.301397 NaN Mean Imputation
97 Neural Networks NaN relu Mean Imputation
98 Neural Networks NaN 0.0001 Mean Imputation
99 Neural Networks NaN auto Mean Imputation
100 Neural Networks NaN 0.9 Mean Imputation
101 Neural Networks NaN 0.999 Mean Imputation
102 Neural Networks NaN False Mean Imputation
103 Neural Networks NaN 0.0 Mean Imputation
104 Neural Networks NaN (100,) Mean Imputation
105 Neural Networks NaN constant Mean Imputation
106 Neural Networks NaN 0.001 Mean Imputation
107 Neural Networks NaN 15000 Mean Imputation
108 Neural Networks NaN 200 Mean Imputation
109 Neural Networks NaN 0.9 Mean Imputation
110 Neural Networks NaN 10 Mean Imputation
111 Neural Networks NaN True Mean Imputation
112 Neural Networks NaN 0.5 Mean Imputation
113 Neural Networks NaN None Mean Imputation
114 Neural Networks NaN True Mean Imputation
115 Neural Networks NaN adam Mean Imputation
116 Neural Networks NaN 0.0001 Mean Imputation
117 Neural Networks NaN 0.1 Mean Imputation
118 Neural Networks NaN False Mean Imputation
119 Neural Networks NaN False Mean Imputation
120 Neural Networks 0.655954 NaN Mean Imputation
121 Neural Networks 0.092676 NaN Mean Imputation
122 Neural Networks 0.574074 NaN Mean Imputation
123 Neural Networks 0.159588 NaN Mean Imputation
124 Neural Networks 0.652773 NaN Mean Imputation
125 Neural Networks 0.249591 NaN Mean Imputation
126 Neural Networks 0.305547 NaN Mean Imputation
127 Neural Networks NaN relu Mean Imputation
128 Neural Networks NaN 0.0001 Mean Imputation
129 Neural Networks NaN auto Mean Imputation
130 Neural Networks NaN 0.9 Mean Imputation
131 Neural Networks NaN 0.999 Mean Imputation
132 Neural Networks NaN False Mean Imputation
133 Neural Networks NaN 0.0 Mean Imputation
134 Neural Networks NaN (100,) Mean Imputation
135 Neural Networks NaN constant Mean Imputation
136 Neural Networks NaN 0.001 Mean Imputation
137 Neural Networks NaN 15000 Mean Imputation
138 Neural Networks NaN 200 Mean Imputation
139 Neural Networks NaN 0.9 Mean Imputation
140 Neural Networks NaN 10 Mean Imputation
141 Neural Networks NaN True Mean Imputation
142 Neural Networks NaN 0.5 Mean Imputation
143 Neural Networks NaN None Mean Imputation
144 Neural Networks NaN True Mean Imputation
145 Neural Networks NaN adam Mean Imputation
146 Neural Networks NaN 0.0001 Mean Imputation
147 Neural Networks NaN 0.1 Mean Imputation
148 Neural Networks NaN False Mean Imputation
149 Neural Networks NaN False Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
95 Hybrid Sampling (SMOTEENN)
96 Hybrid Sampling (SMOTEENN)
97 Hybrid Sampling (SMOTEENN)
98 Hybrid Sampling (SMOTEENN)
99 Hybrid Sampling (SMOTEENN)
100 Hybrid Sampling (SMOTEENN)
101 Hybrid Sampling (SMOTEENN)
102 Hybrid Sampling (SMOTEENN)
103 Hybrid Sampling (SMOTEENN)
104 Hybrid Sampling (SMOTEENN)
105 Hybrid Sampling (SMOTEENN)
106 Hybrid Sampling (SMOTEENN)
107 Hybrid Sampling (SMOTEENN)
108 Hybrid Sampling (SMOTEENN)
109 Hybrid Sampling (SMOTEENN)
110 Hybrid Sampling (SMOTEENN)
111 Hybrid Sampling (SMOTEENN)
112 Hybrid Sampling (SMOTEENN)
113 Hybrid Sampling (SMOTEENN)
114 Hybrid Sampling (SMOTEENN)
115 Hybrid Sampling (SMOTEENN)
116 Hybrid Sampling (SMOTEENN)
117 Hybrid Sampling (SMOTEENN)
118 Hybrid Sampling (SMOTEENN)
119 Hybrid Sampling (SMOTEENN)
120 Hybrid Sampling (SMOTEENN)
121 Hybrid Sampling (SMOTEENN)
122 Hybrid Sampling (SMOTEENN)
123 Hybrid Sampling (SMOTEENN)
124 Hybrid Sampling (SMOTEENN)
125 Hybrid Sampling (SMOTEENN)
126 Hybrid Sampling (SMOTEENN)
127 Hybrid Sampling (SMOTEENN)
128 Hybrid Sampling (SMOTEENN)
129 Hybrid Sampling (SMOTEENN)
130 Hybrid Sampling (SMOTEENN)
131 Hybrid Sampling (SMOTEENN)
132 Hybrid Sampling (SMOTEENN)
133 Hybrid Sampling (SMOTEENN)
134 Hybrid Sampling (SMOTEENN)
135 Hybrid Sampling (SMOTEENN)
136 Hybrid Sampling (SMOTEENN)
137 Hybrid Sampling (SMOTEENN)
138 Hybrid Sampling (SMOTEENN)
139 Hybrid Sampling (SMOTEENN)
140 Hybrid Sampling (SMOTEENN)
141 Hybrid Sampling (SMOTEENN)
142 Hybrid Sampling (SMOTEENN)
143 Hybrid Sampling (SMOTEENN)
144 Hybrid Sampling (SMOTEENN)
145 Hybrid Sampling (SMOTEENN)
146 Hybrid Sampling (SMOTEENN)
147 Hybrid Sampling (SMOTEENN)
148 Hybrid Sampling (SMOTEENN)
149 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
1 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
2 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
3 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
4 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
5 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
6 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
7 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
8 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
9 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
10 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
11 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
12 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
13 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
14 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
15 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
16 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
17 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
18 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
19 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
20 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
21 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
22 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
23 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
24 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
25 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
26 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
27 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
28 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
29 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
30 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
31 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
32 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
33 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
34 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
35 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
36 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
37 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
38 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
39 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
40 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
41 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
42 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
43 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
44 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
45 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
46 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
47 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
48 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
49 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
50 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
51 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
52 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
53 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
54 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
55 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
56 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
57 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
58 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
59 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
60 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
61 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
62 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
63 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
64 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
65 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
66 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
67 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
68 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
69 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
70 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
71 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
72 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
73 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
74 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
75 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
76 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
77 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
78 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
79 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
80 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
81 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
82 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
83 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
84 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
85 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
86 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
87 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
88 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
89 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
90 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
91 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
92 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
93 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
94 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
95 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
96 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
97 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
98 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
99 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
100 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
101 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
102 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
103 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
104 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
105 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
106 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
107 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
108 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
109 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
110 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
111 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
112 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
113 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
114 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
115 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
116 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
117 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
118 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
119 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
120 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
121 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
122 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
123 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
124 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
125 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
126 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
127 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
128 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
129 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
130 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
131 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
132 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
133 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
134 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
135 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
136 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
137 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
138 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
139 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
140 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
141 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
142 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
143 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
144 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
145 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
146 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
147 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
148 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea...
149 Neural Networks_Hybrid Sampling (SMOTEENN)_Mea... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Neural Networks 0.723729 NaN Median Imputation
1 Neural Networks 0.098814 NaN Median Imputation
2 Neural Networks 0.421941 NaN Median Imputation
3 Neural Networks 0.160128 NaN Median Imputation
4 Neural Networks 0.616115 NaN Median Imputation
5 Neural Networks 0.192737 NaN Median Imputation
6 Neural Networks 0.232230 NaN Median Imputation
7 Neural Networks NaN relu Median Imputation
8 Neural Networks NaN 0.0001 Median Imputation
9 Neural Networks NaN auto Median Imputation
10 Neural Networks NaN 0.9 Median Imputation
11 Neural Networks NaN 0.999 Median Imputation
12 Neural Networks NaN False Median Imputation
13 Neural Networks NaN 0.0 Median Imputation
14 Neural Networks NaN (100,) Median Imputation
15 Neural Networks NaN constant Median Imputation
16 Neural Networks NaN 0.001 Median Imputation
17 Neural Networks NaN 15000 Median Imputation
18 Neural Networks NaN 200 Median Imputation
19 Neural Networks NaN 0.9 Median Imputation
20 Neural Networks NaN 10 Median Imputation
21 Neural Networks NaN True Median Imputation
22 Neural Networks NaN 0.5 Median Imputation
23 Neural Networks NaN None Median Imputation
24 Neural Networks NaN True Median Imputation
25 Neural Networks NaN adam Median Imputation
26 Neural Networks NaN 0.0001 Median Imputation
27 Neural Networks NaN 0.1 Median Imputation
28 Neural Networks NaN False Median Imputation
29 Neural Networks NaN False Median Imputation
30 Neural Networks 0.600211 NaN Median Imputation
31 Neural Networks 0.062661 NaN Median Imputation
32 Neural Networks 0.591463 NaN Median Imputation
33 Neural Networks 0.113318 NaN Median Imputation
34 Neural Networks 0.624543 NaN Median Imputation
35 Neural Networks 0.224880 NaN Median Imputation
36 Neural Networks 0.249085 NaN Median Imputation
37 Neural Networks NaN relu Median Imputation
38 Neural Networks NaN 0.0001 Median Imputation
39 Neural Networks NaN auto Median Imputation
40 Neural Networks NaN 0.9 Median Imputation
41 Neural Networks NaN 0.999 Median Imputation
42 Neural Networks NaN False Median Imputation
43 Neural Networks NaN 0.0 Median Imputation
44 Neural Networks NaN (100,) Median Imputation
45 Neural Networks NaN constant Median Imputation
46 Neural Networks NaN 0.001 Median Imputation
47 Neural Networks NaN 15000 Median Imputation
48 Neural Networks NaN 200 Median Imputation
49 Neural Networks NaN 0.9 Median Imputation
50 Neural Networks NaN 10 Median Imputation
51 Neural Networks NaN True Median Imputation
52 Neural Networks NaN 0.5 Median Imputation
53 Neural Networks NaN None Median Imputation
54 Neural Networks NaN True Median Imputation
55 Neural Networks NaN adam Median Imputation
56 Neural Networks NaN 0.0001 Median Imputation
57 Neural Networks NaN 0.1 Median Imputation
58 Neural Networks NaN False Median Imputation
59 Neural Networks NaN False Median Imputation
60 Neural Networks 0.871477 NaN Median Imputation
61 Neural Networks 0.094340 NaN Median Imputation
62 Neural Networks 0.187166 NaN Median Imputation
63 Neural Networks 0.125448 NaN Median Imputation
64 Neural Networks 0.649687 NaN Median Imputation
65 Neural Networks 0.286705 NaN Median Imputation
66 Neural Networks 0.299373 NaN Median Imputation
67 Neural Networks NaN relu Median Imputation
68 Neural Networks NaN 0.0001 Median Imputation
69 Neural Networks NaN auto Median Imputation
70 Neural Networks NaN 0.9 Median Imputation
71 Neural Networks NaN 0.999 Median Imputation
72 Neural Networks NaN False Median Imputation
73 Neural Networks NaN 0.0 Median Imputation
74 Neural Networks NaN (100,) Median Imputation
75 Neural Networks NaN constant Median Imputation
76 Neural Networks NaN 0.001 Median Imputation
77 Neural Networks NaN 15000 Median Imputation
78 Neural Networks NaN 200 Median Imputation
79 Neural Networks NaN 0.9 Median Imputation
80 Neural Networks NaN 10 Median Imputation
81 Neural Networks NaN True Median Imputation
82 Neural Networks NaN 0.5 Median Imputation
83 Neural Networks NaN None Median Imputation
84 Neural Networks NaN True Median Imputation
85 Neural Networks NaN adam Median Imputation
86 Neural Networks NaN 0.0001 Median Imputation
87 Neural Networks NaN 0.1 Median Imputation
88 Neural Networks NaN False Median Imputation
89 Neural Networks NaN False Median Imputation
90 Neural Networks 0.537671 NaN Median Imputation
91 Neural Networks 0.069575 NaN Median Imputation
92 Neural Networks 0.642857 NaN Median Imputation
93 Neural Networks 0.125561 NaN Median Imputation
94 Neural Networks 0.608250 NaN Median Imputation
95 Neural Networks 0.193254 NaN Median Imputation
96 Neural Networks 0.216499 NaN Median Imputation
97 Neural Networks NaN relu Median Imputation
98 Neural Networks NaN 0.0001 Median Imputation
99 Neural Networks NaN auto Median Imputation
100 Neural Networks NaN 0.9 Median Imputation
101 Neural Networks NaN 0.999 Median Imputation
102 Neural Networks NaN False Median Imputation
103 Neural Networks NaN 0.0 Median Imputation
104 Neural Networks NaN (100,) Median Imputation
105 Neural Networks NaN constant Median Imputation
106 Neural Networks NaN 0.001 Median Imputation
107 Neural Networks NaN 15000 Median Imputation
108 Neural Networks NaN 200 Median Imputation
109 Neural Networks NaN 0.9 Median Imputation
110 Neural Networks NaN 10 Median Imputation
111 Neural Networks NaN True Median Imputation
112 Neural Networks NaN 0.5 Median Imputation
113 Neural Networks NaN None Median Imputation
114 Neural Networks NaN True Median Imputation
115 Neural Networks NaN adam Median Imputation
116 Neural Networks NaN 0.0001 Median Imputation
117 Neural Networks NaN 0.1 Median Imputation
118 Neural Networks NaN False Median Imputation
119 Neural Networks NaN False Median Imputation
120 Neural Networks 0.434668 NaN Median Imputation
121 Neural Networks 0.068811 NaN Median Imputation
122 Neural Networks 0.712963 NaN Median Imputation
123 Neural Networks 0.125509 NaN Median Imputation
124 Neural Networks 0.591387 NaN Median Imputation
125 Neural Networks 0.160196 NaN Median Imputation
126 Neural Networks 0.182775 NaN Median Imputation
127 Neural Networks NaN relu Median Imputation
128 Neural Networks NaN 0.0001 Median Imputation
129 Neural Networks NaN auto Median Imputation
130 Neural Networks NaN 0.9 Median Imputation
131 Neural Networks NaN 0.999 Median Imputation
132 Neural Networks NaN False Median Imputation
133 Neural Networks NaN 0.0 Median Imputation
134 Neural Networks NaN (100,) Median Imputation
135 Neural Networks NaN constant Median Imputation
136 Neural Networks NaN 0.001 Median Imputation
137 Neural Networks NaN 15000 Median Imputation
138 Neural Networks NaN 200 Median Imputation
139 Neural Networks NaN 0.9 Median Imputation
140 Neural Networks NaN 10 Median Imputation
141 Neural Networks NaN True Median Imputation
142 Neural Networks NaN 0.5 Median Imputation
143 Neural Networks NaN None Median Imputation
144 Neural Networks NaN True Median Imputation
145 Neural Networks NaN adam Median Imputation
146 Neural Networks NaN 0.0001 Median Imputation
147 Neural Networks NaN 0.1 Median Imputation
148 Neural Networks NaN False Median Imputation
149 Neural Networks NaN False Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
95 Hybrid Sampling (SMOTEENN)
96 Hybrid Sampling (SMOTEENN)
97 Hybrid Sampling (SMOTEENN)
98 Hybrid Sampling (SMOTEENN)
99 Hybrid Sampling (SMOTEENN)
100 Hybrid Sampling (SMOTEENN)
101 Hybrid Sampling (SMOTEENN)
102 Hybrid Sampling (SMOTEENN)
103 Hybrid Sampling (SMOTEENN)
104 Hybrid Sampling (SMOTEENN)
105 Hybrid Sampling (SMOTEENN)
106 Hybrid Sampling (SMOTEENN)
107 Hybrid Sampling (SMOTEENN)
108 Hybrid Sampling (SMOTEENN)
109 Hybrid Sampling (SMOTEENN)
110 Hybrid Sampling (SMOTEENN)
111 Hybrid Sampling (SMOTEENN)
112 Hybrid Sampling (SMOTEENN)
113 Hybrid Sampling (SMOTEENN)
114 Hybrid Sampling (SMOTEENN)
115 Hybrid Sampling (SMOTEENN)
116 Hybrid Sampling (SMOTEENN)
117 Hybrid Sampling (SMOTEENN)
118 Hybrid Sampling (SMOTEENN)
119 Hybrid Sampling (SMOTEENN)
120 Hybrid Sampling (SMOTEENN)
121 Hybrid Sampling (SMOTEENN)
122 Hybrid Sampling (SMOTEENN)
123 Hybrid Sampling (SMOTEENN)
124 Hybrid Sampling (SMOTEENN)
125 Hybrid Sampling (SMOTEENN)
126 Hybrid Sampling (SMOTEENN)
127 Hybrid Sampling (SMOTEENN)
128 Hybrid Sampling (SMOTEENN)
129 Hybrid Sampling (SMOTEENN)
130 Hybrid Sampling (SMOTEENN)
131 Hybrid Sampling (SMOTEENN)
132 Hybrid Sampling (SMOTEENN)
133 Hybrid Sampling (SMOTEENN)
134 Hybrid Sampling (SMOTEENN)
135 Hybrid Sampling (SMOTEENN)
136 Hybrid Sampling (SMOTEENN)
137 Hybrid Sampling (SMOTEENN)
138 Hybrid Sampling (SMOTEENN)
139 Hybrid Sampling (SMOTEENN)
140 Hybrid Sampling (SMOTEENN)
141 Hybrid Sampling (SMOTEENN)
142 Hybrid Sampling (SMOTEENN)
143 Hybrid Sampling (SMOTEENN)
144 Hybrid Sampling (SMOTEENN)
145 Hybrid Sampling (SMOTEENN)
146 Hybrid Sampling (SMOTEENN)
147 Hybrid Sampling (SMOTEENN)
148 Hybrid Sampling (SMOTEENN)
149 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
1 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
2 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
3 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
4 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
5 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
6 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
7 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
8 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
9 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
10 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
11 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
12 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
13 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
14 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
15 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
16 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
17 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
18 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
19 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
20 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
21 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
22 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
23 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
24 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
25 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
26 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
27 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
28 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
29 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
30 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
31 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
32 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
33 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
34 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
35 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
36 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
37 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
38 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
39 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
40 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
41 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
42 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
43 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
44 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
45 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
46 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
47 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
48 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
49 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
50 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
51 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
52 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
53 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
54 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
55 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
56 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
57 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
58 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
59 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
60 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
61 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
62 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
63 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
64 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
65 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
66 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
67 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
68 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
69 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
70 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
71 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
72 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
73 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
74 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
75 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
76 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
77 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
78 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
79 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
80 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
81 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
82 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
83 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
84 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
85 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
86 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
87 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
88 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
89 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
90 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
91 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
92 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
93 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
94 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
95 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
96 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
97 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
98 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
99 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
100 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
101 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
102 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
103 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
104 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
105 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
106 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
107 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
108 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
109 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
110 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
111 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
112 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
113 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
114 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
115 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
116 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
117 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
118 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
119 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
120 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
121 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
122 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
123 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
124 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
125 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
126 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
127 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
128 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
129 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
130 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
131 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
132 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
133 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
134 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
135 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
136 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
137 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
138 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
139 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
140 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
141 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
142 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
143 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
144 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
145 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
146 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
147 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
148 Neural Networks_Hybrid Sampling (SMOTEENN)_Med...
149 Neural Networks_Hybrid Sampling (SMOTEENN)_Med... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Neural Networks 0.750329 NaN Zero Imputation
1 Neural Networks 0.105438 NaN Zero Imputation
2 Neural Networks 0.400844 NaN Zero Imputation
3 Neural Networks 0.166960 NaN Zero Imputation
4 Neural Networks 0.624751 NaN Zero Imputation
5 Neural Networks 0.216705 NaN Zero Imputation
6 Neural Networks 0.249501 NaN Zero Imputation
7 Neural Networks NaN relu Zero Imputation
8 Neural Networks NaN 0.0001 Zero Imputation
9 Neural Networks NaN auto Zero Imputation
10 Neural Networks NaN 0.9 Zero Imputation
11 Neural Networks NaN 0.999 Zero Imputation
12 Neural Networks NaN False Zero Imputation
13 Neural Networks NaN 0.0 Zero Imputation
14 Neural Networks NaN (100,) Zero Imputation
15 Neural Networks NaN constant Zero Imputation
16 Neural Networks NaN 0.001 Zero Imputation
17 Neural Networks NaN 15000 Zero Imputation
18 Neural Networks NaN 200 Zero Imputation
19 Neural Networks NaN 0.9 Zero Imputation
20 Neural Networks NaN 10 Zero Imputation
21 Neural Networks NaN True Zero Imputation
22 Neural Networks NaN 0.5 Zero Imputation
23 Neural Networks NaN None Zero Imputation
24 Neural Networks NaN True Zero Imputation
25 Neural Networks NaN adam Zero Imputation
26 Neural Networks NaN 0.0001 Zero Imputation
27 Neural Networks NaN 0.1 Zero Imputation
28 Neural Networks NaN False Zero Imputation
29 Neural Networks NaN False Zero Imputation
30 Neural Networks 0.776139 NaN Zero Imputation
31 Neural Networks 0.087740 NaN Zero Imputation
32 Neural Networks 0.445122 NaN Zero Imputation
33 Neural Networks 0.146586 NaN Zero Imputation
34 Neural Networks 0.631489 NaN Zero Imputation
35 Neural Networks 0.244377 NaN Zero Imputation
36 Neural Networks 0.262979 NaN Zero Imputation
37 Neural Networks NaN relu Zero Imputation
38 Neural Networks NaN 0.0001 Zero Imputation
39 Neural Networks NaN auto Zero Imputation
40 Neural Networks NaN 0.9 Zero Imputation
41 Neural Networks NaN 0.999 Zero Imputation
42 Neural Networks NaN False Zero Imputation
43 Neural Networks NaN 0.0 Zero Imputation
44 Neural Networks NaN (100,) Zero Imputation
45 Neural Networks NaN constant Zero Imputation
46 Neural Networks NaN 0.001 Zero Imputation
47 Neural Networks NaN 15000 Zero Imputation
48 Neural Networks NaN 200 Zero Imputation
49 Neural Networks NaN 0.9 Zero Imputation
50 Neural Networks NaN 10 Zero Imputation
51 Neural Networks NaN True Zero Imputation
52 Neural Networks NaN 0.5 Zero Imputation
53 Neural Networks NaN None Zero Imputation
54 Neural Networks NaN True Zero Imputation
55 Neural Networks NaN adam Zero Imputation
56 Neural Networks NaN 0.0001 Zero Imputation
57 Neural Networks NaN 0.1 Zero Imputation
58 Neural Networks NaN False Zero Imputation
59 Neural Networks NaN False Zero Imputation
60 Neural Networks 0.785093 NaN Zero Imputation
61 Neural Networks 0.102402 NaN Zero Imputation
62 Neural Networks 0.433155 NaN Zero Imputation
63 Neural Networks 0.165644 NaN Zero Imputation
64 Neural Networks 0.646584 NaN Zero Imputation
65 Neural Networks 0.260207 NaN Zero Imputation
66 Neural Networks 0.293168 NaN Zero Imputation
67 Neural Networks NaN relu Zero Imputation
68 Neural Networks NaN 0.0001 Zero Imputation
69 Neural Networks NaN auto Zero Imputation
70 Neural Networks NaN 0.9 Zero Imputation
71 Neural Networks NaN 0.999 Zero Imputation
72 Neural Networks NaN False Zero Imputation
73 Neural Networks NaN 0.0 Zero Imputation
74 Neural Networks NaN (100,) Zero Imputation
75 Neural Networks NaN constant Zero Imputation
76 Neural Networks NaN 0.001 Zero Imputation
77 Neural Networks NaN 15000 Zero Imputation
78 Neural Networks NaN 200 Zero Imputation
79 Neural Networks NaN 0.9 Zero Imputation
80 Neural Networks NaN 10 Zero Imputation
81 Neural Networks NaN True Zero Imputation
82 Neural Networks NaN 0.5 Zero Imputation
83 Neural Networks NaN None Zero Imputation
84 Neural Networks NaN True Zero Imputation
85 Neural Networks NaN adam Zero Imputation
86 Neural Networks NaN 0.0001 Zero Imputation
87 Neural Networks NaN 0.1 Zero Imputation
88 Neural Networks NaN False Zero Imputation
89 Neural Networks NaN False Zero Imputation
90 Neural Networks 0.801370 NaN Zero Imputation
91 Neural Networks 0.086053 NaN Zero Imputation
92 Neural Networks 0.295918 NaN Zero Imputation
93 Neural Networks 0.133333 NaN Zero Imputation
94 Neural Networks 0.621961 NaN Zero Imputation
95 Neural Networks 0.225437 NaN Zero Imputation
96 Neural Networks 0.243921 NaN Zero Imputation
97 Neural Networks NaN relu Zero Imputation
98 Neural Networks NaN 0.0001 Zero Imputation
99 Neural Networks NaN auto Zero Imputation
100 Neural Networks NaN 0.9 Zero Imputation
101 Neural Networks NaN 0.999 Zero Imputation
102 Neural Networks NaN False Zero Imputation
103 Neural Networks NaN 0.0 Zero Imputation
104 Neural Networks NaN (100,) Zero Imputation
105 Neural Networks NaN constant Zero Imputation
106 Neural Networks NaN 0.001 Zero Imputation
107 Neural Networks NaN 15000 Zero Imputation
108 Neural Networks NaN 200 Zero Imputation
109 Neural Networks NaN 0.9 Zero Imputation
110 Neural Networks NaN 10 Zero Imputation
111 Neural Networks NaN True Zero Imputation
112 Neural Networks NaN 0.5 Zero Imputation
113 Neural Networks NaN None Zero Imputation
114 Neural Networks NaN True Zero Imputation
115 Neural Networks NaN adam Zero Imputation
116 Neural Networks NaN 0.0001 Zero Imputation
117 Neural Networks NaN 0.1 Zero Imputation
118 Neural Networks NaN False Zero Imputation
119 Neural Networks NaN False Zero Imputation
120 Neural Networks 0.796365 NaN Zero Imputation
121 Neural Networks 0.101574 NaN Zero Imputation
122 Neural Networks 0.328704 NaN Zero Imputation
123 Neural Networks 0.155191 NaN Zero Imputation
124 Neural Networks 0.648106 NaN Zero Imputation
125 Neural Networks 0.294082 NaN Zero Imputation
126 Neural Networks 0.296212 NaN Zero Imputation
127 Neural Networks NaN relu Zero Imputation
128 Neural Networks NaN 0.0001 Zero Imputation
129 Neural Networks NaN auto Zero Imputation
130 Neural Networks NaN 0.9 Zero Imputation
131 Neural Networks NaN 0.999 Zero Imputation
132 Neural Networks NaN False Zero Imputation
133 Neural Networks NaN 0.0 Zero Imputation
134 Neural Networks NaN (100,) Zero Imputation
135 Neural Networks NaN constant Zero Imputation
136 Neural Networks NaN 0.001 Zero Imputation
137 Neural Networks NaN 15000 Zero Imputation
138 Neural Networks NaN 200 Zero Imputation
139 Neural Networks NaN 0.9 Zero Imputation
140 Neural Networks NaN 10 Zero Imputation
141 Neural Networks NaN True Zero Imputation
142 Neural Networks NaN 0.5 Zero Imputation
143 Neural Networks NaN None Zero Imputation
144 Neural Networks NaN True Zero Imputation
145 Neural Networks NaN adam Zero Imputation
146 Neural Networks NaN 0.0001 Zero Imputation
147 Neural Networks NaN 0.1 Zero Imputation
148 Neural Networks NaN False Zero Imputation
149 Neural Networks NaN False Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
95 Hybrid Sampling (SMOTETomek)
96 Hybrid Sampling (SMOTETomek)
97 Hybrid Sampling (SMOTETomek)
98 Hybrid Sampling (SMOTETomek)
99 Hybrid Sampling (SMOTETomek)
100 Hybrid Sampling (SMOTETomek)
101 Hybrid Sampling (SMOTETomek)
102 Hybrid Sampling (SMOTETomek)
103 Hybrid Sampling (SMOTETomek)
104 Hybrid Sampling (SMOTETomek)
105 Hybrid Sampling (SMOTETomek)
106 Hybrid Sampling (SMOTETomek)
107 Hybrid Sampling (SMOTETomek)
108 Hybrid Sampling (SMOTETomek)
109 Hybrid Sampling (SMOTETomek)
110 Hybrid Sampling (SMOTETomek)
111 Hybrid Sampling (SMOTETomek)
112 Hybrid Sampling (SMOTETomek)
113 Hybrid Sampling (SMOTETomek)
114 Hybrid Sampling (SMOTETomek)
115 Hybrid Sampling (SMOTETomek)
116 Hybrid Sampling (SMOTETomek)
117 Hybrid Sampling (SMOTETomek)
118 Hybrid Sampling (SMOTETomek)
119 Hybrid Sampling (SMOTETomek)
120 Hybrid Sampling (SMOTETomek)
121 Hybrid Sampling (SMOTETomek)
122 Hybrid Sampling (SMOTETomek)
123 Hybrid Sampling (SMOTETomek)
124 Hybrid Sampling (SMOTETomek)
125 Hybrid Sampling (SMOTETomek)
126 Hybrid Sampling (SMOTETomek)
127 Hybrid Sampling (SMOTETomek)
128 Hybrid Sampling (SMOTETomek)
129 Hybrid Sampling (SMOTETomek)
130 Hybrid Sampling (SMOTETomek)
131 Hybrid Sampling (SMOTETomek)
132 Hybrid Sampling (SMOTETomek)
133 Hybrid Sampling (SMOTETomek)
134 Hybrid Sampling (SMOTETomek)
135 Hybrid Sampling (SMOTETomek)
136 Hybrid Sampling (SMOTETomek)
137 Hybrid Sampling (SMOTETomek)
138 Hybrid Sampling (SMOTETomek)
139 Hybrid Sampling (SMOTETomek)
140 Hybrid Sampling (SMOTETomek)
141 Hybrid Sampling (SMOTETomek)
142 Hybrid Sampling (SMOTETomek)
143 Hybrid Sampling (SMOTETomek)
144 Hybrid Sampling (SMOTETomek)
145 Hybrid Sampling (SMOTETomek)
146 Hybrid Sampling (SMOTETomek)
147 Hybrid Sampling (SMOTETomek)
148 Hybrid Sampling (SMOTETomek)
149 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
1 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
2 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
3 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
4 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
5 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
6 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
7 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
8 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
9 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
10 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
11 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
12 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
13 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
14 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
15 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
16 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
17 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
18 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
19 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
20 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
21 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
22 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
23 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
24 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
25 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
26 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
27 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
28 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
29 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
30 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
31 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
32 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
33 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
34 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
35 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
36 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
37 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
38 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
39 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
40 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
41 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
42 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
43 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
44 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
45 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
46 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
47 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
48 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
49 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
50 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
51 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
52 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
53 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
54 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
55 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
56 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
57 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
58 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
59 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
60 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
61 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
62 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
63 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
64 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
65 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
66 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
67 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
68 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
69 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
70 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
71 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
72 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
73 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
74 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
75 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
76 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
77 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
78 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
79 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
80 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
81 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
82 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
83 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
84 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
85 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
86 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
87 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
88 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
89 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
90 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
91 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
92 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
93 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
94 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
95 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
96 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
97 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
98 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
99 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
100 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
101 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
102 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
103 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
104 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
105 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
106 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
107 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
108 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
109 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
110 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
111 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
112 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
113 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
114 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
115 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
116 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
117 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
118 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
119 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
120 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
121 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
122 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
123 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
124 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
125 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
126 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
127 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
128 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
129 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
130 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
131 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
132 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
133 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
134 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
135 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
136 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
137 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
138 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
139 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
140 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
141 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
142 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
143 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
144 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
145 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
146 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
147 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
148 Neural Networks_Hybrid Sampling (SMOTETomek)_Z...
149 Neural Networks_Hybrid Sampling (SMOTETomek)_Z... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Neural Networks 0.825652 NaN Mean Imputation
1 Neural Networks 0.112933 NaN Mean Imputation
2 Neural Networks 0.261603 NaN Mean Imputation
3 Neural Networks 0.157761 NaN Mean Imputation
4 Neural Networks 0.637041 NaN Mean Imputation
5 Neural Networks 0.248500 NaN Mean Imputation
6 Neural Networks 0.274083 NaN Mean Imputation
7 Neural Networks NaN relu Mean Imputation
8 Neural Networks NaN 0.0001 Mean Imputation
9 Neural Networks NaN auto Mean Imputation
10 Neural Networks NaN 0.9 Mean Imputation
11 Neural Networks NaN 0.999 Mean Imputation
12 Neural Networks NaN False Mean Imputation
13 Neural Networks NaN 0.0 Mean Imputation
14 Neural Networks NaN (100,) Mean Imputation
15 Neural Networks NaN constant Mean Imputation
16 Neural Networks NaN 0.001 Mean Imputation
17 Neural Networks NaN 15000 Mean Imputation
18 Neural Networks NaN 200 Mean Imputation
19 Neural Networks NaN 0.9 Mean Imputation
20 Neural Networks NaN 10 Mean Imputation
21 Neural Networks NaN True Mean Imputation
22 Neural Networks NaN 0.5 Mean Imputation
23 Neural Networks NaN None Mean Imputation
24 Neural Networks NaN True Mean Imputation
25 Neural Networks NaN adam Mean Imputation
26 Neural Networks NaN 0.0001 Mean Imputation
27 Neural Networks NaN 0.1 Mean Imputation
28 Neural Networks NaN False Mean Imputation
29 Neural Networks NaN False Mean Imputation
30 Neural Networks 0.929681 NaN Mean Imputation
31 Neural Networks 0.088000 NaN Mean Imputation
32 Neural Networks 0.067073 NaN Mean Imputation
33 Neural Networks 0.076125 NaN Mean Imputation
34 Neural Networks 0.649786 NaN Mean Imputation
35 Neural Networks 0.286332 NaN Mean Imputation
36 Neural Networks 0.299571 NaN Mean Imputation
37 Neural Networks NaN relu Mean Imputation
38 Neural Networks NaN 0.0001 Mean Imputation
39 Neural Networks NaN auto Mean Imputation
40 Neural Networks NaN 0.9 Mean Imputation
41 Neural Networks NaN 0.999 Mean Imputation
42 Neural Networks NaN False Mean Imputation
43 Neural Networks NaN 0.0 Mean Imputation
44 Neural Networks NaN (100,) Mean Imputation
45 Neural Networks NaN constant Mean Imputation
46 Neural Networks NaN 0.001 Mean Imputation
47 Neural Networks NaN 15000 Mean Imputation
48 Neural Networks NaN 200 Mean Imputation
49 Neural Networks NaN 0.9 Mean Imputation
50 Neural Networks NaN 10 Mean Imputation
51 Neural Networks NaN True Mean Imputation
52 Neural Networks NaN 0.5 Mean Imputation
53 Neural Networks NaN None Mean Imputation
54 Neural Networks NaN True Mean Imputation
55 Neural Networks NaN adam Mean Imputation
56 Neural Networks NaN 0.0001 Mean Imputation
57 Neural Networks NaN 0.1 Mean Imputation
58 Neural Networks NaN False Mean Imputation
59 Neural Networks NaN False Mean Imputation
60 Neural Networks 0.676060 NaN Mean Imputation
61 Neural Networks 0.077048 NaN Mean Imputation
62 Neural Networks 0.508021 NaN Mean Imputation
63 Neural Networks 0.133803 NaN Mean Imputation
64 Neural Networks 0.645434 NaN Mean Imputation
65 Neural Networks 0.237955 NaN Mean Imputation
66 Neural Networks 0.290868 NaN Mean Imputation
67 Neural Networks NaN relu Mean Imputation
68 Neural Networks NaN 0.0001 Mean Imputation
69 Neural Networks NaN auto Mean Imputation
70 Neural Networks NaN 0.9 Mean Imputation
71 Neural Networks NaN 0.999 Mean Imputation
72 Neural Networks NaN False Mean Imputation
73 Neural Networks NaN 0.0 Mean Imputation
74 Neural Networks NaN (100,) Mean Imputation
75 Neural Networks NaN constant Mean Imputation
76 Neural Networks NaN 0.001 Mean Imputation
77 Neural Networks NaN 15000 Mean Imputation
78 Neural Networks NaN 200 Mean Imputation
79 Neural Networks NaN 0.9 Mean Imputation
80 Neural Networks NaN 10 Mean Imputation
81 Neural Networks NaN True Mean Imputation
82 Neural Networks NaN 0.5 Mean Imputation
83 Neural Networks NaN None Mean Imputation
84 Neural Networks NaN True Mean Imputation
85 Neural Networks NaN adam Mean Imputation
86 Neural Networks NaN 0.0001 Mean Imputation
87 Neural Networks NaN 0.1 Mean Imputation
88 Neural Networks NaN False Mean Imputation
89 Neural Networks NaN False Mean Imputation
90 Neural Networks 0.903056 NaN Mean Imputation
91 Neural Networks 0.070000 NaN Mean Imputation
92 Neural Networks 0.071429 NaN Mean Imputation
93 Neural Networks 0.070707 NaN Mean Imputation
94 Neural Networks 0.630675 NaN Mean Imputation
95 Neural Networks 0.226967 NaN Mean Imputation
96 Neural Networks 0.261351 NaN Mean Imputation
97 Neural Networks NaN relu Mean Imputation
98 Neural Networks NaN 0.0001 Mean Imputation
99 Neural Networks NaN auto Mean Imputation
100 Neural Networks NaN 0.9 Mean Imputation
101 Neural Networks NaN 0.999 Mean Imputation
102 Neural Networks NaN False Mean Imputation
103 Neural Networks NaN 0.0 Mean Imputation
104 Neural Networks NaN (100,) Mean Imputation
105 Neural Networks NaN constant Mean Imputation
106 Neural Networks NaN 0.001 Mean Imputation
107 Neural Networks NaN 15000 Mean Imputation
108 Neural Networks NaN 200 Mean Imputation
109 Neural Networks NaN 0.9 Mean Imputation
110 Neural Networks NaN 10 Mean Imputation
111 Neural Networks NaN True Mean Imputation
112 Neural Networks NaN 0.5 Mean Imputation
113 Neural Networks NaN None Mean Imputation
114 Neural Networks NaN True Mean Imputation
115 Neural Networks NaN adam Mean Imputation
116 Neural Networks NaN 0.0001 Mean Imputation
117 Neural Networks NaN 0.1 Mean Imputation
118 Neural Networks NaN False Mean Imputation
119 Neural Networks NaN False Mean Imputation
120 Neural Networks 0.712856 NaN Mean Imputation
121 Neural Networks 0.094620 NaN Mean Imputation
122 Neural Networks 0.472222 NaN Mean Imputation
123 Neural Networks 0.157651 NaN Mean Imputation
124 Neural Networks 0.633189 NaN Mean Imputation
125 Neural Networks 0.210035 NaN Mean Imputation
126 Neural Networks 0.266378 NaN Mean Imputation
127 Neural Networks NaN relu Mean Imputation
128 Neural Networks NaN 0.0001 Mean Imputation
129 Neural Networks NaN auto Mean Imputation
130 Neural Networks NaN 0.9 Mean Imputation
131 Neural Networks NaN 0.999 Mean Imputation
132 Neural Networks NaN False Mean Imputation
133 Neural Networks NaN 0.0 Mean Imputation
134 Neural Networks NaN (100,) Mean Imputation
135 Neural Networks NaN constant Mean Imputation
136 Neural Networks NaN 0.001 Mean Imputation
137 Neural Networks NaN 15000 Mean Imputation
138 Neural Networks NaN 200 Mean Imputation
139 Neural Networks NaN 0.9 Mean Imputation
140 Neural Networks NaN 10 Mean Imputation
141 Neural Networks NaN True Mean Imputation
142 Neural Networks NaN 0.5 Mean Imputation
143 Neural Networks NaN None Mean Imputation
144 Neural Networks NaN True Mean Imputation
145 Neural Networks NaN adam Mean Imputation
146 Neural Networks NaN 0.0001 Mean Imputation
147 Neural Networks NaN 0.1 Mean Imputation
148 Neural Networks NaN False Mean Imputation
149 Neural Networks NaN False Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
95 Hybrid Sampling (SMOTETomek)
96 Hybrid Sampling (SMOTETomek)
97 Hybrid Sampling (SMOTETomek)
98 Hybrid Sampling (SMOTETomek)
99 Hybrid Sampling (SMOTETomek)
100 Hybrid Sampling (SMOTETomek)
101 Hybrid Sampling (SMOTETomek)
102 Hybrid Sampling (SMOTETomek)
103 Hybrid Sampling (SMOTETomek)
104 Hybrid Sampling (SMOTETomek)
105 Hybrid Sampling (SMOTETomek)
106 Hybrid Sampling (SMOTETomek)
107 Hybrid Sampling (SMOTETomek)
108 Hybrid Sampling (SMOTETomek)
109 Hybrid Sampling (SMOTETomek)
110 Hybrid Sampling (SMOTETomek)
111 Hybrid Sampling (SMOTETomek)
112 Hybrid Sampling (SMOTETomek)
113 Hybrid Sampling (SMOTETomek)
114 Hybrid Sampling (SMOTETomek)
115 Hybrid Sampling (SMOTETomek)
116 Hybrid Sampling (SMOTETomek)
117 Hybrid Sampling (SMOTETomek)
118 Hybrid Sampling (SMOTETomek)
119 Hybrid Sampling (SMOTETomek)
120 Hybrid Sampling (SMOTETomek)
121 Hybrid Sampling (SMOTETomek)
122 Hybrid Sampling (SMOTETomek)
123 Hybrid Sampling (SMOTETomek)
124 Hybrid Sampling (SMOTETomek)
125 Hybrid Sampling (SMOTETomek)
126 Hybrid Sampling (SMOTETomek)
127 Hybrid Sampling (SMOTETomek)
128 Hybrid Sampling (SMOTETomek)
129 Hybrid Sampling (SMOTETomek)
130 Hybrid Sampling (SMOTETomek)
131 Hybrid Sampling (SMOTETomek)
132 Hybrid Sampling (SMOTETomek)
133 Hybrid Sampling (SMOTETomek)
134 Hybrid Sampling (SMOTETomek)
135 Hybrid Sampling (SMOTETomek)
136 Hybrid Sampling (SMOTETomek)
137 Hybrid Sampling (SMOTETomek)
138 Hybrid Sampling (SMOTETomek)
139 Hybrid Sampling (SMOTETomek)
140 Hybrid Sampling (SMOTETomek)
141 Hybrid Sampling (SMOTETomek)
142 Hybrid Sampling (SMOTETomek)
143 Hybrid Sampling (SMOTETomek)
144 Hybrid Sampling (SMOTETomek)
145 Hybrid Sampling (SMOTETomek)
146 Hybrid Sampling (SMOTETomek)
147 Hybrid Sampling (SMOTETomek)
148 Hybrid Sampling (SMOTETomek)
149 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
1 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
2 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
3 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
4 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
5 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
6 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
7 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
8 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
9 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
10 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
11 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
12 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
13 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
14 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
15 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
16 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
17 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
18 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
19 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
20 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
21 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
22 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
23 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
24 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
25 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
26 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
27 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
28 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
29 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
30 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
31 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
32 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
33 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
34 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
35 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
36 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
37 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
38 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
39 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
40 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
41 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
42 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
43 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
44 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
45 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
46 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
47 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
48 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
49 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
50 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
51 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
52 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
53 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
54 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
55 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
56 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
57 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
58 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
59 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
60 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
61 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
62 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
63 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
64 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
65 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
66 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
67 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
68 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
69 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
70 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
71 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
72 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
73 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
74 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
75 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
76 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
77 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
78 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
79 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
80 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
81 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
82 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
83 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
84 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
85 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
86 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
87 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
88 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
89 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
90 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
91 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
92 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
93 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
94 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
95 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
96 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
97 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
98 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
99 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
100 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
101 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
102 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
103 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
104 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
105 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
106 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
107 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
108 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
109 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
110 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
111 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
112 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
113 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
114 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
115 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
116 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
117 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
118 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
119 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
120 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
121 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
122 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
123 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
124 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
125 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
126 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
127 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
128 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
129 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
130 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
131 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
132 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
133 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
134 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
135 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
136 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
137 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
138 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
139 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
140 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
141 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
142 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
143 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
144 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
145 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
146 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
147 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
148 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
149 Neural Networks_Hybrid Sampling (SMOTETomek)_M... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Neural Networks 0.654991 NaN Median Imputation
1 Neural Networks 0.089518 NaN Median Imputation
2 Neural Networks 0.493671 NaN Median Imputation
3 Neural Networks 0.151554 NaN Median Imputation
4 Neural Networks 0.602210 NaN Median Imputation
5 Neural Networks 0.189032 NaN Median Imputation
6 Neural Networks 0.204421 NaN Median Imputation
7 Neural Networks NaN relu Median Imputation
8 Neural Networks NaN 0.0001 Median Imputation
9 Neural Networks NaN auto Median Imputation
10 Neural Networks NaN 0.9 Median Imputation
11 Neural Networks NaN 0.999 Median Imputation
12 Neural Networks NaN False Median Imputation
13 Neural Networks NaN 0.0 Median Imputation
14 Neural Networks NaN (100,) Median Imputation
15 Neural Networks NaN constant Median Imputation
16 Neural Networks NaN 0.001 Median Imputation
17 Neural Networks NaN 15000 Median Imputation
18 Neural Networks NaN 200 Median Imputation
19 Neural Networks NaN 0.9 Median Imputation
20 Neural Networks NaN 10 Median Imputation
21 Neural Networks NaN True Median Imputation
22 Neural Networks NaN 0.5 Median Imputation
23 Neural Networks NaN None Median Imputation
24 Neural Networks NaN True Median Imputation
25 Neural Networks NaN adam Median Imputation
26 Neural Networks NaN 0.0001 Median Imputation
27 Neural Networks NaN 0.1 Median Imputation
28 Neural Networks NaN False Median Imputation
29 Neural Networks NaN False Median Imputation
30 Neural Networks 0.666316 NaN Median Imputation
31 Neural Networks 0.066090 NaN Median Imputation
32 Neural Networks 0.512195 NaN Median Imputation
33 Neural Networks 0.117073 NaN Median Imputation
34 Neural Networks 0.620379 NaN Median Imputation
35 Neural Networks 0.200919 NaN Median Imputation
36 Neural Networks 0.240759 NaN Median Imputation
37 Neural Networks NaN relu Median Imputation
38 Neural Networks NaN 0.0001 Median Imputation
39 Neural Networks NaN auto Median Imputation
40 Neural Networks NaN 0.9 Median Imputation
41 Neural Networks NaN 0.999 Median Imputation
42 Neural Networks NaN False Median Imputation
43 Neural Networks NaN 0.0 Median Imputation
44 Neural Networks NaN (100,) Median Imputation
45 Neural Networks NaN constant Median Imputation
46 Neural Networks NaN 0.001 Median Imputation
47 Neural Networks NaN 15000 Median Imputation
48 Neural Networks NaN 200 Median Imputation
49 Neural Networks NaN 0.9 Median Imputation
50 Neural Networks NaN 10 Median Imputation
51 Neural Networks NaN True Median Imputation
52 Neural Networks NaN 0.5 Median Imputation
53 Neural Networks NaN None Median Imputation
54 Neural Networks NaN True Median Imputation
55 Neural Networks NaN adam Median Imputation
56 Neural Networks NaN 0.0001 Median Imputation
57 Neural Networks NaN 0.1 Median Imputation
58 Neural Networks NaN False Median Imputation
59 Neural Networks NaN False Median Imputation
60 Neural Networks 0.848828 NaN Median Imputation
61 Neural Networks 0.094340 NaN Median Imputation
62 Neural Networks 0.240642 NaN Median Imputation
63 Neural Networks 0.135542 NaN Median Imputation
64 Neural Networks 0.645083 NaN Median Imputation
65 Neural Networks 0.271855 NaN Median Imputation
66 Neural Networks 0.290167 NaN Median Imputation
67 Neural Networks NaN relu Median Imputation
68 Neural Networks NaN 0.0001 Median Imputation
69 Neural Networks NaN auto Median Imputation
70 Neural Networks NaN 0.9 Median Imputation
71 Neural Networks NaN 0.999 Median Imputation
72 Neural Networks NaN False Median Imputation
73 Neural Networks NaN 0.0 Median Imputation
74 Neural Networks NaN (100,) Median Imputation
75 Neural Networks NaN constant Median Imputation
76 Neural Networks NaN 0.001 Median Imputation
77 Neural Networks NaN 15000 Median Imputation
78 Neural Networks NaN 200 Median Imputation
79 Neural Networks NaN 0.9 Median Imputation
80 Neural Networks NaN 10 Median Imputation
81 Neural Networks NaN True Median Imputation
82 Neural Networks NaN 0.5 Median Imputation
83 Neural Networks NaN None Median Imputation
84 Neural Networks NaN True Median Imputation
85 Neural Networks NaN adam Median Imputation
86 Neural Networks NaN 0.0001 Median Imputation
87 Neural Networks NaN 0.1 Median Imputation
88 Neural Networks NaN False Median Imputation
89 Neural Networks NaN False Median Imputation
90 Neural Networks 0.834826 NaN Median Imputation
91 Neural Networks 0.084778 NaN Median Imputation
92 Neural Networks 0.224490 NaN Median Imputation
93 Neural Networks 0.123077 NaN Median Imputation
94 Neural Networks 0.628404 NaN Median Imputation
95 Neural Networks 0.238390 NaN Median Imputation
96 Neural Networks 0.256808 NaN Median Imputation
97 Neural Networks NaN relu Median Imputation
98 Neural Networks NaN 0.0001 Median Imputation
99 Neural Networks NaN auto Median Imputation
100 Neural Networks NaN 0.9 Median Imputation
101 Neural Networks NaN 0.999 Median Imputation
102 Neural Networks NaN False Median Imputation
103 Neural Networks NaN 0.0 Median Imputation
104 Neural Networks NaN (100,) Median Imputation
105 Neural Networks NaN constant Median Imputation
106 Neural Networks NaN 0.001 Median Imputation
107 Neural Networks NaN 15000 Median Imputation
108 Neural Networks NaN 200 Median Imputation
109 Neural Networks NaN 0.9 Median Imputation
110 Neural Networks NaN 10 Median Imputation
111 Neural Networks NaN True Median Imputation
112 Neural Networks NaN 0.5 Median Imputation
113 Neural Networks NaN None Median Imputation
114 Neural Networks NaN True Median Imputation
115 Neural Networks NaN adam Median Imputation
116 Neural Networks NaN 0.0001 Median Imputation
117 Neural Networks NaN 0.1 Median Imputation
118 Neural Networks NaN False Median Imputation
119 Neural Networks NaN False Median Imputation
120 Neural Networks 0.569810 NaN Median Imputation
121 Neural Networks 0.075494 NaN Median Imputation
122 Neural Networks 0.583333 NaN Median Imputation
123 Neural Networks 0.133687 NaN Median Imputation
124 Neural Networks 0.629997 NaN Median Imputation
125 Neural Networks 0.204702 NaN Median Imputation
126 Neural Networks 0.259994 NaN Median Imputation
127 Neural Networks NaN relu Median Imputation
128 Neural Networks NaN 0.0001 Median Imputation
129 Neural Networks NaN auto Median Imputation
130 Neural Networks NaN 0.9 Median Imputation
131 Neural Networks NaN 0.999 Median Imputation
132 Neural Networks NaN False Median Imputation
133 Neural Networks NaN 0.0 Median Imputation
134 Neural Networks NaN (100,) Median Imputation
135 Neural Networks NaN constant Median Imputation
136 Neural Networks NaN 0.001 Median Imputation
137 Neural Networks NaN 15000 Median Imputation
138 Neural Networks NaN 200 Median Imputation
139 Neural Networks NaN 0.9 Median Imputation
140 Neural Networks NaN 10 Median Imputation
141 Neural Networks NaN True Median Imputation
142 Neural Networks NaN 0.5 Median Imputation
143 Neural Networks NaN None Median Imputation
144 Neural Networks NaN True Median Imputation
145 Neural Networks NaN adam Median Imputation
146 Neural Networks NaN 0.0001 Median Imputation
147 Neural Networks NaN 0.1 Median Imputation
148 Neural Networks NaN False Median Imputation
149 Neural Networks NaN False Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
95 Hybrid Sampling (SMOTETomek)
96 Hybrid Sampling (SMOTETomek)
97 Hybrid Sampling (SMOTETomek)
98 Hybrid Sampling (SMOTETomek)
99 Hybrid Sampling (SMOTETomek)
100 Hybrid Sampling (SMOTETomek)
101 Hybrid Sampling (SMOTETomek)
102 Hybrid Sampling (SMOTETomek)
103 Hybrid Sampling (SMOTETomek)
104 Hybrid Sampling (SMOTETomek)
105 Hybrid Sampling (SMOTETomek)
106 Hybrid Sampling (SMOTETomek)
107 Hybrid Sampling (SMOTETomek)
108 Hybrid Sampling (SMOTETomek)
109 Hybrid Sampling (SMOTETomek)
110 Hybrid Sampling (SMOTETomek)
111 Hybrid Sampling (SMOTETomek)
112 Hybrid Sampling (SMOTETomek)
113 Hybrid Sampling (SMOTETomek)
114 Hybrid Sampling (SMOTETomek)
115 Hybrid Sampling (SMOTETomek)
116 Hybrid Sampling (SMOTETomek)
117 Hybrid Sampling (SMOTETomek)
118 Hybrid Sampling (SMOTETomek)
119 Hybrid Sampling (SMOTETomek)
120 Hybrid Sampling (SMOTETomek)
121 Hybrid Sampling (SMOTETomek)
122 Hybrid Sampling (SMOTETomek)
123 Hybrid Sampling (SMOTETomek)
124 Hybrid Sampling (SMOTETomek)
125 Hybrid Sampling (SMOTETomek)
126 Hybrid Sampling (SMOTETomek)
127 Hybrid Sampling (SMOTETomek)
128 Hybrid Sampling (SMOTETomek)
129 Hybrid Sampling (SMOTETomek)
130 Hybrid Sampling (SMOTETomek)
131 Hybrid Sampling (SMOTETomek)
132 Hybrid Sampling (SMOTETomek)
133 Hybrid Sampling (SMOTETomek)
134 Hybrid Sampling (SMOTETomek)
135 Hybrid Sampling (SMOTETomek)
136 Hybrid Sampling (SMOTETomek)
137 Hybrid Sampling (SMOTETomek)
138 Hybrid Sampling (SMOTETomek)
139 Hybrid Sampling (SMOTETomek)
140 Hybrid Sampling (SMOTETomek)
141 Hybrid Sampling (SMOTETomek)
142 Hybrid Sampling (SMOTETomek)
143 Hybrid Sampling (SMOTETomek)
144 Hybrid Sampling (SMOTETomek)
145 Hybrid Sampling (SMOTETomek)
146 Hybrid Sampling (SMOTETomek)
147 Hybrid Sampling (SMOTETomek)
148 Hybrid Sampling (SMOTETomek)
149 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
1 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
2 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
3 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
4 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
5 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
6 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
7 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
8 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
9 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
10 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
11 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
12 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
13 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
14 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
15 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
16 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
17 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
18 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
19 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
20 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
21 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
22 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
23 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
24 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
25 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
26 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
27 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
28 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
29 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
30 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
31 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
32 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
33 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
34 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
35 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
36 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
37 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
38 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
39 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
40 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
41 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
42 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
43 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
44 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
45 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
46 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
47 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
48 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
49 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
50 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
51 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
52 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
53 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
54 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
55 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
56 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
57 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
58 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
59 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
60 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
61 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
62 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
63 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
64 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
65 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
66 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
67 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
68 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
69 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
70 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
71 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
72 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
73 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
74 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
75 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
76 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
77 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
78 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
79 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
80 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
81 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
82 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
83 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
84 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
85 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
86 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
87 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
88 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
89 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
90 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
91 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
92 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
93 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
94 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
95 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
96 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
97 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
98 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
99 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
100 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
101 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
102 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
103 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
104 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
105 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
106 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
107 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
108 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
109 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
110 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
111 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
112 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
113 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
114 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
115 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
116 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
117 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
118 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
119 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
120 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
121 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
122 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
123 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
124 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
125 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
126 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
127 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
128 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
129 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
130 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
131 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
132 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
133 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
134 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
135 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
136 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
137 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
138 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
139 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
140 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
141 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
142 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
143 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
144 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
145 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
146 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
147 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
148 Neural Networks_Hybrid Sampling (SMOTETomek)_M...
149 Neural Networks_Hybrid Sampling (SMOTETomek)_M... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Naive Bayes 0.223334 NaN Zero Imputation
1 Naive Bayes 0.065663 NaN Zero Imputation
2 Naive Bayes 0.864979 NaN Zero Imputation
3 Naive Bayes 0.122060 NaN Zero Imputation
4 Naive Bayes 0.525013 NaN Zero Imputation
5 Naive Bayes 0.055428 NaN Zero Imputation
6 Naive Bayes 0.050026 NaN Zero Imputation
7 Naive Bayes NaN NaN Zero Imputation
8 Naive Bayes NaN 1.000000e-09 Zero Imputation
9 Naive Bayes 0.208059 NaN Zero Imputation
10 Naive Bayes 0.045411 NaN Zero Imputation
11 Naive Bayes 0.865854 NaN Zero Imputation
12 Naive Bayes 0.086296 NaN Zero Imputation
13 Naive Bayes 0.523033 NaN Zero Imputation
14 Naive Bayes 0.058868 NaN Zero Imputation
15 Naive Bayes 0.046067 NaN Zero Imputation
16 Naive Bayes NaN NaN Zero Imputation
17 Naive Bayes NaN 1.000000e-09 Zero Imputation
18 Naive Bayes 0.214643 NaN Zero Imputation
19 Naive Bayes 0.055644 NaN Zero Imputation
20 Naive Bayes 0.935829 NaN Zero Imputation
21 Naive Bayes 0.105042 NaN Zero Imputation
22 Naive Bayes 0.561959 NaN Zero Imputation
23 Naive Bayes 0.124472 NaN Zero Imputation
24 Naive Bayes 0.123918 NaN Zero Imputation
25 Naive Bayes NaN NaN Zero Imputation
26 Naive Bayes NaN 1.000000e-09 Zero Imputation
27 Naive Bayes 0.206797 NaN Zero Imputation
28 Naive Bayes 0.055292 NaN Zero Imputation
29 Naive Bayes 0.892857 NaN Zero Imputation
30 Naive Bayes 0.104136 NaN Zero Imputation
31 Naive Bayes 0.534178 NaN Zero Imputation
32 Naive Bayes 0.070635 NaN Zero Imputation
33 Naive Bayes 0.068356 NaN Zero Imputation
34 Naive Bayes NaN NaN Zero Imputation
35 Naive Bayes NaN 1.000000e-09 Zero Imputation
36 Naive Bayes 0.212065 NaN Zero Imputation
37 Naive Bayes 0.063816 NaN Zero Imputation
38 Naive Bayes 0.939815 NaN Zero Imputation
39 Naive Bayes 0.119517 NaN Zero Imputation
40 Naive Bayes 0.557759 NaN Zero Imputation
41 Naive Bayes 0.117112 NaN Zero Imputation
42 Naive Bayes 0.115518 NaN Zero Imputation
43 Naive Bayes NaN NaN Zero Imputation
44 Naive Bayes NaN 1.000000e-09 Zero Imputation
Imbalance Class Technique Model Unique Code
0 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_0
1 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_0
2 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_0
3 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_0
4 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_0
5 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_0
6 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_0
7 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_0
8 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_0
9 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_1
10 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_1
11 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_1
12 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_1
13 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_1
14 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_1
15 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_1
16 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_1
17 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_1
18 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_2
19 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_2
20 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_2
21 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_2
22 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_2
23 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_2
24 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_2
25 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_2
26 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_2
27 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_3
28 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_3
29 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_3
30 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_3
31 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_3
32 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_3
33 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_3
34 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_3
35 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_3
36 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_4
37 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_4
38 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_4
39 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_4
40 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_4
41 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_4
42 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_4
43 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_4
44 Oversampling Naive Bayes_Oversampling_Zero Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Naive Bayes 0.214116 NaN Mean Imputation
1 Naive Bayes 0.065211 NaN Mean Imputation
2 Naive Bayes 0.869198 NaN Mean Imputation
3 Naive Bayes 0.121319 NaN Mean Imputation
4 Naive Bayes 0.520069 NaN Mean Imputation
5 Naive Bayes 0.048693 NaN Mean Imputation
6 Naive Bayes 0.040139 NaN Mean Imputation
7 Naive Bayes NaN NaN Mean Imputation
8 Naive Bayes NaN 1.000000e-09 Mean Imputation
9 Naive Bayes 0.200948 NaN Mean Imputation
10 Naive Bayes 0.044444 NaN Mean Imputation
11 Naive Bayes 0.853659 NaN Mean Imputation
12 Naive Bayes 0.084490 NaN Mean Imputation
13 Naive Bayes 0.518148 NaN Mean Imputation
14 Naive Bayes 0.061345 NaN Mean Imputation
15 Naive Bayes 0.036297 NaN Mean Imputation
16 Naive Bayes NaN NaN Mean Imputation
17 Naive Bayes NaN 1.000000e-09 Mean Imputation
18 Naive Bayes 0.205162 NaN Mean Imputation
19 Naive Bayes 0.055294 NaN Mean Imputation
20 Naive Bayes 0.941176 NaN Mean Imputation
21 Naive Bayes 0.104451 NaN Mean Imputation
22 Naive Bayes 0.553100 NaN Mean Imputation
23 Naive Bayes 0.110706 NaN Mean Imputation
24 Naive Bayes 0.106201 NaN Mean Imputation
25 Naive Bayes NaN NaN Mean Imputation
26 Naive Bayes NaN 1.000000e-09 Mean Imputation
27 Naive Bayes 0.192571 NaN Mean Imputation
28 Naive Bayes 0.054641 NaN Mean Imputation
29 Naive Bayes 0.897959 NaN Mean Imputation
30 Naive Bayes 0.103014 NaN Mean Imputation
31 Naive Bayes 0.529030 NaN Mean Imputation
32 Naive Bayes 0.061293 NaN Mean Imputation
33 Naive Bayes 0.058060 NaN Mean Imputation
34 Naive Bayes NaN NaN Mean Imputation
35 Naive Bayes NaN 1.000000e-09 Mean Imputation
36 Naive Bayes 0.202318 NaN Mean Imputation
37 Naive Bayes 0.063896 NaN Mean Imputation
38 Naive Bayes 0.953704 NaN Mean Imputation
39 Naive Bayes 0.119767 NaN Mean Imputation
40 Naive Bayes 0.559854 NaN Mean Imputation
41 Naive Bayes 0.117789 NaN Mean Imputation
42 Naive Bayes 0.119708 NaN Mean Imputation
43 Naive Bayes NaN NaN Mean Imputation
44 Naive Bayes NaN 1.000000e-09 Mean Imputation
Imbalance Class Technique Model Unique Code
0 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_0
1 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_0
2 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_0
3 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_0
4 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_0
5 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_0
6 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_0
7 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_0
8 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_0
9 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_1
10 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_1
11 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_1
12 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_1
13 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_1
14 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_1
15 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_1
16 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_1
17 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_1
18 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_2
19 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_2
20 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_2
21 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_2
22 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_2
23 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_2
24 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_2
25 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_2
26 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_2
27 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_3
28 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_3
29 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_3
30 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_3
31 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_3
32 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_3
33 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_3
34 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_3
35 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_3
36 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_4
37 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_4
38 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_4
39 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_4
40 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_4
41 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_4
42 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_4
43 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_4
44 Oversampling Naive Bayes_Oversampling_Mean Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Naive Bayes 0.217277 NaN Median Imputation
1 Naive Bayes 0.065183 NaN Median Imputation
2 Naive Bayes 0.864979 NaN Median Imputation
3 Naive Bayes 0.121230 NaN Median Imputation
4 Naive Bayes 0.523200 NaN Median Imputation
5 Naive Bayes 0.050934 NaN Median Imputation
6 Naive Bayes 0.046399 NaN Median Imputation
7 Naive Bayes NaN NaN Median Imputation
8 Naive Bayes NaN 1.000000e-09 Median Imputation
9 Naive Bayes 0.211219 NaN Median Imputation
10 Naive Bayes 0.045002 NaN Median Imputation
11 Naive Bayes 0.853659 NaN Median Imputation
12 Naive Bayes 0.085496 NaN Median Imputation
13 Naive Bayes 0.524629 NaN Median Imputation
14 Naive Bayes 0.055606 NaN Median Imputation
15 Naive Bayes 0.049257 NaN Median Imputation
16 Naive Bayes NaN NaN Median Imputation
17 Naive Bayes NaN 1.000000e-09 Median Imputation
18 Naive Bayes 0.212536 NaN Median Imputation
19 Naive Bayes 0.055503 NaN Median Imputation
20 Naive Bayes 0.935829 NaN Median Imputation
21 Naive Bayes 0.104790 NaN Median Imputation
22 Naive Bayes 0.559830 NaN Median Imputation
23 Naive Bayes 0.120039 NaN Median Imputation
24 Naive Bayes 0.119660 NaN Median Imputation
25 Naive Bayes NaN NaN Median Imputation
26 Naive Bayes NaN 1.000000e-09 Median Imputation
27 Naive Bayes 0.203635 NaN Median Imputation
28 Naive Bayes 0.055083 NaN Median Imputation
29 Naive Bayes 0.892857 NaN Median Imputation
30 Naive Bayes 0.103765 NaN Median Imputation
31 Naive Bayes 0.533959 NaN Median Imputation
32 Naive Bayes 0.069246 NaN Median Imputation
33 Naive Bayes 0.067918 NaN Median Imputation
34 Naive Bayes NaN NaN Median Imputation
35 Naive Bayes NaN 1.000000e-09 Median Imputation
36 Naive Bayes 0.208377 NaN Median Imputation
37 Naive Bayes 0.064083 NaN Median Imputation
38 Naive Bayes 0.949074 NaN Median Imputation
39 Naive Bayes 0.120059 NaN Median Imputation
40 Naive Bayes 0.559402 NaN Median Imputation
41 Naive Bayes 0.120303 NaN Median Imputation
42 Naive Bayes 0.118804 NaN Median Imputation
43 Naive Bayes NaN NaN Median Imputation
44 Naive Bayes NaN 1.000000e-09 Median Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
Model Unique Code
0 Naive Bayes_Oversampling_Median Imputation_fold_0
1 Naive Bayes_Oversampling_Median Imputation_fold_0
2 Naive Bayes_Oversampling_Median Imputation_fold_0
3 Naive Bayes_Oversampling_Median Imputation_fold_0
4 Naive Bayes_Oversampling_Median Imputation_fold_0
5 Naive Bayes_Oversampling_Median Imputation_fold_0
6 Naive Bayes_Oversampling_Median Imputation_fold_0
7 Naive Bayes_Oversampling_Median Imputation_fold_0
8 Naive Bayes_Oversampling_Median Imputation_fold_0
9 Naive Bayes_Oversampling_Median Imputation_fold_1
10 Naive Bayes_Oversampling_Median Imputation_fold_1
11 Naive Bayes_Oversampling_Median Imputation_fold_1
12 Naive Bayes_Oversampling_Median Imputation_fold_1
13 Naive Bayes_Oversampling_Median Imputation_fold_1
14 Naive Bayes_Oversampling_Median Imputation_fold_1
15 Naive Bayes_Oversampling_Median Imputation_fold_1
16 Naive Bayes_Oversampling_Median Imputation_fold_1
17 Naive Bayes_Oversampling_Median Imputation_fold_1
18 Naive Bayes_Oversampling_Median Imputation_fold_2
19 Naive Bayes_Oversampling_Median Imputation_fold_2
20 Naive Bayes_Oversampling_Median Imputation_fold_2
21 Naive Bayes_Oversampling_Median Imputation_fold_2
22 Naive Bayes_Oversampling_Median Imputation_fold_2
23 Naive Bayes_Oversampling_Median Imputation_fold_2
24 Naive Bayes_Oversampling_Median Imputation_fold_2
25 Naive Bayes_Oversampling_Median Imputation_fold_2
26 Naive Bayes_Oversampling_Median Imputation_fold_2
27 Naive Bayes_Oversampling_Median Imputation_fold_3
28 Naive Bayes_Oversampling_Median Imputation_fold_3
29 Naive Bayes_Oversampling_Median Imputation_fold_3
30 Naive Bayes_Oversampling_Median Imputation_fold_3
31 Naive Bayes_Oversampling_Median Imputation_fold_3
32 Naive Bayes_Oversampling_Median Imputation_fold_3
33 Naive Bayes_Oversampling_Median Imputation_fold_3
34 Naive Bayes_Oversampling_Median Imputation_fold_3
35 Naive Bayes_Oversampling_Median Imputation_fold_3
36 Naive Bayes_Oversampling_Median Imputation_fold_4
37 Naive Bayes_Oversampling_Median Imputation_fold_4
38 Naive Bayes_Oversampling_Median Imputation_fold_4
39 Naive Bayes_Oversampling_Median Imputation_fold_4
40 Naive Bayes_Oversampling_Median Imputation_fold_4
41 Naive Bayes_Oversampling_Median Imputation_fold_4
42 Naive Bayes_Oversampling_Median Imputation_fold_4
43 Naive Bayes_Oversampling_Median Imputation_fold_4
44 Naive Bayes_Oversampling_Median Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Naive Bayes 0.229655 NaN Zero Imputation
1 Naive Bayes 0.066452 NaN Zero Imputation
2 Naive Bayes 0.869198 NaN Zero Imputation
3 Naive Bayes 0.123464 NaN Zero Imputation
4 Naive Bayes 0.532896 NaN Zero Imputation
5 Naive Bayes 0.072844 NaN Zero Imputation
6 Naive Bayes 0.065792 NaN Zero Imputation
7 Naive Bayes NaN NaN Zero Imputation
8 Naive Bayes NaN 1.000000e-09 Zero Imputation
9 Naive Bayes 0.254938 NaN Zero Imputation
10 Naive Bayes 0.047538 NaN Zero Imputation
11 Naive Bayes 0.853659 NaN Zero Imputation
12 Naive Bayes 0.090061 NaN Zero Imputation
13 Naive Bayes 0.548922 NaN Zero Imputation
14 Naive Bayes 0.109053 NaN Zero Imputation
15 Naive Bayes 0.097845 NaN Zero Imputation
16 Naive Bayes NaN NaN Zero Imputation
17 Naive Bayes NaN 1.000000e-09 Zero Imputation
18 Naive Bayes 0.245457 NaN Zero Imputation
19 Naive Bayes 0.056329 NaN Zero Imputation
20 Naive Bayes 0.909091 NaN Zero Imputation
21 Naive Bayes 0.106084 NaN Zero Imputation
22 Naive Bayes 0.569309 NaN Zero Imputation
23 Naive Bayes 0.138923 NaN Zero Imputation
24 Naive Bayes 0.138618 NaN Zero Imputation
25 Naive Bayes NaN NaN Zero Imputation
26 Naive Bayes NaN 1.000000e-09 Zero Imputation
27 Naive Bayes 0.228398 NaN Zero Imputation
28 Naive Bayes 0.057050 NaN Zero Imputation
29 Naive Bayes 0.897959 NaN Zero Imputation
30 Naive Bayes 0.107284 NaN Zero Imputation
31 Naive Bayes 0.551027 NaN Zero Imputation
32 Naive Bayes 0.111015 NaN Zero Imputation
33 Naive Bayes 0.102055 NaN Zero Imputation
34 Naive Bayes NaN NaN Zero Imputation
35 Naive Bayes NaN 1.000000e-09 Zero Imputation
36 Naive Bayes 0.245522 NaN Zero Imputation
37 Naive Bayes 0.065617 NaN Zero Imputation
38 Naive Bayes 0.925926 NaN Zero Imputation
39 Naive Bayes 0.122549 NaN Zero Imputation
40 Naive Bayes 0.569207 NaN Zero Imputation
41 Naive Bayes 0.140451 NaN Zero Imputation
42 Naive Bayes 0.138414 NaN Zero Imputation
43 Naive Bayes NaN NaN Zero Imputation
44 Naive Bayes NaN 1.000000e-09 Zero Imputation
Imbalance Class Technique Model Unique Code
0 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_0
1 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_0
2 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_0
3 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_0
4 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_0
5 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_0
6 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_0
7 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_0
8 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_0
9 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_1
10 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_1
11 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_1
12 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_1
13 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_1
14 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_1
15 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_1
16 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_1
17 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_1
18 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_2
19 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_2
20 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_2
21 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_2
22 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_2
23 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_2
24 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_2
25 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_2
26 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_2
27 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_3
28 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_3
29 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_3
30 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_3
31 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_3
32 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_3
33 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_3
34 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_3
35 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_3
36 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_4
37 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_4
38 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_4
39 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_4
40 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_4
41 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_4
42 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_4
43 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_4
44 Undersampling Naive Bayes_Undersampling_Zero Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Naive Bayes 0.246510 NaN Mean Imputation
1 Naive Bayes 0.066997 NaN Mean Imputation
2 Naive Bayes 0.856540 NaN Mean Imputation
3 Naive Bayes 0.124273 NaN Mean Imputation
4 Naive Bayes 0.537565 NaN Mean Imputation
5 Naive Bayes 0.079831 NaN Mean Imputation
6 Naive Bayes 0.075130 NaN Mean Imputation
7 Naive Bayes NaN NaN Mean Imputation
8 Naive Bayes NaN 1.000000e-09 Mean Imputation
9 Naive Bayes 0.209376 NaN Mean Imputation
10 Naive Bayes 0.045192 NaN Mean Imputation
11 Naive Bayes 0.859756 NaN Mean Imputation
12 Naive Bayes 0.085871 NaN Mean Imputation
13 Naive Bayes 0.528474 NaN Mean Imputation
14 Naive Bayes 0.057389 NaN Mean Imputation
15 Naive Bayes 0.056947 NaN Mean Imputation
16 Naive Bayes NaN NaN Mean Imputation
17 Naive Bayes NaN 1.000000e-09 Mean Imputation
18 Naive Bayes 0.213853 NaN Mean Imputation
19 Naive Bayes 0.055591 NaN Mean Imputation
20 Naive Bayes 0.935829 NaN Mean Imputation
21 Naive Bayes 0.104948 NaN Mean Imputation
22 Naive Bayes 0.557655 NaN Mean Imputation
23 Naive Bayes 0.117077 NaN Mean Imputation
24 Naive Bayes 0.115310 NaN Mean Imputation
25 Naive Bayes NaN NaN Mean Imputation
26 Naive Bayes NaN 1.000000e-09 Mean Imputation
27 Naive Bayes 0.190991 NaN Mean Imputation
28 Naive Bayes 0.055367 NaN Mean Imputation
29 Naive Bayes 0.913265 NaN Mean Imputation
30 Naive Bayes 0.104404 NaN Mean Imputation
31 Naive Bayes 0.536050 NaN Mean Imputation
32 Naive Bayes 0.073163 NaN Mean Imputation
33 Naive Bayes 0.072100 NaN Mean Imputation
34 Naive Bayes NaN NaN Mean Imputation
35 Naive Bayes NaN 1.000000e-09 Mean Imputation
36 Naive Bayes 0.234194 NaN Mean Imputation
37 Naive Bayes 0.064985 NaN Mean Imputation
38 Naive Bayes 0.930556 NaN Mean Imputation
39 Naive Bayes 0.121487 NaN Mean Imputation
40 Naive Bayes 0.564278 NaN Mean Imputation
41 Naive Bayes 0.127566 NaN Mean Imputation
42 Naive Bayes 0.128556 NaN Mean Imputation
43 Naive Bayes NaN NaN Mean Imputation
44 Naive Bayes NaN 1.000000e-09 Mean Imputation
Imbalance Class Technique Model Unique Code
0 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_0
1 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_0
2 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_0
3 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_0
4 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_0
5 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_0
6 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_0
7 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_0
8 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_0
9 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_1
10 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_1
11 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_1
12 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_1
13 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_1
14 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_1
15 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_1
16 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_1
17 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_1
18 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_2
19 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_2
20 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_2
21 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_2
22 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_2
23 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_2
24 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_2
25 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_2
26 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_2
27 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_3
28 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_3
29 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_3
30 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_3
31 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_3
32 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_3
33 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_3
34 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_3
35 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_3
36 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_4
37 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_4
38 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_4
39 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_4
40 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_4
41 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_4
42 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_4
43 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_4
44 Undersampling Naive Bayes_Undersampling_Mean Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Naive Bayes 0.247827 NaN Median Imputation
1 Naive Bayes 0.068534 NaN Median Imputation
2 Naive Bayes 0.877637 NaN Median Imputation
3 Naive Bayes 0.127139 NaN Median Imputation
4 Naive Bayes 0.549276 NaN Median Imputation
5 Naive Bayes 0.104042 NaN Median Imputation
6 Naive Bayes 0.098552 NaN Median Imputation
7 Naive Bayes NaN NaN Median Imputation
8 Naive Bayes NaN 1.000000e-09 Median Imputation
9 Naive Bayes 0.227811 NaN Median Imputation
10 Naive Bayes 0.045634 NaN Median Imputation
11 Naive Bayes 0.847561 NaN Median Imputation
12 Naive Bayes 0.086604 NaN Median Imputation
13 Naive Bayes 0.528625 NaN Median Imputation
14 Naive Bayes 0.059698 NaN Median Imputation
15 Naive Bayes 0.057250 NaN Median Imputation
16 Naive Bayes NaN NaN Median Imputation
17 Naive Bayes NaN 1.000000e-09 Median Imputation
18 Naive Bayes 0.274691 NaN Median Imputation
19 Naive Bayes 0.056955 NaN Median Imputation
20 Naive Bayes 0.882353 NaN Median Imputation
21 Naive Bayes 0.107004 NaN Median Imputation
22 Naive Bayes 0.576260 NaN Median Imputation
23 Naive Bayes 0.150183 NaN Median Imputation
24 Naive Bayes 0.152520 NaN Median Imputation
25 Naive Bayes NaN NaN Median Imputation
26 Naive Bayes NaN 1.000000e-09 Median Imputation
27 Naive Bayes 0.207850 NaN Median Imputation
28 Naive Bayes 0.055643 NaN Median Imputation
29 Naive Bayes 0.897959 NaN Median Imputation
30 Naive Bayes 0.104793 NaN Median Imputation
31 Naive Bayes 0.539891 NaN Median Imputation
32 Naive Bayes 0.083237 NaN Median Imputation
33 Naive Bayes 0.079782 NaN Median Imputation
34 Naive Bayes NaN NaN Median Imputation
35 Naive Bayes NaN 1.000000e-09 Median Imputation
36 Naive Bayes 0.233140 NaN Median Imputation
37 Naive Bayes 0.064339 NaN Median Imputation
38 Naive Bayes 0.921296 NaN Median Imputation
39 Naive Bayes 0.120278 NaN Median Imputation
40 Naive Bayes 0.566532 NaN Median Imputation
41 Naive Bayes 0.129635 NaN Median Imputation
42 Naive Bayes 0.133063 NaN Median Imputation
43 Naive Bayes NaN NaN Median Imputation
44 Naive Bayes NaN 1.000000e-09 Median Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
Model Unique Code
0 Naive Bayes_Undersampling_Median Imputation_fo...
1 Naive Bayes_Undersampling_Median Imputation_fo...
2 Naive Bayes_Undersampling_Median Imputation_fo...
3 Naive Bayes_Undersampling_Median Imputation_fo...
4 Naive Bayes_Undersampling_Median Imputation_fo...
5 Naive Bayes_Undersampling_Median Imputation_fo...
6 Naive Bayes_Undersampling_Median Imputation_fo...
7 Naive Bayes_Undersampling_Median Imputation_fo...
8 Naive Bayes_Undersampling_Median Imputation_fo...
9 Naive Bayes_Undersampling_Median Imputation_fo...
10 Naive Bayes_Undersampling_Median Imputation_fo...
11 Naive Bayes_Undersampling_Median Imputation_fo...
12 Naive Bayes_Undersampling_Median Imputation_fo...
13 Naive Bayes_Undersampling_Median Imputation_fo...
14 Naive Bayes_Undersampling_Median Imputation_fo...
15 Naive Bayes_Undersampling_Median Imputation_fo...
16 Naive Bayes_Undersampling_Median Imputation_fo...
17 Naive Bayes_Undersampling_Median Imputation_fo...
18 Naive Bayes_Undersampling_Median Imputation_fo...
19 Naive Bayes_Undersampling_Median Imputation_fo...
20 Naive Bayes_Undersampling_Median Imputation_fo...
21 Naive Bayes_Undersampling_Median Imputation_fo...
22 Naive Bayes_Undersampling_Median Imputation_fo...
23 Naive Bayes_Undersampling_Median Imputation_fo...
24 Naive Bayes_Undersampling_Median Imputation_fo...
25 Naive Bayes_Undersampling_Median Imputation_fo...
26 Naive Bayes_Undersampling_Median Imputation_fo...
27 Naive Bayes_Undersampling_Median Imputation_fo...
28 Naive Bayes_Undersampling_Median Imputation_fo...
29 Naive Bayes_Undersampling_Median Imputation_fo...
30 Naive Bayes_Undersampling_Median Imputation_fo...
31 Naive Bayes_Undersampling_Median Imputation_fo...
32 Naive Bayes_Undersampling_Median Imputation_fo...
33 Naive Bayes_Undersampling_Median Imputation_fo...
34 Naive Bayes_Undersampling_Median Imputation_fo...
35 Naive Bayes_Undersampling_Median Imputation_fo...
36 Naive Bayes_Undersampling_Median Imputation_fo...
37 Naive Bayes_Undersampling_Median Imputation_fo...
38 Naive Bayes_Undersampling_Median Imputation_fo...
39 Naive Bayes_Undersampling_Median Imputation_fo...
40 Naive Bayes_Undersampling_Median Imputation_fo...
41 Naive Bayes_Undersampling_Median Imputation_fo...
42 Naive Bayes_Undersampling_Median Imputation_fo...
43 Naive Bayes_Undersampling_Median Imputation_fo...
44 Naive Bayes_Undersampling_Median Imputation_fo... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Naive Bayes 0.239663 NaN Zero Imputation
1 Naive Bayes 0.066710 NaN Zero Imputation
2 Naive Bayes 0.860759 NaN Zero Imputation
3 Naive Bayes 0.123824 NaN Zero Imputation
4 Naive Bayes 0.528565 NaN Zero Imputation
5 Naive Bayes 0.061596 NaN Zero Imputation
6 Naive Bayes 0.057130 NaN Zero Imputation
7 Naive Bayes NaN NaN Zero Imputation
8 Naive Bayes NaN 1.000000e-09 Zero Imputation
9 Naive Bayes 0.219910 NaN Zero Imputation
10 Naive Bayes 0.044893 NaN Zero Imputation
11 Naive Bayes 0.841463 NaN Zero Imputation
12 Naive Bayes 0.085238 NaN Zero Imputation
13 Naive Bayes 0.520338 NaN Zero Imputation
14 Naive Bayes 0.055266 NaN Zero Imputation
15 Naive Bayes 0.040676 NaN Zero Imputation
16 Naive Bayes NaN NaN Zero Imputation
17 Naive Bayes NaN 1.000000e-09 Zero Imputation
18 Naive Bayes 0.224124 NaN Zero Imputation
19 Naive Bayes 0.056003 NaN Zero Imputation
20 Naive Bayes 0.930481 NaN Zero Imputation
21 Naive Bayes 0.105647 NaN Zero Imputation
22 Naive Bayes 0.555513 NaN Zero Imputation
23 Naive Bayes 0.118570 NaN Zero Imputation
24 Naive Bayes 0.111026 NaN Zero Imputation
25 Naive Bayes NaN NaN Zero Imputation
26 Naive Bayes NaN 1.000000e-09 Zero Imputation
27 Naive Bayes 0.214436 NaN Zero Imputation
28 Naive Bayes 0.055804 NaN Zero Imputation
29 Naive Bayes 0.892857 NaN Zero Imputation
30 Naive Bayes 0.105042 NaN Zero Imputation
31 Naive Bayes 0.533795 NaN Zero Imputation
32 Naive Bayes 0.073413 NaN Zero Imputation
33 Naive Bayes 0.067591 NaN Zero Imputation
34 Naive Bayes NaN NaN Zero Imputation
35 Naive Bayes NaN 1.000000e-09 Zero Imputation
36 Naive Bayes 0.222076 NaN Zero Imputation
37 Naive Bayes 0.064311 NaN Zero Imputation
38 Naive Bayes 0.935185 NaN Zero Imputation
39 Naive Bayes 0.120346 NaN Zero Imputation
40 Naive Bayes 0.562531 NaN Zero Imputation
41 Naive Bayes 0.123174 NaN Zero Imputation
42 Naive Bayes 0.125062 NaN Zero Imputation
43 Naive Bayes NaN NaN Zero Imputation
44 Naive Bayes NaN 1.000000e-09 Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
1 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
2 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
3 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
4 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
5 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
6 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
7 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
8 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
9 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
10 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
11 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
12 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
13 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
14 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
15 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
16 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
17 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
18 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
19 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
20 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
21 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
22 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
23 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
24 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
25 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
26 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
27 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
28 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
29 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
30 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
31 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
32 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
33 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
34 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
35 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
36 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
37 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
38 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
39 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
40 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
41 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
42 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
43 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im...
44 Naive Bayes_Hybrid Sampling (SMOTEENN)_Zero Im... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Naive Bayes 0.229392 NaN Mean Imputation
1 Naive Bayes 0.066430 NaN Mean Imputation
2 Naive Bayes 0.869198 NaN Mean Imputation
3 Naive Bayes 0.123427 NaN Mean Imputation
4 Naive Bayes 0.527628 NaN Mean Imputation
5 Naive Bayes 0.059923 NaN Mean Imputation
6 Naive Bayes 0.055255 NaN Mean Imputation
7 Naive Bayes NaN NaN Mean Imputation
8 Naive Bayes NaN 1.000000e-09 Mean Imputation
9 Naive Bayes 0.214643 NaN Mean Imputation
10 Naive Bayes 0.044602 NaN Mean Imputation
11 Naive Bayes 0.841463 NaN Mean Imputation
12 Naive Bayes 0.084715 NaN Mean Imputation
13 Naive Bayes 0.517486 NaN Mean Imputation
14 Naive Bayes 0.050503 NaN Mean Imputation
15 Naive Bayes 0.034972 NaN Mean Imputation
16 Naive Bayes NaN NaN Mean Imputation
17 Naive Bayes NaN 1.000000e-09 Mean Imputation
18 Naive Bayes 0.223334 NaN Mean Imputation
19 Naive Bayes 0.055663 NaN Mean Imputation
20 Naive Bayes 0.925134 NaN Mean Imputation
21 Naive Bayes 0.105008 NaN Mean Imputation
22 Naive Bayes 0.554203 NaN Mean Imputation
23 Naive Bayes 0.115077 NaN Mean Imputation
24 Naive Bayes 0.108407 NaN Mean Imputation
25 Naive Bayes NaN NaN Mean Imputation
26 Naive Bayes NaN 1.000000e-09 Mean Imputation
27 Naive Bayes 0.209958 NaN Mean Imputation
28 Naive Bayes 0.054938 NaN Mean Imputation
29 Naive Bayes 0.882653 NaN Mean Imputation
30 Naive Bayes 0.103438 NaN Mean Imputation
31 Naive Bayes 0.527458 NaN Mean Imputation
32 Naive Bayes 0.075034 NaN Mean Imputation
33 Naive Bayes 0.054916 NaN Mean Imputation
34 Naive Bayes NaN NaN Mean Imputation
35 Naive Bayes NaN 1.000000e-09 Mean Imputation
36 Naive Bayes 0.215227 NaN Mean Imputation
37 Naive Bayes 0.063783 NaN Mean Imputation
38 Naive Bayes 0.935185 NaN Mean Imputation
39 Naive Bayes 0.119421 NaN Mean Imputation
40 Naive Bayes 0.558412 NaN Mean Imputation
41 Naive Bayes 0.115632 NaN Mean Imputation
42 Naive Bayes 0.116824 NaN Mean Imputation
43 Naive Bayes NaN NaN Mean Imputation
44 Naive Bayes NaN 1.000000e-09 Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
1 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
2 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
3 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
4 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
5 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
6 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
7 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
8 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
9 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
10 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
11 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
12 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
13 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
14 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
15 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
16 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
17 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
18 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
19 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
20 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
21 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
22 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
23 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
24 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
25 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
26 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
27 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
28 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
29 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
30 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
31 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
32 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
33 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
34 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
35 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
36 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
37 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
38 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
39 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
40 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
41 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
42 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
43 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im...
44 Naive Bayes_Hybrid Sampling (SMOTEENN)_Mean Im... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Naive Bayes 0.236766 NaN Median Imputation
1 Naive Bayes 0.066471 NaN Median Imputation
2 Naive Bayes 0.860759 NaN Median Imputation
3 Naive Bayes 0.123412 NaN Median Imputation
4 Naive Bayes 0.527737 NaN Median Imputation
5 Naive Bayes 0.059630 NaN Median Imputation
6 Naive Bayes 0.055475 NaN Median Imputation
7 Naive Bayes NaN NaN Median Imputation
8 Naive Bayes NaN 1.000000e-09 Median Imputation
9 Naive Bayes 0.216750 NaN Median Imputation
10 Naive Bayes 0.044718 NaN Median Imputation
11 Naive Bayes 0.841463 NaN Median Imputation
12 Naive Bayes 0.084923 NaN Median Imputation
13 Naive Bayes 0.518103 NaN Median Imputation
14 Naive Bayes 0.052980 NaN Median Imputation
15 Naive Bayes 0.036206 NaN Median Imputation
16 Naive Bayes NaN NaN Median Imputation
17 Naive Bayes NaN 1.000000e-09 Median Imputation
18 Naive Bayes 0.223598 NaN Median Imputation
19 Naive Bayes 0.055967 NaN Median Imputation
20 Naive Bayes 0.930481 NaN Median Imputation
21 Naive Bayes 0.105583 NaN Median Imputation
22 Naive Bayes 0.557676 NaN Median Imputation
23 Naive Bayes 0.119401 NaN Median Imputation
24 Naive Bayes 0.115351 NaN Median Imputation
25 Naive Bayes NaN NaN Median Imputation
26 Naive Bayes NaN 1.000000e-09 Median Imputation
27 Naive Bayes 0.212065 NaN Median Imputation
28 Naive Bayes 0.055644 NaN Median Imputation
29 Naive Bayes 0.892857 NaN Median Imputation
30 Naive Bayes 0.104759 NaN Median Imputation
31 Naive Bayes 0.535581 NaN Median Imputation
32 Naive Bayes 0.074524 NaN Median Imputation
33 Naive Bayes 0.071162 NaN Median Imputation
34 Naive Bayes NaN NaN Median Imputation
35 Naive Bayes NaN 1.000000e-09 Median Imputation
36 Naive Bayes 0.222603 NaN Median Imputation
37 Naive Bayes 0.064352 NaN Median Imputation
38 Naive Bayes 0.935185 NaN Median Imputation
39 Naive Bayes 0.120417 NaN Median Imputation
40 Naive Bayes 0.561485 NaN Median Imputation
41 Naive Bayes 0.124291 NaN Median Imputation
42 Naive Bayes 0.122971 NaN Median Imputation
43 Naive Bayes NaN NaN Median Imputation
44 Naive Bayes NaN 1.000000e-09 Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
1 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
2 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
3 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
4 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
5 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
6 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
7 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
8 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
9 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
10 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
11 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
12 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
13 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
14 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
15 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
16 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
17 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
18 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
19 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
20 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
21 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
22 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
23 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
24 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
25 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
26 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
27 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
28 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
29 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
30 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
31 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
32 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
33 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
34 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
35 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
36 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
37 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
38 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
39 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
40 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
41 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
42 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
43 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ...
44 Naive Bayes_Hybrid Sampling (SMOTEENN)_Median ... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Naive Bayes 0.248881 NaN Zero Imputation
1 Naive Bayes 0.066335 NaN Zero Imputation
2 Naive Bayes 0.843882 NaN Zero Imputation
3 Naive Bayes 0.123001 NaN Zero Imputation
4 Naive Bayes 0.529404 NaN Zero Imputation
5 Naive Bayes 0.062421 NaN Zero Imputation
6 Naive Bayes 0.058809 NaN Zero Imputation
7 Naive Bayes NaN NaN Zero Imputation
8 Naive Bayes NaN 1.000000e-09 Zero Imputation
9 Naive Bayes 0.229655 NaN Zero Imputation
10 Naive Bayes 0.045140 NaN Zero Imputation
11 Naive Bayes 0.835366 NaN Zero Imputation
12 Naive Bayes 0.085652 NaN Zero Imputation
13 Naive Bayes 0.522738 NaN Zero Imputation
14 Naive Bayes 0.059628 NaN Zero Imputation
15 Naive Bayes 0.045476 NaN Zero Imputation
16 Naive Bayes NaN NaN Zero Imputation
17 Naive Bayes NaN 1.000000e-09 Zero Imputation
18 Naive Bayes 0.232289 NaN Zero Imputation
19 Naive Bayes 0.055990 NaN Zero Imputation
20 Naive Bayes 0.919786 NaN Zero Imputation
21 Naive Bayes 0.105554 NaN Zero Imputation
22 Naive Bayes 0.560588 NaN Zero Imputation
23 Naive Bayes 0.120702 NaN Zero Imputation
24 Naive Bayes 0.121176 NaN Zero Imputation
25 Naive Bayes NaN NaN Zero Imputation
26 Naive Bayes NaN 1.000000e-09 Zero Imputation
27 Naive Bayes 0.220495 NaN Zero Imputation
28 Naive Bayes 0.055359 NaN Zero Imputation
29 Naive Bayes 0.877551 NaN Zero Imputation
30 Naive Bayes 0.104148 NaN Zero Imputation
31 Naive Bayes 0.532452 NaN Zero Imputation
32 Naive Bayes 0.071774 NaN Zero Imputation
33 Naive Bayes 0.064904 NaN Zero Imputation
34 Naive Bayes NaN NaN Zero Imputation
35 Naive Bayes NaN 1.000000e-09 Zero Imputation
36 Naive Bayes 0.234457 NaN Zero Imputation
37 Naive Bayes 0.064725 NaN Zero Imputation
38 Naive Bayes 0.925926 NaN Zero Imputation
39 Naive Bayes 0.120992 NaN Zero Imputation
40 Naive Bayes 0.556747 NaN Zero Imputation
41 Naive Bayes 0.121177 NaN Zero Imputation
42 Naive Bayes 0.113493 NaN Zero Imputation
43 Naive Bayes NaN NaN Zero Imputation
44 Naive Bayes NaN 1.000000e-09 Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
1 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
2 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
3 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
4 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
5 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
6 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
7 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
8 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
9 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
10 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
11 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
12 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
13 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
14 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
15 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
16 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
17 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
18 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
19 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
20 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
21 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
22 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
23 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
24 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
25 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
26 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
27 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
28 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
29 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
30 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
31 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
32 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
33 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
34 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
35 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
36 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
37 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
38 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
39 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
40 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
41 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
42 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
43 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ...
44 Naive Bayes_Hybrid Sampling (SMOTETomek)_Zero ... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Naive Bayes 0.237029 NaN Mean Imputation
1 Naive Bayes 0.066210 NaN Mean Imputation
2 Naive Bayes 0.856540 NaN Mean Imputation
3 Naive Bayes 0.122919 NaN Mean Imputation
4 Naive Bayes 0.529378 NaN Mean Imputation
5 Naive Bayes 0.063001 NaN Mean Imputation
6 Naive Bayes 0.058756 NaN Mean Imputation
7 Naive Bayes NaN NaN Mean Imputation
8 Naive Bayes NaN 1.000000e-09 Mean Imputation
9 Naive Bayes 0.220437 NaN Mean Imputation
10 Naive Bayes 0.044922 NaN Mean Imputation
11 Naive Bayes 0.841463 NaN Mean Imputation
12 Naive Bayes 0.085290 NaN Mean Imputation
13 Naive Bayes 0.519999 NaN Mean Imputation
14 Naive Bayes 0.053045 NaN Mean Imputation
15 Naive Bayes 0.039998 NaN Mean Imputation
16 Naive Bayes NaN NaN Mean Imputation
17 Naive Bayes NaN 1.000000e-09 Mean Imputation
18 Naive Bayes 0.228865 NaN Mean Imputation
19 Naive Bayes 0.055465 NaN Mean Imputation
20 Naive Bayes 0.914439 NaN Mean Imputation
21 Naive Bayes 0.104587 NaN Mean Imputation
22 Naive Bayes 0.556979 NaN Mean Imputation
23 Naive Bayes 0.116823 NaN Mean Imputation
24 Naive Bayes 0.113957 NaN Mean Imputation
25 Naive Bayes NaN NaN Mean Imputation
26 Naive Bayes NaN 1.000000e-09 Mean Imputation
27 Naive Bayes 0.215490 NaN Mean Imputation
28 Naive Bayes 0.055307 NaN Mean Imputation
29 Naive Bayes 0.882653 NaN Mean Imputation
30 Naive Bayes 0.104091 NaN Mean Imputation
31 Naive Bayes 0.530223 NaN Mean Imputation
32 Naive Bayes 0.068719 NaN Mean Imputation
33 Naive Bayes 0.060446 NaN Mean Imputation
34 Naive Bayes NaN NaN Mean Imputation
35 Naive Bayes NaN 1.000000e-09 Mean Imputation
36 Naive Bayes 0.222339 NaN Mean Imputation
37 Naive Bayes 0.062660 NaN Mean Imputation
38 Naive Bayes 0.907407 NaN Mean Imputation
39 Naive Bayes 0.117225 NaN Mean Imputation
40 Naive Bayes 0.549197 NaN Mean Imputation
41 Naive Bayes 0.096276 NaN Mean Imputation
42 Naive Bayes 0.098394 NaN Mean Imputation
43 Naive Bayes NaN NaN Mean Imputation
44 Naive Bayes NaN 1.000000e-09 Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
1 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
2 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
3 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
4 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
5 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
6 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
7 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
8 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
9 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
10 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
11 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
12 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
13 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
14 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
15 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
16 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
17 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
18 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
19 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
20 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
21 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
22 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
23 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
24 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
25 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
26 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
27 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
28 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
29 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
30 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
31 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
32 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
33 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
34 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
35 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
36 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
37 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
38 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
39 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
40 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
41 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
42 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
43 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ...
44 Naive Bayes_Hybrid Sampling (SMOTETomek)_Mean ... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Naive Bayes 0.240980 NaN Median Imputation
1 Naive Bayes 0.066251 NaN Median Imputation
2 Naive Bayes 0.852321 NaN Median Imputation
3 Naive Bayes 0.122946 NaN Median Imputation
4 Naive Bayes 0.528796 NaN Median Imputation
5 Naive Bayes 0.061590 NaN Median Imputation
6 Naive Bayes 0.057593 NaN Median Imputation
7 Naive Bayes NaN NaN Median Imputation
8 Naive Bayes NaN 1.000000e-09 Median Imputation
9 Naive Bayes 0.226231 NaN Median Imputation
10 Naive Bayes 0.044948 NaN Median Imputation
11 Naive Bayes 0.835366 NaN Median Imputation
12 Naive Bayes 0.085305 NaN Median Imputation
13 Naive Bayes 0.519713 NaN Median Imputation
14 Naive Bayes 0.052980 NaN Median Imputation
15 Naive Bayes 0.039425 NaN Median Imputation
16 Naive Bayes NaN NaN Median Imputation
17 Naive Bayes NaN 1.000000e-09 Median Imputation
18 Naive Bayes 0.230972 NaN Median Imputation
19 Naive Bayes 0.055610 NaN Median Imputation
20 Naive Bayes 0.914439 NaN Median Imputation
21 Naive Bayes 0.104844 NaN Median Imputation
22 Naive Bayes 0.557534 NaN Median Imputation
23 Naive Bayes 0.114161 NaN Median Imputation
24 Naive Bayes 0.115068 NaN Median Imputation
25 Naive Bayes NaN NaN Median Imputation
26 Naive Bayes NaN 1.000000e-09 Median Imputation
27 Naive Bayes 0.224974 NaN Median Imputation
28 Naive Bayes 0.056238 NaN Median Imputation
29 Naive Bayes 0.887755 NaN Median Imputation
30 Naive Bayes 0.105775 NaN Median Imputation
31 Naive Bayes 0.532014 NaN Median Imputation
32 Naive Bayes 0.077477 NaN Median Imputation
33 Naive Bayes 0.064028 NaN Median Imputation
34 Naive Bayes NaN NaN Median Imputation
35 Naive Bayes NaN 1.000000e-09 Median Imputation
36 Naive Bayes 0.225501 NaN Median Imputation
37 Naive Bayes 0.063462 NaN Median Imputation
38 Naive Bayes 0.916667 NaN Median Imputation
39 Naive Bayes 0.118705 NaN Median Imputation
40 Naive Bayes 0.553297 NaN Median Imputation
41 Naive Bayes 0.104055 NaN Median Imputation
42 Naive Bayes 0.106594 NaN Median Imputation
43 Naive Bayes NaN NaN Median Imputation
44 Naive Bayes NaN 1.000000e-09 Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
1 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
2 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
3 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
4 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
5 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
6 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
7 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
8 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
9 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
10 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
11 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
12 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
13 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
14 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
15 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
16 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
17 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
18 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
19 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
20 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
21 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
22 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
23 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
24 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
25 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
26 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
27 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
28 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
29 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
30 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
31 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
32 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
33 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
34 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
35 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
36 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
37 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
38 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
39 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
40 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
41 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
42 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
43 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media...
44 Naive Bayes_Hybrid Sampling (SMOTETomek)_Media... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Decision Trees 0.873848 NaN Zero Imputation
1 Decision Trees 0.104575 NaN Zero Imputation
2 Decision Trees 0.135021 NaN Zero Imputation
3 Decision Trees 0.117864 NaN Zero Imputation
4 Decision Trees 0.528177 NaN Zero Imputation
5 Decision Trees 0.058055 NaN Zero Imputation
6 Decision Trees 0.056354 NaN Zero Imputation
7 Decision Trees NaN 0.0 Zero Imputation
8 Decision Trees NaN None Zero Imputation
9 Decision Trees NaN gini Zero Imputation
10 Decision Trees NaN None Zero Imputation
11 Decision Trees NaN None Zero Imputation
12 Decision Trees NaN None Zero Imputation
13 Decision Trees NaN 0.0 Zero Imputation
14 Decision Trees NaN 1 Zero Imputation
15 Decision Trees NaN 2 Zero Imputation
16 Decision Trees NaN 0.0 Zero Imputation
17 Decision Trees NaN None Zero Imputation
18 Decision Trees NaN best Zero Imputation
19 Decision Trees 0.887016 NaN Zero Imputation
20 Decision Trees 0.084639 NaN Zero Imputation
21 Decision Trees 0.164634 NaN Zero Imputation
22 Decision Trees 0.111801 NaN Zero Imputation
23 Decision Trees 0.546918 NaN Zero Imputation
24 Decision Trees 0.100350 NaN Zero Imputation
25 Decision Trees 0.093837 NaN Zero Imputation
26 Decision Trees NaN 0.0 Zero Imputation
27 Decision Trees NaN None Zero Imputation
28 Decision Trees NaN gini Zero Imputation
29 Decision Trees NaN None Zero Imputation
30 Decision Trees NaN None Zero Imputation
31 Decision Trees NaN None Zero Imputation
32 Decision Trees NaN 0.0 Zero Imputation
33 Decision Trees NaN 1 Zero Imputation
34 Decision Trees NaN 2 Zero Imputation
35 Decision Trees NaN 0.0 Zero Imputation
36 Decision Trees NaN None Zero Imputation
37 Decision Trees NaN best Zero Imputation
38 Decision Trees 0.875165 NaN Zero Imputation
39 Decision Trees 0.106849 NaN Zero Imputation
40 Decision Trees 0.208556 NaN Zero Imputation
41 Decision Trees 0.141304 NaN Zero Imputation
42 Decision Trees 0.555618 NaN Zero Imputation
43 Decision Trees 0.119359 NaN Zero Imputation
44 Decision Trees 0.111236 NaN Zero Imputation
45 Decision Trees NaN 0.0 Zero Imputation
46 Decision Trees NaN None Zero Imputation
47 Decision Trees NaN gini Zero Imputation
48 Decision Trees NaN None Zero Imputation
49 Decision Trees NaN None Zero Imputation
50 Decision Trees NaN None Zero Imputation
51 Decision Trees NaN 0.0 Zero Imputation
52 Decision Trees NaN 1 Zero Imputation
53 Decision Trees NaN 2 Zero Imputation
54 Decision Trees NaN 0.0 Zero Imputation
55 Decision Trees NaN None Zero Imputation
56 Decision Trees NaN best Zero Imputation
57 Decision Trees 0.874868 NaN Zero Imputation
58 Decision Trees 0.088496 NaN Zero Imputation
59 Decision Trees 0.153061 NaN Zero Imputation
60 Decision Trees 0.112150 NaN Zero Imputation
61 Decision Trees 0.531580 NaN Zero Imputation
62 Decision Trees 0.070283 NaN Zero Imputation
63 Decision Trees 0.063160 NaN Zero Imputation
64 Decision Trees NaN 0.0 Zero Imputation
65 Decision Trees NaN None Zero Imputation
66 Decision Trees NaN gini Zero Imputation
67 Decision Trees NaN None Zero Imputation
68 Decision Trees NaN None Zero Imputation
69 Decision Trees NaN None Zero Imputation
70 Decision Trees NaN 0.0 Zero Imputation
71 Decision Trees NaN 1 Zero Imputation
72 Decision Trees NaN 2 Zero Imputation
73 Decision Trees NaN 0.0 Zero Imputation
74 Decision Trees NaN None Zero Imputation
75 Decision Trees NaN best Zero Imputation
76 Decision Trees 0.877503 NaN Zero Imputation
77 Decision Trees 0.121581 NaN Zero Imputation
78 Decision Trees 0.185185 NaN Zero Imputation
79 Decision Trees 0.146789 NaN Zero Imputation
80 Decision Trees 0.550908 NaN Zero Imputation
81 Decision Trees 0.104459 NaN Zero Imputation
82 Decision Trees 0.101816 NaN Zero Imputation
83 Decision Trees NaN 0.0 Zero Imputation
84 Decision Trees NaN None Zero Imputation
85 Decision Trees NaN gini Zero Imputation
86 Decision Trees NaN None Zero Imputation
87 Decision Trees NaN None Zero Imputation
88 Decision Trees NaN None Zero Imputation
89 Decision Trees NaN 0.0 Zero Imputation
90 Decision Trees NaN 1 Zero Imputation
91 Decision Trees NaN 2 Zero Imputation
92 Decision Trees NaN 0.0 Zero Imputation
93 Decision Trees NaN None Zero Imputation
94 Decision Trees NaN best Zero Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
45 Oversampling
46 Oversampling
47 Oversampling
48 Oversampling
49 Oversampling
50 Oversampling
51 Oversampling
52 Oversampling
53 Oversampling
54 Oversampling
55 Oversampling
56 Oversampling
57 Oversampling
58 Oversampling
59 Oversampling
60 Oversampling
61 Oversampling
62 Oversampling
63 Oversampling
64 Oversampling
65 Oversampling
66 Oversampling
67 Oversampling
68 Oversampling
69 Oversampling
70 Oversampling
71 Oversampling
72 Oversampling
73 Oversampling
74 Oversampling
75 Oversampling
76 Oversampling
77 Oversampling
78 Oversampling
79 Oversampling
80 Oversampling
81 Oversampling
82 Oversampling
83 Oversampling
84 Oversampling
85 Oversampling
86 Oversampling
87 Oversampling
88 Oversampling
89 Oversampling
90 Oversampling
91 Oversampling
92 Oversampling
93 Oversampling
94 Oversampling
Model Unique Code
0 Decision Trees_Oversampling_Zero Imputation_fo...
1 Decision Trees_Oversampling_Zero Imputation_fo...
2 Decision Trees_Oversampling_Zero Imputation_fo...
3 Decision Trees_Oversampling_Zero Imputation_fo...
4 Decision Trees_Oversampling_Zero Imputation_fo...
5 Decision Trees_Oversampling_Zero Imputation_fo...
6 Decision Trees_Oversampling_Zero Imputation_fo...
7 Decision Trees_Oversampling_Zero Imputation_fo...
8 Decision Trees_Oversampling_Zero Imputation_fo...
9 Decision Trees_Oversampling_Zero Imputation_fo...
10 Decision Trees_Oversampling_Zero Imputation_fo...
11 Decision Trees_Oversampling_Zero Imputation_fo...
12 Decision Trees_Oversampling_Zero Imputation_fo...
13 Decision Trees_Oversampling_Zero Imputation_fo...
14 Decision Trees_Oversampling_Zero Imputation_fo...
15 Decision Trees_Oversampling_Zero Imputation_fo...
16 Decision Trees_Oversampling_Zero Imputation_fo...
17 Decision Trees_Oversampling_Zero Imputation_fo...
18 Decision Trees_Oversampling_Zero Imputation_fo...
19 Decision Trees_Oversampling_Zero Imputation_fo...
20 Decision Trees_Oversampling_Zero Imputation_fo...
21 Decision Trees_Oversampling_Zero Imputation_fo...
22 Decision Trees_Oversampling_Zero Imputation_fo...
23 Decision Trees_Oversampling_Zero Imputation_fo...
24 Decision Trees_Oversampling_Zero Imputation_fo...
25 Decision Trees_Oversampling_Zero Imputation_fo...
26 Decision Trees_Oversampling_Zero Imputation_fo...
27 Decision Trees_Oversampling_Zero Imputation_fo...
28 Decision Trees_Oversampling_Zero Imputation_fo...
29 Decision Trees_Oversampling_Zero Imputation_fo...
30 Decision Trees_Oversampling_Zero Imputation_fo...
31 Decision Trees_Oversampling_Zero Imputation_fo...
32 Decision Trees_Oversampling_Zero Imputation_fo...
33 Decision Trees_Oversampling_Zero Imputation_fo...
34 Decision Trees_Oversampling_Zero Imputation_fo...
35 Decision Trees_Oversampling_Zero Imputation_fo...
36 Decision Trees_Oversampling_Zero Imputation_fo...
37 Decision Trees_Oversampling_Zero Imputation_fo...
38 Decision Trees_Oversampling_Zero Imputation_fo...
39 Decision Trees_Oversampling_Zero Imputation_fo...
40 Decision Trees_Oversampling_Zero Imputation_fo...
41 Decision Trees_Oversampling_Zero Imputation_fo...
42 Decision Trees_Oversampling_Zero Imputation_fo...
43 Decision Trees_Oversampling_Zero Imputation_fo...
44 Decision Trees_Oversampling_Zero Imputation_fo...
45 Decision Trees_Oversampling_Zero Imputation_fo...
46 Decision Trees_Oversampling_Zero Imputation_fo...
47 Decision Trees_Oversampling_Zero Imputation_fo...
48 Decision Trees_Oversampling_Zero Imputation_fo...
49 Decision Trees_Oversampling_Zero Imputation_fo...
50 Decision Trees_Oversampling_Zero Imputation_fo...
51 Decision Trees_Oversampling_Zero Imputation_fo...
52 Decision Trees_Oversampling_Zero Imputation_fo...
53 Decision Trees_Oversampling_Zero Imputation_fo...
54 Decision Trees_Oversampling_Zero Imputation_fo...
55 Decision Trees_Oversampling_Zero Imputation_fo...
56 Decision Trees_Oversampling_Zero Imputation_fo...
57 Decision Trees_Oversampling_Zero Imputation_fo...
58 Decision Trees_Oversampling_Zero Imputation_fo...
59 Decision Trees_Oversampling_Zero Imputation_fo...
60 Decision Trees_Oversampling_Zero Imputation_fo...
61 Decision Trees_Oversampling_Zero Imputation_fo...
62 Decision Trees_Oversampling_Zero Imputation_fo...
63 Decision Trees_Oversampling_Zero Imputation_fo...
64 Decision Trees_Oversampling_Zero Imputation_fo...
65 Decision Trees_Oversampling_Zero Imputation_fo...
66 Decision Trees_Oversampling_Zero Imputation_fo...
67 Decision Trees_Oversampling_Zero Imputation_fo...
68 Decision Trees_Oversampling_Zero Imputation_fo...
69 Decision Trees_Oversampling_Zero Imputation_fo...
70 Decision Trees_Oversampling_Zero Imputation_fo...
71 Decision Trees_Oversampling_Zero Imputation_fo...
72 Decision Trees_Oversampling_Zero Imputation_fo...
73 Decision Trees_Oversampling_Zero Imputation_fo...
74 Decision Trees_Oversampling_Zero Imputation_fo...
75 Decision Trees_Oversampling_Zero Imputation_fo...
76 Decision Trees_Oversampling_Zero Imputation_fo...
77 Decision Trees_Oversampling_Zero Imputation_fo...
78 Decision Trees_Oversampling_Zero Imputation_fo...
79 Decision Trees_Oversampling_Zero Imputation_fo...
80 Decision Trees_Oversampling_Zero Imputation_fo...
81 Decision Trees_Oversampling_Zero Imputation_fo...
82 Decision Trees_Oversampling_Zero Imputation_fo...
83 Decision Trees_Oversampling_Zero Imputation_fo...
84 Decision Trees_Oversampling_Zero Imputation_fo...
85 Decision Trees_Oversampling_Zero Imputation_fo...
86 Decision Trees_Oversampling_Zero Imputation_fo...
87 Decision Trees_Oversampling_Zero Imputation_fo...
88 Decision Trees_Oversampling_Zero Imputation_fo...
89 Decision Trees_Oversampling_Zero Imputation_fo...
90 Decision Trees_Oversampling_Zero Imputation_fo...
91 Decision Trees_Oversampling_Zero Imputation_fo...
92 Decision Trees_Oversampling_Zero Imputation_fo...
93 Decision Trees_Oversampling_Zero Imputation_fo...
94 Decision Trees_Oversampling_Zero Imputation_fo... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Decision Trees 0.874638 NaN Mean Imputation
1 Decision Trees 0.118211 NaN Mean Imputation
2 Decision Trees 0.156118 NaN Mean Imputation
3 Decision Trees 0.134545 NaN Mean Imputation
4 Decision Trees 0.539191 NaN Mean Imputation
5 Decision Trees 0.078590 NaN Mean Imputation
6 Decision Trees 0.078383 NaN Mean Imputation
7 Decision Trees NaN 0.0 Mean Imputation
8 Decision Trees NaN None Mean Imputation
9 Decision Trees NaN gini Mean Imputation
10 Decision Trees NaN None Mean Imputation
11 Decision Trees NaN None Mean Imputation
12 Decision Trees NaN None Mean Imputation
13 Decision Trees NaN 0.0 Mean Imputation
14 Decision Trees NaN 1 Mean Imputation
15 Decision Trees NaN 2 Mean Imputation
16 Decision Trees NaN 0.0 Mean Imputation
17 Decision Trees NaN None Mean Imputation
18 Decision Trees NaN best Mean Imputation
19 Decision Trees 0.882275 NaN Mean Imputation
20 Decision Trees 0.094556 NaN Mean Imputation
21 Decision Trees 0.201220 NaN Mean Imputation
22 Decision Trees 0.128655 NaN Mean Imputation
23 Decision Trees 0.562656 NaN Mean Imputation
24 Decision Trees 0.130330 NaN Mean Imputation
25 Decision Trees 0.125311 NaN Mean Imputation
26 Decision Trees NaN 0.0 Mean Imputation
27 Decision Trees NaN None Mean Imputation
28 Decision Trees NaN gini Mean Imputation
29 Decision Trees NaN None Mean Imputation
30 Decision Trees NaN None Mean Imputation
31 Decision Trees NaN None Mean Imputation
32 Decision Trees NaN 0.0 Mean Imputation
33 Decision Trees NaN 1 Mean Imputation
34 Decision Trees NaN 2 Mean Imputation
35 Decision Trees NaN 0.0 Mean Imputation
36 Decision Trees NaN None Mean Imputation
37 Decision Trees NaN best Mean Imputation
38 Decision Trees 0.873848 NaN Mean Imputation
39 Decision Trees 0.109626 NaN Mean Imputation
40 Decision Trees 0.219251 NaN Mean Imputation
41 Decision Trees 0.146168 NaN Mean Imputation
42 Decision Trees 0.560513 NaN Mean Imputation
43 Decision Trees 0.127839 NaN Mean Imputation
44 Decision Trees 0.121026 NaN Mean Imputation
45 Decision Trees NaN 0.0 Mean Imputation
46 Decision Trees NaN None Mean Imputation
47 Decision Trees NaN gini Mean Imputation
48 Decision Trees NaN None Mean Imputation
49 Decision Trees NaN None Mean Imputation
50 Decision Trees NaN None Mean Imputation
51 Decision Trees NaN 0.0 Mean Imputation
52 Decision Trees NaN 1 Mean Imputation
53 Decision Trees NaN 2 Mean Imputation
54 Decision Trees NaN 0.0 Mean Imputation
55 Decision Trees NaN None Mean Imputation
56 Decision Trees NaN best Mean Imputation
57 Decision Trees 0.878820 NaN Mean Imputation
58 Decision Trees 0.107143 NaN Mean Imputation
59 Decision Trees 0.183673 NaN Mean Imputation
60 Decision Trees 0.135338 NaN Mean Imputation
61 Decision Trees 0.548421 NaN Mean Imputation
62 Decision Trees 0.102562 NaN Mean Imputation
63 Decision Trees 0.096842 NaN Mean Imputation
64 Decision Trees NaN 0.0 Mean Imputation
65 Decision Trees NaN None Mean Imputation
66 Decision Trees NaN gini Mean Imputation
67 Decision Trees NaN None Mean Imputation
68 Decision Trees NaN None Mean Imputation
69 Decision Trees NaN None Mean Imputation
70 Decision Trees NaN 0.0 Mean Imputation
71 Decision Trees NaN 1 Mean Imputation
72 Decision Trees NaN 2 Mean Imputation
73 Decision Trees NaN 0.0 Mean Imputation
74 Decision Trees NaN None Mean Imputation
75 Decision Trees NaN best Mean Imputation
76 Decision Trees 0.873024 NaN Mean Imputation
77 Decision Trees 0.101796 NaN Mean Imputation
78 Decision Trees 0.157407 NaN Mean Imputation
79 Decision Trees 0.123636 NaN Mean Imputation
80 Decision Trees 0.534819 NaN Mean Imputation
81 Decision Trees 0.073609 NaN Mean Imputation
82 Decision Trees 0.069638 NaN Mean Imputation
83 Decision Trees NaN 0.0 Mean Imputation
84 Decision Trees NaN None Mean Imputation
85 Decision Trees NaN gini Mean Imputation
86 Decision Trees NaN None Mean Imputation
87 Decision Trees NaN None Mean Imputation
88 Decision Trees NaN None Mean Imputation
89 Decision Trees NaN 0.0 Mean Imputation
90 Decision Trees NaN 1 Mean Imputation
91 Decision Trees NaN 2 Mean Imputation
92 Decision Trees NaN 0.0 Mean Imputation
93 Decision Trees NaN None Mean Imputation
94 Decision Trees NaN best Mean Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
45 Oversampling
46 Oversampling
47 Oversampling
48 Oversampling
49 Oversampling
50 Oversampling
51 Oversampling
52 Oversampling
53 Oversampling
54 Oversampling
55 Oversampling
56 Oversampling
57 Oversampling
58 Oversampling
59 Oversampling
60 Oversampling
61 Oversampling
62 Oversampling
63 Oversampling
64 Oversampling
65 Oversampling
66 Oversampling
67 Oversampling
68 Oversampling
69 Oversampling
70 Oversampling
71 Oversampling
72 Oversampling
73 Oversampling
74 Oversampling
75 Oversampling
76 Oversampling
77 Oversampling
78 Oversampling
79 Oversampling
80 Oversampling
81 Oversampling
82 Oversampling
83 Oversampling
84 Oversampling
85 Oversampling
86 Oversampling
87 Oversampling
88 Oversampling
89 Oversampling
90 Oversampling
91 Oversampling
92 Oversampling
93 Oversampling
94 Oversampling
Model Unique Code
0 Decision Trees_Oversampling_Mean Imputation_fo...
1 Decision Trees_Oversampling_Mean Imputation_fo...
2 Decision Trees_Oversampling_Mean Imputation_fo...
3 Decision Trees_Oversampling_Mean Imputation_fo...
4 Decision Trees_Oversampling_Mean Imputation_fo...
5 Decision Trees_Oversampling_Mean Imputation_fo...
6 Decision Trees_Oversampling_Mean Imputation_fo...
7 Decision Trees_Oversampling_Mean Imputation_fo...
8 Decision Trees_Oversampling_Mean Imputation_fo...
9 Decision Trees_Oversampling_Mean Imputation_fo...
10 Decision Trees_Oversampling_Mean Imputation_fo...
11 Decision Trees_Oversampling_Mean Imputation_fo...
12 Decision Trees_Oversampling_Mean Imputation_fo...
13 Decision Trees_Oversampling_Mean Imputation_fo...
14 Decision Trees_Oversampling_Mean Imputation_fo...
15 Decision Trees_Oversampling_Mean Imputation_fo...
16 Decision Trees_Oversampling_Mean Imputation_fo...
17 Decision Trees_Oversampling_Mean Imputation_fo...
18 Decision Trees_Oversampling_Mean Imputation_fo...
19 Decision Trees_Oversampling_Mean Imputation_fo...
20 Decision Trees_Oversampling_Mean Imputation_fo...
21 Decision Trees_Oversampling_Mean Imputation_fo...
22 Decision Trees_Oversampling_Mean Imputation_fo...
23 Decision Trees_Oversampling_Mean Imputation_fo...
24 Decision Trees_Oversampling_Mean Imputation_fo...
25 Decision Trees_Oversampling_Mean Imputation_fo...
26 Decision Trees_Oversampling_Mean Imputation_fo...
27 Decision Trees_Oversampling_Mean Imputation_fo...
28 Decision Trees_Oversampling_Mean Imputation_fo...
29 Decision Trees_Oversampling_Mean Imputation_fo...
30 Decision Trees_Oversampling_Mean Imputation_fo...
31 Decision Trees_Oversampling_Mean Imputation_fo...
32 Decision Trees_Oversampling_Mean Imputation_fo...
33 Decision Trees_Oversampling_Mean Imputation_fo...
34 Decision Trees_Oversampling_Mean Imputation_fo...
35 Decision Trees_Oversampling_Mean Imputation_fo...
36 Decision Trees_Oversampling_Mean Imputation_fo...
37 Decision Trees_Oversampling_Mean Imputation_fo...
38 Decision Trees_Oversampling_Mean Imputation_fo...
39 Decision Trees_Oversampling_Mean Imputation_fo...
40 Decision Trees_Oversampling_Mean Imputation_fo...
41 Decision Trees_Oversampling_Mean Imputation_fo...
42 Decision Trees_Oversampling_Mean Imputation_fo...
43 Decision Trees_Oversampling_Mean Imputation_fo...
44 Decision Trees_Oversampling_Mean Imputation_fo...
45 Decision Trees_Oversampling_Mean Imputation_fo...
46 Decision Trees_Oversampling_Mean Imputation_fo...
47 Decision Trees_Oversampling_Mean Imputation_fo...
48 Decision Trees_Oversampling_Mean Imputation_fo...
49 Decision Trees_Oversampling_Mean Imputation_fo...
50 Decision Trees_Oversampling_Mean Imputation_fo...
51 Decision Trees_Oversampling_Mean Imputation_fo...
52 Decision Trees_Oversampling_Mean Imputation_fo...
53 Decision Trees_Oversampling_Mean Imputation_fo...
54 Decision Trees_Oversampling_Mean Imputation_fo...
55 Decision Trees_Oversampling_Mean Imputation_fo...
56 Decision Trees_Oversampling_Mean Imputation_fo...
57 Decision Trees_Oversampling_Mean Imputation_fo...
58 Decision Trees_Oversampling_Mean Imputation_fo...
59 Decision Trees_Oversampling_Mean Imputation_fo...
60 Decision Trees_Oversampling_Mean Imputation_fo...
61 Decision Trees_Oversampling_Mean Imputation_fo...
62 Decision Trees_Oversampling_Mean Imputation_fo...
63 Decision Trees_Oversampling_Mean Imputation_fo...
64 Decision Trees_Oversampling_Mean Imputation_fo...
65 Decision Trees_Oversampling_Mean Imputation_fo...
66 Decision Trees_Oversampling_Mean Imputation_fo...
67 Decision Trees_Oversampling_Mean Imputation_fo...
68 Decision Trees_Oversampling_Mean Imputation_fo...
69 Decision Trees_Oversampling_Mean Imputation_fo...
70 Decision Trees_Oversampling_Mean Imputation_fo...
71 Decision Trees_Oversampling_Mean Imputation_fo...
72 Decision Trees_Oversampling_Mean Imputation_fo...
73 Decision Trees_Oversampling_Mean Imputation_fo...
74 Decision Trees_Oversampling_Mean Imputation_fo...
75 Decision Trees_Oversampling_Mean Imputation_fo...
76 Decision Trees_Oversampling_Mean Imputation_fo...
77 Decision Trees_Oversampling_Mean Imputation_fo...
78 Decision Trees_Oversampling_Mean Imputation_fo...
79 Decision Trees_Oversampling_Mean Imputation_fo...
80 Decision Trees_Oversampling_Mean Imputation_fo...
81 Decision Trees_Oversampling_Mean Imputation_fo...
82 Decision Trees_Oversampling_Mean Imputation_fo...
83 Decision Trees_Oversampling_Mean Imputation_fo...
84 Decision Trees_Oversampling_Mean Imputation_fo...
85 Decision Trees_Oversampling_Mean Imputation_fo...
86 Decision Trees_Oversampling_Mean Imputation_fo...
87 Decision Trees_Oversampling_Mean Imputation_fo...
88 Decision Trees_Oversampling_Mean Imputation_fo...
89 Decision Trees_Oversampling_Mean Imputation_fo...
90 Decision Trees_Oversampling_Mean Imputation_fo...
91 Decision Trees_Oversampling_Mean Imputation_fo...
92 Decision Trees_Oversampling_Mean Imputation_fo...
93 Decision Trees_Oversampling_Mean Imputation_fo...
94 Decision Trees_Oversampling_Mean Imputation_fo... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Decision Trees 0.867264 NaN Median Imputation
1 Decision Trees 0.101493 NaN Median Imputation
2 Decision Trees 0.143460 NaN Median Imputation
3 Decision Trees 0.118881 NaN Median Imputation
4 Decision Trees 0.528470 NaN Median Imputation
5 Decision Trees 0.059471 NaN Median Imputation
6 Decision Trees 0.056941 NaN Median Imputation
7 Decision Trees NaN 0.0 Median Imputation
8 Decision Trees NaN None Median Imputation
9 Decision Trees NaN gini Median Imputation
10 Decision Trees NaN None Median Imputation
11 Decision Trees NaN None Median Imputation
12 Decision Trees NaN None Median Imputation
13 Decision Trees NaN 0.0 Median Imputation
14 Decision Trees NaN 1 Median Imputation
15 Decision Trees NaN 2 Median Imputation
16 Decision Trees NaN 0.0 Median Imputation
17 Decision Trees NaN None Median Imputation
18 Decision Trees NaN best Median Imputation
19 Decision Trees 0.882539 NaN Median Imputation
20 Decision Trees 0.103933 NaN Median Imputation
21 Decision Trees 0.225610 NaN Median Imputation
22 Decision Trees 0.142308 NaN Median Imputation
23 Decision Trees 0.566118 NaN Median Imputation
24 Decision Trees 0.137804 NaN Median Imputation
25 Decision Trees 0.132236 NaN Median Imputation
26 Decision Trees NaN 0.0 Median Imputation
27 Decision Trees NaN None Median Imputation
28 Decision Trees NaN gini Median Imputation
29 Decision Trees NaN None Median Imputation
30 Decision Trees NaN None Median Imputation
31 Decision Trees NaN None Median Imputation
32 Decision Trees NaN 0.0 Median Imputation
33 Decision Trees NaN 1 Median Imputation
34 Decision Trees NaN 2 Median Imputation
35 Decision Trees NaN 0.0 Median Imputation
36 Decision Trees NaN None Median Imputation
37 Decision Trees NaN best Median Imputation
38 Decision Trees 0.881485 NaN Median Imputation
39 Decision Trees 0.121037 NaN Median Imputation
40 Decision Trees 0.224599 NaN Median Imputation
41 Decision Trees 0.157303 NaN Median Imputation
42 Decision Trees 0.567455 NaN Median Imputation
43 Decision Trees 0.141219 NaN Median Imputation
44 Decision Trees 0.134910 NaN Median Imputation
45 Decision Trees NaN 0.0 Median Imputation
46 Decision Trees NaN None Median Imputation
47 Decision Trees NaN gini Median Imputation
48 Decision Trees NaN None Median Imputation
49 Decision Trees NaN None Median Imputation
50 Decision Trees NaN None Median Imputation
51 Decision Trees NaN 0.0 Median Imputation
52 Decision Trees NaN 1 Median Imputation
53 Decision Trees NaN 2 Median Imputation
54 Decision Trees NaN 0.0 Median Imputation
55 Decision Trees NaN None Median Imputation
56 Decision Trees NaN best Median Imputation
57 Decision Trees 0.871970 NaN Median Imputation
58 Decision Trees 0.080925 NaN Median Imputation
59 Decision Trees 0.142857 NaN Median Imputation
60 Decision Trees 0.103321 NaN Median Imputation
61 Decision Trees 0.524960 NaN Median Imputation
62 Decision Trees 0.055079 NaN Median Imputation
63 Decision Trees 0.049919 NaN Median Imputation
64 Decision Trees NaN 0.0 Median Imputation
65 Decision Trees NaN None Median Imputation
66 Decision Trees NaN gini Median Imputation
67 Decision Trees NaN None Median Imputation
68 Decision Trees NaN None Median Imputation
69 Decision Trees NaN None Median Imputation
70 Decision Trees NaN 0.0 Median Imputation
71 Decision Trees NaN 1 Median Imputation
72 Decision Trees NaN 2 Median Imputation
73 Decision Trees NaN 0.0 Median Imputation
74 Decision Trees NaN None Median Imputation
75 Decision Trees NaN best Median Imputation
76 Decision Trees 0.874341 NaN Median Imputation
77 Decision Trees 0.112760 NaN Median Imputation
78 Decision Trees 0.175926 NaN Median Imputation
79 Decision Trees 0.137432 NaN Median Imputation
80 Decision Trees 0.544465 NaN Median Imputation
81 Decision Trees 0.092406 NaN Median Imputation
82 Decision Trees 0.088930 NaN Median Imputation
83 Decision Trees NaN 0.0 Median Imputation
84 Decision Trees NaN None Median Imputation
85 Decision Trees NaN gini Median Imputation
86 Decision Trees NaN None Median Imputation
87 Decision Trees NaN None Median Imputation
88 Decision Trees NaN None Median Imputation
89 Decision Trees NaN 0.0 Median Imputation
90 Decision Trees NaN 1 Median Imputation
91 Decision Trees NaN 2 Median Imputation
92 Decision Trees NaN 0.0 Median Imputation
93 Decision Trees NaN None Median Imputation
94 Decision Trees NaN best Median Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
45 Oversampling
46 Oversampling
47 Oversampling
48 Oversampling
49 Oversampling
50 Oversampling
51 Oversampling
52 Oversampling
53 Oversampling
54 Oversampling
55 Oversampling
56 Oversampling
57 Oversampling
58 Oversampling
59 Oversampling
60 Oversampling
61 Oversampling
62 Oversampling
63 Oversampling
64 Oversampling
65 Oversampling
66 Oversampling
67 Oversampling
68 Oversampling
69 Oversampling
70 Oversampling
71 Oversampling
72 Oversampling
73 Oversampling
74 Oversampling
75 Oversampling
76 Oversampling
77 Oversampling
78 Oversampling
79 Oversampling
80 Oversampling
81 Oversampling
82 Oversampling
83 Oversampling
84 Oversampling
85 Oversampling
86 Oversampling
87 Oversampling
88 Oversampling
89 Oversampling
90 Oversampling
91 Oversampling
92 Oversampling
93 Oversampling
94 Oversampling
Model Unique Code
0 Decision Trees_Oversampling_Median Imputation_...
1 Decision Trees_Oversampling_Median Imputation_...
2 Decision Trees_Oversampling_Median Imputation_...
3 Decision Trees_Oversampling_Median Imputation_...
4 Decision Trees_Oversampling_Median Imputation_...
5 Decision Trees_Oversampling_Median Imputation_...
6 Decision Trees_Oversampling_Median Imputation_...
7 Decision Trees_Oversampling_Median Imputation_...
8 Decision Trees_Oversampling_Median Imputation_...
9 Decision Trees_Oversampling_Median Imputation_...
10 Decision Trees_Oversampling_Median Imputation_...
11 Decision Trees_Oversampling_Median Imputation_...
12 Decision Trees_Oversampling_Median Imputation_...
13 Decision Trees_Oversampling_Median Imputation_...
14 Decision Trees_Oversampling_Median Imputation_...
15 Decision Trees_Oversampling_Median Imputation_...
16 Decision Trees_Oversampling_Median Imputation_...
17 Decision Trees_Oversampling_Median Imputation_...
18 Decision Trees_Oversampling_Median Imputation_...
19 Decision Trees_Oversampling_Median Imputation_...
20 Decision Trees_Oversampling_Median Imputation_...
21 Decision Trees_Oversampling_Median Imputation_...
22 Decision Trees_Oversampling_Median Imputation_...
23 Decision Trees_Oversampling_Median Imputation_...
24 Decision Trees_Oversampling_Median Imputation_...
25 Decision Trees_Oversampling_Median Imputation_...
26 Decision Trees_Oversampling_Median Imputation_...
27 Decision Trees_Oversampling_Median Imputation_...
28 Decision Trees_Oversampling_Median Imputation_...
29 Decision Trees_Oversampling_Median Imputation_...
30 Decision Trees_Oversampling_Median Imputation_...
31 Decision Trees_Oversampling_Median Imputation_...
32 Decision Trees_Oversampling_Median Imputation_...
33 Decision Trees_Oversampling_Median Imputation_...
34 Decision Trees_Oversampling_Median Imputation_...
35 Decision Trees_Oversampling_Median Imputation_...
36 Decision Trees_Oversampling_Median Imputation_...
37 Decision Trees_Oversampling_Median Imputation_...
38 Decision Trees_Oversampling_Median Imputation_...
39 Decision Trees_Oversampling_Median Imputation_...
40 Decision Trees_Oversampling_Median Imputation_...
41 Decision Trees_Oversampling_Median Imputation_...
42 Decision Trees_Oversampling_Median Imputation_...
43 Decision Trees_Oversampling_Median Imputation_...
44 Decision Trees_Oversampling_Median Imputation_...
45 Decision Trees_Oversampling_Median Imputation_...
46 Decision Trees_Oversampling_Median Imputation_...
47 Decision Trees_Oversampling_Median Imputation_...
48 Decision Trees_Oversampling_Median Imputation_...
49 Decision Trees_Oversampling_Median Imputation_...
50 Decision Trees_Oversampling_Median Imputation_...
51 Decision Trees_Oversampling_Median Imputation_...
52 Decision Trees_Oversampling_Median Imputation_...
53 Decision Trees_Oversampling_Median Imputation_...
54 Decision Trees_Oversampling_Median Imputation_...
55 Decision Trees_Oversampling_Median Imputation_...
56 Decision Trees_Oversampling_Median Imputation_...
57 Decision Trees_Oversampling_Median Imputation_...
58 Decision Trees_Oversampling_Median Imputation_...
59 Decision Trees_Oversampling_Median Imputation_...
60 Decision Trees_Oversampling_Median Imputation_...
61 Decision Trees_Oversampling_Median Imputation_...
62 Decision Trees_Oversampling_Median Imputation_...
63 Decision Trees_Oversampling_Median Imputation_...
64 Decision Trees_Oversampling_Median Imputation_...
65 Decision Trees_Oversampling_Median Imputation_...
66 Decision Trees_Oversampling_Median Imputation_...
67 Decision Trees_Oversampling_Median Imputation_...
68 Decision Trees_Oversampling_Median Imputation_...
69 Decision Trees_Oversampling_Median Imputation_...
70 Decision Trees_Oversampling_Median Imputation_...
71 Decision Trees_Oversampling_Median Imputation_...
72 Decision Trees_Oversampling_Median Imputation_...
73 Decision Trees_Oversampling_Median Imputation_...
74 Decision Trees_Oversampling_Median Imputation_...
75 Decision Trees_Oversampling_Median Imputation_...
76 Decision Trees_Oversampling_Median Imputation_...
77 Decision Trees_Oversampling_Median Imputation_...
78 Decision Trees_Oversampling_Median Imputation_...
79 Decision Trees_Oversampling_Median Imputation_...
80 Decision Trees_Oversampling_Median Imputation_...
81 Decision Trees_Oversampling_Median Imputation_...
82 Decision Trees_Oversampling_Median Imputation_...
83 Decision Trees_Oversampling_Median Imputation_...
84 Decision Trees_Oversampling_Median Imputation_...
85 Decision Trees_Oversampling_Median Imputation_...
86 Decision Trees_Oversampling_Median Imputation_...
87 Decision Trees_Oversampling_Median Imputation_...
88 Decision Trees_Oversampling_Median Imputation_...
89 Decision Trees_Oversampling_Median Imputation_...
90 Decision Trees_Oversampling_Median Imputation_...
91 Decision Trees_Oversampling_Median Imputation_...
92 Decision Trees_Oversampling_Median Imputation_...
93 Decision Trees_Oversampling_Median Imputation_...
94 Decision Trees_Oversampling_Median Imputation_... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Decision Trees 0.571767 NaN Zero Imputation
1 Decision Trees 0.080363 NaN Zero Imputation
2 Decision Trees 0.561181 NaN Zero Imputation
3 Decision Trees 0.140592 NaN Zero Imputation
4 Decision Trees 0.562283 NaN Zero Imputation
5 Decision Trees 0.136187 NaN Zero Imputation
6 Decision Trees 0.124566 NaN Zero Imputation
7 Decision Trees NaN 0.0 Zero Imputation
8 Decision Trees NaN None Zero Imputation
9 Decision Trees NaN gini Zero Imputation
10 Decision Trees NaN None Zero Imputation
11 Decision Trees NaN None Zero Imputation
12 Decision Trees NaN None Zero Imputation
13 Decision Trees NaN 0.0 Zero Imputation
14 Decision Trees NaN 1 Zero Imputation
15 Decision Trees NaN 2 Zero Imputation
16 Decision Trees NaN 0.0 Zero Imputation
17 Decision Trees NaN None Zero Imputation
18 Decision Trees NaN best Zero Imputation
19 Decision Trees 0.570977 NaN Zero Imputation
20 Decision Trees 0.058469 NaN Zero Imputation
21 Decision Trees 0.591463 NaN Zero Imputation
22 Decision Trees 0.106418 NaN Zero Imputation
23 Decision Trees 0.580208 NaN Zero Imputation
24 Decision Trees 0.174028 NaN Zero Imputation
25 Decision Trees 0.160416 NaN Zero Imputation
26 Decision Trees NaN 0.0 Zero Imputation
27 Decision Trees NaN None Zero Imputation
28 Decision Trees NaN gini Zero Imputation
29 Decision Trees NaN None Zero Imputation
30 Decision Trees NaN None Zero Imputation
31 Decision Trees NaN None Zero Imputation
32 Decision Trees NaN 0.0 Zero Imputation
33 Decision Trees NaN 1 Zero Imputation
34 Decision Trees NaN 2 Zero Imputation
35 Decision Trees NaN 0.0 Zero Imputation
36 Decision Trees NaN None Zero Imputation
37 Decision Trees NaN best Zero Imputation
38 Decision Trees 0.538320 NaN Zero Imputation
39 Decision Trees 0.060606 NaN Zero Imputation
40 Decision Trees 0.577540 NaN Zero Imputation
41 Decision Trees 0.109700 NaN Zero Imputation
42 Decision Trees 0.547375 NaN Zero Imputation
43 Decision Trees 0.115575 NaN Zero Imputation
44 Decision Trees 0.094750 NaN Zero Imputation
45 Decision Trees NaN 0.0 Zero Imputation
46 Decision Trees NaN None Zero Imputation
47 Decision Trees NaN gini Zero Imputation
48 Decision Trees NaN None Zero Imputation
49 Decision Trees NaN None Zero Imputation
50 Decision Trees NaN None Zero Imputation
51 Decision Trees NaN 0.0 Zero Imputation
52 Decision Trees NaN 1 Zero Imputation
53 Decision Trees NaN 2 Zero Imputation
54 Decision Trees NaN 0.0 Zero Imputation
55 Decision Trees NaN None Zero Imputation
56 Decision Trees NaN best Zero Imputation
57 Decision Trees 0.573498 NaN Zero Imputation
58 Decision Trees 0.068526 NaN Zero Imputation
59 Decision Trees 0.576531 NaN Zero Imputation
60 Decision Trees 0.122493 NaN Zero Imputation
61 Decision Trees 0.572456 NaN Zero Imputation
62 Decision Trees 0.153226 NaN Zero Imputation
63 Decision Trees 0.144912 NaN Zero Imputation
64 Decision Trees NaN 0.0 Zero Imputation
65 Decision Trees NaN None Zero Imputation
66 Decision Trees NaN gini Zero Imputation
67 Decision Trees NaN None Zero Imputation
68 Decision Trees NaN None Zero Imputation
69 Decision Trees NaN None Zero Imputation
70 Decision Trees NaN 0.0 Zero Imputation
71 Decision Trees NaN 1 Zero Imputation
72 Decision Trees NaN 2 Zero Imputation
73 Decision Trees NaN 0.0 Zero Imputation
74 Decision Trees NaN None Zero Imputation
75 Decision Trees NaN best Zero Imputation
76 Decision Trees 0.577713 NaN Zero Imputation
77 Decision Trees 0.077392 NaN Zero Imputation
78 Decision Trees 0.587963 NaN Zero Imputation
79 Decision Trees 0.136780 NaN Zero Imputation
80 Decision Trees 0.578050 NaN Zero Imputation
81 Decision Trees 0.165058 NaN Zero Imputation
82 Decision Trees 0.156100 NaN Zero Imputation
83 Decision Trees NaN 0.0 Zero Imputation
84 Decision Trees NaN None Zero Imputation
85 Decision Trees NaN gini Zero Imputation
86 Decision Trees NaN None Zero Imputation
87 Decision Trees NaN None Zero Imputation
88 Decision Trees NaN None Zero Imputation
89 Decision Trees NaN 0.0 Zero Imputation
90 Decision Trees NaN 1 Zero Imputation
91 Decision Trees NaN 2 Zero Imputation
92 Decision Trees NaN 0.0 Zero Imputation
93 Decision Trees NaN None Zero Imputation
94 Decision Trees NaN best Zero Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
45 Undersampling
46 Undersampling
47 Undersampling
48 Undersampling
49 Undersampling
50 Undersampling
51 Undersampling
52 Undersampling
53 Undersampling
54 Undersampling
55 Undersampling
56 Undersampling
57 Undersampling
58 Undersampling
59 Undersampling
60 Undersampling
61 Undersampling
62 Undersampling
63 Undersampling
64 Undersampling
65 Undersampling
66 Undersampling
67 Undersampling
68 Undersampling
69 Undersampling
70 Undersampling
71 Undersampling
72 Undersampling
73 Undersampling
74 Undersampling
75 Undersampling
76 Undersampling
77 Undersampling
78 Undersampling
79 Undersampling
80 Undersampling
81 Undersampling
82 Undersampling
83 Undersampling
84 Undersampling
85 Undersampling
86 Undersampling
87 Undersampling
88 Undersampling
89 Undersampling
90 Undersampling
91 Undersampling
92 Undersampling
93 Undersampling
94 Undersampling
Model Unique Code
0 Decision Trees_Undersampling_Zero Imputation_f...
1 Decision Trees_Undersampling_Zero Imputation_f...
2 Decision Trees_Undersampling_Zero Imputation_f...
3 Decision Trees_Undersampling_Zero Imputation_f...
4 Decision Trees_Undersampling_Zero Imputation_f...
5 Decision Trees_Undersampling_Zero Imputation_f...
6 Decision Trees_Undersampling_Zero Imputation_f...
7 Decision Trees_Undersampling_Zero Imputation_f...
8 Decision Trees_Undersampling_Zero Imputation_f...
9 Decision Trees_Undersampling_Zero Imputation_f...
10 Decision Trees_Undersampling_Zero Imputation_f...
11 Decision Trees_Undersampling_Zero Imputation_f...
12 Decision Trees_Undersampling_Zero Imputation_f...
13 Decision Trees_Undersampling_Zero Imputation_f...
14 Decision Trees_Undersampling_Zero Imputation_f...
15 Decision Trees_Undersampling_Zero Imputation_f...
16 Decision Trees_Undersampling_Zero Imputation_f...
17 Decision Trees_Undersampling_Zero Imputation_f...
18 Decision Trees_Undersampling_Zero Imputation_f...
19 Decision Trees_Undersampling_Zero Imputation_f...
20 Decision Trees_Undersampling_Zero Imputation_f...
21 Decision Trees_Undersampling_Zero Imputation_f...
22 Decision Trees_Undersampling_Zero Imputation_f...
23 Decision Trees_Undersampling_Zero Imputation_f...
24 Decision Trees_Undersampling_Zero Imputation_f...
25 Decision Trees_Undersampling_Zero Imputation_f...
26 Decision Trees_Undersampling_Zero Imputation_f...
27 Decision Trees_Undersampling_Zero Imputation_f...
28 Decision Trees_Undersampling_Zero Imputation_f...
29 Decision Trees_Undersampling_Zero Imputation_f...
30 Decision Trees_Undersampling_Zero Imputation_f...
31 Decision Trees_Undersampling_Zero Imputation_f...
32 Decision Trees_Undersampling_Zero Imputation_f...
33 Decision Trees_Undersampling_Zero Imputation_f...
34 Decision Trees_Undersampling_Zero Imputation_f...
35 Decision Trees_Undersampling_Zero Imputation_f...
36 Decision Trees_Undersampling_Zero Imputation_f...
37 Decision Trees_Undersampling_Zero Imputation_f...
38 Decision Trees_Undersampling_Zero Imputation_f...
39 Decision Trees_Undersampling_Zero Imputation_f...
40 Decision Trees_Undersampling_Zero Imputation_f...
41 Decision Trees_Undersampling_Zero Imputation_f...
42 Decision Trees_Undersampling_Zero Imputation_f...
43 Decision Trees_Undersampling_Zero Imputation_f...
44 Decision Trees_Undersampling_Zero Imputation_f...
45 Decision Trees_Undersampling_Zero Imputation_f...
46 Decision Trees_Undersampling_Zero Imputation_f...
47 Decision Trees_Undersampling_Zero Imputation_f...
48 Decision Trees_Undersampling_Zero Imputation_f...
49 Decision Trees_Undersampling_Zero Imputation_f...
50 Decision Trees_Undersampling_Zero Imputation_f...
51 Decision Trees_Undersampling_Zero Imputation_f...
52 Decision Trees_Undersampling_Zero Imputation_f...
53 Decision Trees_Undersampling_Zero Imputation_f...
54 Decision Trees_Undersampling_Zero Imputation_f...
55 Decision Trees_Undersampling_Zero Imputation_f...
56 Decision Trees_Undersampling_Zero Imputation_f...
57 Decision Trees_Undersampling_Zero Imputation_f...
58 Decision Trees_Undersampling_Zero Imputation_f...
59 Decision Trees_Undersampling_Zero Imputation_f...
60 Decision Trees_Undersampling_Zero Imputation_f...
61 Decision Trees_Undersampling_Zero Imputation_f...
62 Decision Trees_Undersampling_Zero Imputation_f...
63 Decision Trees_Undersampling_Zero Imputation_f...
64 Decision Trees_Undersampling_Zero Imputation_f...
65 Decision Trees_Undersampling_Zero Imputation_f...
66 Decision Trees_Undersampling_Zero Imputation_f...
67 Decision Trees_Undersampling_Zero Imputation_f...
68 Decision Trees_Undersampling_Zero Imputation_f...
69 Decision Trees_Undersampling_Zero Imputation_f...
70 Decision Trees_Undersampling_Zero Imputation_f...
71 Decision Trees_Undersampling_Zero Imputation_f...
72 Decision Trees_Undersampling_Zero Imputation_f...
73 Decision Trees_Undersampling_Zero Imputation_f...
74 Decision Trees_Undersampling_Zero Imputation_f...
75 Decision Trees_Undersampling_Zero Imputation_f...
76 Decision Trees_Undersampling_Zero Imputation_f...
77 Decision Trees_Undersampling_Zero Imputation_f...
78 Decision Trees_Undersampling_Zero Imputation_f...
79 Decision Trees_Undersampling_Zero Imputation_f...
80 Decision Trees_Undersampling_Zero Imputation_f...
81 Decision Trees_Undersampling_Zero Imputation_f...
82 Decision Trees_Undersampling_Zero Imputation_f...
83 Decision Trees_Undersampling_Zero Imputation_f...
84 Decision Trees_Undersampling_Zero Imputation_f...
85 Decision Trees_Undersampling_Zero Imputation_f...
86 Decision Trees_Undersampling_Zero Imputation_f...
87 Decision Trees_Undersampling_Zero Imputation_f...
88 Decision Trees_Undersampling_Zero Imputation_f...
89 Decision Trees_Undersampling_Zero Imputation_f...
90 Decision Trees_Undersampling_Zero Imputation_f...
91 Decision Trees_Undersampling_Zero Imputation_f...
92 Decision Trees_Undersampling_Zero Imputation_f...
93 Decision Trees_Undersampling_Zero Imputation_f...
94 Decision Trees_Undersampling_Zero Imputation_f... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Decision Trees 0.569397 NaN Mean Imputation
1 Decision Trees 0.069581 NaN Mean Imputation
2 Decision Trees 0.476793 NaN Mean Imputation
3 Decision Trees 0.121440 NaN Mean Imputation
4 Decision Trees 0.533647 NaN Mean Imputation
5 Decision Trees 0.075430 NaN Mean Imputation
6 Decision Trees 0.067295 NaN Mean Imputation
7 Decision Trees NaN 0.0 Mean Imputation
8 Decision Trees NaN None Mean Imputation
9 Decision Trees NaN gini Mean Imputation
10 Decision Trees NaN None Mean Imputation
11 Decision Trees NaN None Mean Imputation
12 Decision Trees NaN None Mean Imputation
13 Decision Trees NaN 0.0 Mean Imputation
14 Decision Trees NaN 1 Mean Imputation
15 Decision Trees NaN 2 Mean Imputation
16 Decision Trees NaN 0.0 Mean Imputation
17 Decision Trees NaN None Mean Imputation
18 Decision Trees NaN best Mean Imputation
19 Decision Trees 0.568343 NaN Mean Imputation
20 Decision Trees 0.058119 NaN Mean Imputation
21 Decision Trees 0.591463 NaN Mean Imputation
22 Decision Trees 0.105837 NaN Mean Imputation
23 Decision Trees 0.579129 NaN Mean Imputation
24 Decision Trees 0.168672 NaN Mean Imputation
25 Decision Trees 0.158258 NaN Mean Imputation
26 Decision Trees NaN 0.0 Mean Imputation
27 Decision Trees NaN None Mean Imputation
28 Decision Trees NaN gini Mean Imputation
29 Decision Trees NaN None Mean Imputation
30 Decision Trees NaN None Mean Imputation
31 Decision Trees NaN None Mean Imputation
32 Decision Trees NaN 0.0 Mean Imputation
33 Decision Trees NaN 1 Mean Imputation
34 Decision Trees NaN 2 Mean Imputation
35 Decision Trees NaN 0.0 Mean Imputation
36 Decision Trees NaN None Mean Imputation
37 Decision Trees NaN best Mean Imputation
38 Decision Trees 0.575718 NaN Mean Imputation
39 Decision Trees 0.070567 NaN Mean Imputation
40 Decision Trees 0.625668 NaN Mean Imputation
41 Decision Trees 0.126829 NaN Mean Imputation
42 Decision Trees 0.592935 NaN Mean Imputation
43 Decision Trees 0.214649 NaN Mean Imputation
44 Decision Trees 0.185870 NaN Mean Imputation
45 Decision Trees NaN 0.0 Mean Imputation
46 Decision Trees NaN None Mean Imputation
47 Decision Trees NaN gini Mean Imputation
48 Decision Trees NaN None Mean Imputation
49 Decision Trees NaN None Mean Imputation
50 Decision Trees NaN None Mean Imputation
51 Decision Trees NaN 0.0 Mean Imputation
52 Decision Trees NaN 1 Mean Imputation
53 Decision Trees NaN 2 Mean Imputation
54 Decision Trees NaN 0.0 Mean Imputation
55 Decision Trees NaN None Mean Imputation
56 Decision Trees NaN best Mean Imputation
57 Decision Trees 0.560590 NaN Mean Imputation
58 Decision Trees 0.066549 NaN Mean Imputation
59 Decision Trees 0.576531 NaN Mean Imputation
60 Decision Trees 0.119324 NaN Mean Imputation
61 Decision Trees 0.562394 NaN Mean Imputation
62 Decision Trees 0.140068 NaN Mean Imputation
63 Decision Trees 0.124787 NaN Mean Imputation
64 Decision Trees NaN 0.0 Mean Imputation
65 Decision Trees NaN None Mean Imputation
66 Decision Trees NaN gini Mean Imputation
67 Decision Trees NaN None Mean Imputation
68 Decision Trees NaN None Mean Imputation
69 Decision Trees NaN None Mean Imputation
70 Decision Trees NaN 0.0 Mean Imputation
71 Decision Trees NaN 1 Mean Imputation
72 Decision Trees NaN 2 Mean Imputation
73 Decision Trees NaN 0.0 Mean Imputation
74 Decision Trees NaN None Mean Imputation
75 Decision Trees NaN best Mean Imputation
76 Decision Trees 0.582455 NaN Mean Imputation
77 Decision Trees 0.076685 NaN Mean Imputation
78 Decision Trees 0.574074 NaN Mean Imputation
79 Decision Trees 0.135297 NaN Mean Imputation
80 Decision Trees 0.579621 NaN Mean Imputation
81 Decision Trees 0.178109 NaN Mean Imputation
82 Decision Trees 0.159241 NaN Mean Imputation
83 Decision Trees NaN 0.0 Mean Imputation
84 Decision Trees NaN None Mean Imputation
85 Decision Trees NaN gini Mean Imputation
86 Decision Trees NaN None Mean Imputation
87 Decision Trees NaN None Mean Imputation
88 Decision Trees NaN None Mean Imputation
89 Decision Trees NaN 0.0 Mean Imputation
90 Decision Trees NaN 1 Mean Imputation
91 Decision Trees NaN 2 Mean Imputation
92 Decision Trees NaN 0.0 Mean Imputation
93 Decision Trees NaN None Mean Imputation
94 Decision Trees NaN best Mean Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
45 Undersampling
46 Undersampling
47 Undersampling
48 Undersampling
49 Undersampling
50 Undersampling
51 Undersampling
52 Undersampling
53 Undersampling
54 Undersampling
55 Undersampling
56 Undersampling
57 Undersampling
58 Undersampling
59 Undersampling
60 Undersampling
61 Undersampling
62 Undersampling
63 Undersampling
64 Undersampling
65 Undersampling
66 Undersampling
67 Undersampling
68 Undersampling
69 Undersampling
70 Undersampling
71 Undersampling
72 Undersampling
73 Undersampling
74 Undersampling
75 Undersampling
76 Undersampling
77 Undersampling
78 Undersampling
79 Undersampling
80 Undersampling
81 Undersampling
82 Undersampling
83 Undersampling
84 Undersampling
85 Undersampling
86 Undersampling
87 Undersampling
88 Undersampling
89 Undersampling
90 Undersampling
91 Undersampling
92 Undersampling
93 Undersampling
94 Undersampling
Model Unique Code
0 Decision Trees_Undersampling_Mean Imputation_f...
1 Decision Trees_Undersampling_Mean Imputation_f...
2 Decision Trees_Undersampling_Mean Imputation_f...
3 Decision Trees_Undersampling_Mean Imputation_f...
4 Decision Trees_Undersampling_Mean Imputation_f...
5 Decision Trees_Undersampling_Mean Imputation_f...
6 Decision Trees_Undersampling_Mean Imputation_f...
7 Decision Trees_Undersampling_Mean Imputation_f...
8 Decision Trees_Undersampling_Mean Imputation_f...
9 Decision Trees_Undersampling_Mean Imputation_f...
10 Decision Trees_Undersampling_Mean Imputation_f...
11 Decision Trees_Undersampling_Mean Imputation_f...
12 Decision Trees_Undersampling_Mean Imputation_f...
13 Decision Trees_Undersampling_Mean Imputation_f...
14 Decision Trees_Undersampling_Mean Imputation_f...
15 Decision Trees_Undersampling_Mean Imputation_f...
16 Decision Trees_Undersampling_Mean Imputation_f...
17 Decision Trees_Undersampling_Mean Imputation_f...
18 Decision Trees_Undersampling_Mean Imputation_f...
19 Decision Trees_Undersampling_Mean Imputation_f...
20 Decision Trees_Undersampling_Mean Imputation_f...
21 Decision Trees_Undersampling_Mean Imputation_f...
22 Decision Trees_Undersampling_Mean Imputation_f...
23 Decision Trees_Undersampling_Mean Imputation_f...
24 Decision Trees_Undersampling_Mean Imputation_f...
25 Decision Trees_Undersampling_Mean Imputation_f...
26 Decision Trees_Undersampling_Mean Imputation_f...
27 Decision Trees_Undersampling_Mean Imputation_f...
28 Decision Trees_Undersampling_Mean Imputation_f...
29 Decision Trees_Undersampling_Mean Imputation_f...
30 Decision Trees_Undersampling_Mean Imputation_f...
31 Decision Trees_Undersampling_Mean Imputation_f...
32 Decision Trees_Undersampling_Mean Imputation_f...
33 Decision Trees_Undersampling_Mean Imputation_f...
34 Decision Trees_Undersampling_Mean Imputation_f...
35 Decision Trees_Undersampling_Mean Imputation_f...
36 Decision Trees_Undersampling_Mean Imputation_f...
37 Decision Trees_Undersampling_Mean Imputation_f...
38 Decision Trees_Undersampling_Mean Imputation_f...
39 Decision Trees_Undersampling_Mean Imputation_f...
40 Decision Trees_Undersampling_Mean Imputation_f...
41 Decision Trees_Undersampling_Mean Imputation_f...
42 Decision Trees_Undersampling_Mean Imputation_f...
43 Decision Trees_Undersampling_Mean Imputation_f...
44 Decision Trees_Undersampling_Mean Imputation_f...
45 Decision Trees_Undersampling_Mean Imputation_f...
46 Decision Trees_Undersampling_Mean Imputation_f...
47 Decision Trees_Undersampling_Mean Imputation_f...
48 Decision Trees_Undersampling_Mean Imputation_f...
49 Decision Trees_Undersampling_Mean Imputation_f...
50 Decision Trees_Undersampling_Mean Imputation_f...
51 Decision Trees_Undersampling_Mean Imputation_f...
52 Decision Trees_Undersampling_Mean Imputation_f...
53 Decision Trees_Undersampling_Mean Imputation_f...
54 Decision Trees_Undersampling_Mean Imputation_f...
55 Decision Trees_Undersampling_Mean Imputation_f...
56 Decision Trees_Undersampling_Mean Imputation_f...
57 Decision Trees_Undersampling_Mean Imputation_f...
58 Decision Trees_Undersampling_Mean Imputation_f...
59 Decision Trees_Undersampling_Mean Imputation_f...
60 Decision Trees_Undersampling_Mean Imputation_f...
61 Decision Trees_Undersampling_Mean Imputation_f...
62 Decision Trees_Undersampling_Mean Imputation_f...
63 Decision Trees_Undersampling_Mean Imputation_f...
64 Decision Trees_Undersampling_Mean Imputation_f...
65 Decision Trees_Undersampling_Mean Imputation_f...
66 Decision Trees_Undersampling_Mean Imputation_f...
67 Decision Trees_Undersampling_Mean Imputation_f...
68 Decision Trees_Undersampling_Mean Imputation_f...
69 Decision Trees_Undersampling_Mean Imputation_f...
70 Decision Trees_Undersampling_Mean Imputation_f...
71 Decision Trees_Undersampling_Mean Imputation_f...
72 Decision Trees_Undersampling_Mean Imputation_f...
73 Decision Trees_Undersampling_Mean Imputation_f...
74 Decision Trees_Undersampling_Mean Imputation_f...
75 Decision Trees_Undersampling_Mean Imputation_f...
76 Decision Trees_Undersampling_Mean Imputation_f...
77 Decision Trees_Undersampling_Mean Imputation_f...
78 Decision Trees_Undersampling_Mean Imputation_f...
79 Decision Trees_Undersampling_Mean Imputation_f...
80 Decision Trees_Undersampling_Mean Imputation_f...
81 Decision Trees_Undersampling_Mean Imputation_f...
82 Decision Trees_Undersampling_Mean Imputation_f...
83 Decision Trees_Undersampling_Mean Imputation_f...
84 Decision Trees_Undersampling_Mean Imputation_f...
85 Decision Trees_Undersampling_Mean Imputation_f...
86 Decision Trees_Undersampling_Mean Imputation_f...
87 Decision Trees_Undersampling_Mean Imputation_f...
88 Decision Trees_Undersampling_Mean Imputation_f...
89 Decision Trees_Undersampling_Mean Imputation_f...
90 Decision Trees_Undersampling_Mean Imputation_f...
91 Decision Trees_Undersampling_Mean Imputation_f...
92 Decision Trees_Undersampling_Mean Imputation_f...
93 Decision Trees_Undersampling_Mean Imputation_f...
94 Decision Trees_Undersampling_Mean Imputation_f... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Decision Trees 0.570714 NaN Median Imputation
1 Decision Trees 0.080674 NaN Median Imputation
2 Decision Trees 0.565401 NaN Median Imputation
3 Decision Trees 0.141201 NaN Median Imputation
4 Decision Trees 0.565989 NaN Median Imputation
5 Decision Trees 0.138154 NaN Median Imputation
6 Decision Trees 0.131977 NaN Median Imputation
7 Decision Trees NaN 0.0 Median Imputation
8 Decision Trees NaN None Median Imputation
9 Decision Trees NaN gini Median Imputation
10 Decision Trees NaN None Median Imputation
11 Decision Trees NaN None Median Imputation
12 Decision Trees NaN None Median Imputation
13 Decision Trees NaN 0.0 Median Imputation
14 Decision Trees NaN 1 Median Imputation
15 Decision Trees NaN 2 Median Imputation
16 Decision Trees NaN 0.0 Median Imputation
17 Decision Trees NaN None Median Imputation
18 Decision Trees NaN best Median Imputation
19 Decision Trees 0.569134 NaN Median Imputation
20 Decision Trees 0.051766 NaN Median Imputation
21 Decision Trees 0.518293 NaN Median Imputation
22 Decision Trees 0.094131 NaN Median Imputation
23 Decision Trees 0.550633 NaN Median Imputation
24 Decision Trees 0.125798 NaN Median Imputation
25 Decision Trees 0.101265 NaN Median Imputation
26 Decision Trees NaN 0.0 Median Imputation
27 Decision Trees NaN None Median Imputation
28 Decision Trees NaN gini Median Imputation
29 Decision Trees NaN None Median Imputation
30 Decision Trees NaN None Median Imputation
31 Decision Trees NaN None Median Imputation
32 Decision Trees NaN 0.0 Median Imputation
33 Decision Trees NaN 1 Median Imputation
34 Decision Trees NaN 2 Median Imputation
35 Decision Trees NaN 0.0 Median Imputation
36 Decision Trees NaN None Median Imputation
37 Decision Trees NaN best Median Imputation
38 Decision Trees 0.572031 NaN Median Imputation
39 Decision Trees 0.063714 NaN Median Imputation
40 Decision Trees 0.561497 NaN Median Imputation
41 Decision Trees 0.114441 NaN Median Imputation
42 Decision Trees 0.549744 NaN Median Imputation
43 Decision Trees 0.134074 NaN Median Imputation
44 Decision Trees 0.099489 NaN Median Imputation
45 Decision Trees NaN 0.0 Median Imputation
46 Decision Trees NaN None Median Imputation
47 Decision Trees NaN gini Median Imputation
48 Decision Trees NaN None Median Imputation
49 Decision Trees NaN None Median Imputation
50 Decision Trees NaN None Median Imputation
51 Decision Trees NaN 0.0 Median Imputation
52 Decision Trees NaN 1 Median Imputation
53 Decision Trees NaN 2 Median Imputation
54 Decision Trees NaN 0.0 Median Imputation
55 Decision Trees NaN None Median Imputation
56 Decision Trees NaN best Median Imputation
57 Decision Trees 0.561117 NaN Median Imputation
58 Decision Trees 0.067647 NaN Median Imputation
59 Decision Trees 0.586735 NaN Median Imputation
60 Decision Trees 0.121308 NaN Median Imputation
61 Decision Trees 0.568175 NaN Median Imputation
62 Decision Trees 0.146457 NaN Median Imputation
63 Decision Trees 0.136349 NaN Median Imputation
64 Decision Trees NaN 0.0 Median Imputation
65 Decision Trees NaN None Median Imputation
66 Decision Trees NaN gini Median Imputation
67 Decision Trees NaN None Median Imputation
68 Decision Trees NaN None Median Imputation
69 Decision Trees NaN None Median Imputation
70 Decision Trees NaN 0.0 Median Imputation
71 Decision Trees NaN 1 Median Imputation
72 Decision Trees NaN 2 Median Imputation
73 Decision Trees NaN 0.0 Median Imputation
74 Decision Trees NaN None Median Imputation
75 Decision Trees NaN best Median Imputation
76 Decision Trees 0.566649 NaN Median Imputation
77 Decision Trees 0.080446 NaN Median Imputation
78 Decision Trees 0.634259 NaN Median Imputation
79 Decision Trees 0.142783 NaN Median Imputation
80 Decision Trees 0.596461 NaN Median Imputation
81 Decision Trees 0.205411 NaN Median Imputation
82 Decision Trees 0.192922 NaN Median Imputation
83 Decision Trees NaN 0.0 Median Imputation
84 Decision Trees NaN None Median Imputation
85 Decision Trees NaN gini Median Imputation
86 Decision Trees NaN None Median Imputation
87 Decision Trees NaN None Median Imputation
88 Decision Trees NaN None Median Imputation
89 Decision Trees NaN 0.0 Median Imputation
90 Decision Trees NaN 1 Median Imputation
91 Decision Trees NaN 2 Median Imputation
92 Decision Trees NaN 0.0 Median Imputation
93 Decision Trees NaN None Median Imputation
94 Decision Trees NaN best Median Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
45 Undersampling
46 Undersampling
47 Undersampling
48 Undersampling
49 Undersampling
50 Undersampling
51 Undersampling
52 Undersampling
53 Undersampling
54 Undersampling
55 Undersampling
56 Undersampling
57 Undersampling
58 Undersampling
59 Undersampling
60 Undersampling
61 Undersampling
62 Undersampling
63 Undersampling
64 Undersampling
65 Undersampling
66 Undersampling
67 Undersampling
68 Undersampling
69 Undersampling
70 Undersampling
71 Undersampling
72 Undersampling
73 Undersampling
74 Undersampling
75 Undersampling
76 Undersampling
77 Undersampling
78 Undersampling
79 Undersampling
80 Undersampling
81 Undersampling
82 Undersampling
83 Undersampling
84 Undersampling
85 Undersampling
86 Undersampling
87 Undersampling
88 Undersampling
89 Undersampling
90 Undersampling
91 Undersampling
92 Undersampling
93 Undersampling
94 Undersampling
Model Unique Code
0 Decision Trees_Undersampling_Median Imputation...
1 Decision Trees_Undersampling_Median Imputation...
2 Decision Trees_Undersampling_Median Imputation...
3 Decision Trees_Undersampling_Median Imputation...
4 Decision Trees_Undersampling_Median Imputation...
5 Decision Trees_Undersampling_Median Imputation...
6 Decision Trees_Undersampling_Median Imputation...
7 Decision Trees_Undersampling_Median Imputation...
8 Decision Trees_Undersampling_Median Imputation...
9 Decision Trees_Undersampling_Median Imputation...
10 Decision Trees_Undersampling_Median Imputation...
11 Decision Trees_Undersampling_Median Imputation...
12 Decision Trees_Undersampling_Median Imputation...
13 Decision Trees_Undersampling_Median Imputation...
14 Decision Trees_Undersampling_Median Imputation...
15 Decision Trees_Undersampling_Median Imputation...
16 Decision Trees_Undersampling_Median Imputation...
17 Decision Trees_Undersampling_Median Imputation...
18 Decision Trees_Undersampling_Median Imputation...
19 Decision Trees_Undersampling_Median Imputation...
20 Decision Trees_Undersampling_Median Imputation...
21 Decision Trees_Undersampling_Median Imputation...
22 Decision Trees_Undersampling_Median Imputation...
23 Decision Trees_Undersampling_Median Imputation...
24 Decision Trees_Undersampling_Median Imputation...
25 Decision Trees_Undersampling_Median Imputation...
26 Decision Trees_Undersampling_Median Imputation...
27 Decision Trees_Undersampling_Median Imputation...
28 Decision Trees_Undersampling_Median Imputation...
29 Decision Trees_Undersampling_Median Imputation...
30 Decision Trees_Undersampling_Median Imputation...
31 Decision Trees_Undersampling_Median Imputation...
32 Decision Trees_Undersampling_Median Imputation...
33 Decision Trees_Undersampling_Median Imputation...
34 Decision Trees_Undersampling_Median Imputation...
35 Decision Trees_Undersampling_Median Imputation...
36 Decision Trees_Undersampling_Median Imputation...
37 Decision Trees_Undersampling_Median Imputation...
38 Decision Trees_Undersampling_Median Imputation...
39 Decision Trees_Undersampling_Median Imputation...
40 Decision Trees_Undersampling_Median Imputation...
41 Decision Trees_Undersampling_Median Imputation...
42 Decision Trees_Undersampling_Median Imputation...
43 Decision Trees_Undersampling_Median Imputation...
44 Decision Trees_Undersampling_Median Imputation...
45 Decision Trees_Undersampling_Median Imputation...
46 Decision Trees_Undersampling_Median Imputation...
47 Decision Trees_Undersampling_Median Imputation...
48 Decision Trees_Undersampling_Median Imputation...
49 Decision Trees_Undersampling_Median Imputation...
50 Decision Trees_Undersampling_Median Imputation...
51 Decision Trees_Undersampling_Median Imputation...
52 Decision Trees_Undersampling_Median Imputation...
53 Decision Trees_Undersampling_Median Imputation...
54 Decision Trees_Undersampling_Median Imputation...
55 Decision Trees_Undersampling_Median Imputation...
56 Decision Trees_Undersampling_Median Imputation...
57 Decision Trees_Undersampling_Median Imputation...
58 Decision Trees_Undersampling_Median Imputation...
59 Decision Trees_Undersampling_Median Imputation...
60 Decision Trees_Undersampling_Median Imputation...
61 Decision Trees_Undersampling_Median Imputation...
62 Decision Trees_Undersampling_Median Imputation...
63 Decision Trees_Undersampling_Median Imputation...
64 Decision Trees_Undersampling_Median Imputation...
65 Decision Trees_Undersampling_Median Imputation...
66 Decision Trees_Undersampling_Median Imputation...
67 Decision Trees_Undersampling_Median Imputation...
68 Decision Trees_Undersampling_Median Imputation...
69 Decision Trees_Undersampling_Median Imputation...
70 Decision Trees_Undersampling_Median Imputation...
71 Decision Trees_Undersampling_Median Imputation...
72 Decision Trees_Undersampling_Median Imputation...
73 Decision Trees_Undersampling_Median Imputation...
74 Decision Trees_Undersampling_Median Imputation...
75 Decision Trees_Undersampling_Median Imputation...
76 Decision Trees_Undersampling_Median Imputation...
77 Decision Trees_Undersampling_Median Imputation...
78 Decision Trees_Undersampling_Median Imputation...
79 Decision Trees_Undersampling_Median Imputation...
80 Decision Trees_Undersampling_Median Imputation...
81 Decision Trees_Undersampling_Median Imputation...
82 Decision Trees_Undersampling_Median Imputation...
83 Decision Trees_Undersampling_Median Imputation...
84 Decision Trees_Undersampling_Median Imputation...
85 Decision Trees_Undersampling_Median Imputation...
86 Decision Trees_Undersampling_Median Imputation...
87 Decision Trees_Undersampling_Median Imputation...
88 Decision Trees_Undersampling_Median Imputation...
89 Decision Trees_Undersampling_Median Imputation...
90 Decision Trees_Undersampling_Median Imputation...
91 Decision Trees_Undersampling_Median Imputation...
92 Decision Trees_Undersampling_Median Imputation...
93 Decision Trees_Undersampling_Median Imputation...
94 Decision Trees_Undersampling_Median Imputation... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Decision Trees 0.837767 NaN Zero Imputation
1 Decision Trees 0.107660 NaN Zero Imputation
2 Decision Trees 0.219409 NaN Zero Imputation
3 Decision Trees 0.144444 NaN Zero Imputation
4 Decision Trees 0.549171 NaN Zero Imputation
5 Decision Trees 0.098342 NaN Zero Imputation
6 Decision Trees 0.098342 NaN Zero Imputation
7 Decision Trees NaN 0.0 Zero Imputation
8 Decision Trees NaN None Zero Imputation
9 Decision Trees NaN gini Zero Imputation
10 Decision Trees NaN None Zero Imputation
11 Decision Trees NaN None Zero Imputation
12 Decision Trees NaN None Zero Imputation
13 Decision Trees NaN 0.0 Zero Imputation
14 Decision Trees NaN 1 Zero Imputation
15 Decision Trees NaN 2 Zero Imputation
16 Decision Trees NaN 0.0 Zero Imputation
17 Decision Trees NaN None Zero Imputation
18 Decision Trees NaN best Zero Imputation
19 Decision Trees 0.828812 NaN Zero Imputation
20 Decision Trees 0.078125 NaN Zero Imputation
21 Decision Trees 0.274390 NaN Zero Imputation
22 Decision Trees 0.121622 NaN Zero Imputation
23 Decision Trees 0.564115 NaN Zero Imputation
24 Decision Trees 0.128230 NaN Zero Imputation
25 Decision Trees 0.128230 NaN Zero Imputation
26 Decision Trees NaN 0.0 Zero Imputation
27 Decision Trees NaN None Zero Imputation
28 Decision Trees NaN gini Zero Imputation
29 Decision Trees NaN None Zero Imputation
30 Decision Trees NaN None Zero Imputation
31 Decision Trees NaN None Zero Imputation
32 Decision Trees NaN 0.0 Zero Imputation
33 Decision Trees NaN 1 Zero Imputation
34 Decision Trees NaN 2 Zero Imputation
35 Decision Trees NaN 0.0 Zero Imputation
36 Decision Trees NaN None Zero Imputation
37 Decision Trees NaN best Zero Imputation
38 Decision Trees 0.830919 NaN Zero Imputation
39 Decision Trees 0.076350 NaN Zero Imputation
40 Decision Trees 0.219251 NaN Zero Imputation
41 Decision Trees 0.113260 NaN Zero Imputation
42 Decision Trees 0.540928 NaN Zero Imputation
43 Decision Trees 0.081855 NaN Zero Imputation
44 Decision Trees 0.081855 NaN Zero Imputation
45 Decision Trees NaN 0.0 Zero Imputation
46 Decision Trees NaN None Zero Imputation
47 Decision Trees NaN gini Zero Imputation
48 Decision Trees NaN None Zero Imputation
49 Decision Trees NaN None Zero Imputation
50 Decision Trees NaN None Zero Imputation
51 Decision Trees NaN 0.0 Zero Imputation
52 Decision Trees NaN 1 Zero Imputation
53 Decision Trees NaN 2 Zero Imputation
54 Decision Trees NaN 0.0 Zero Imputation
55 Decision Trees NaN None Zero Imputation
56 Decision Trees NaN best Zero Imputation
57 Decision Trees 0.843783 NaN Zero Imputation
58 Decision Trees 0.100604 NaN Zero Imputation
59 Decision Trees 0.255102 NaN Zero Imputation
60 Decision Trees 0.144300 NaN Zero Imputation
61 Decision Trees 0.565468 NaN Zero Imputation
62 Decision Trees 0.130935 NaN Zero Imputation
63 Decision Trees 0.130935 NaN Zero Imputation
64 Decision Trees NaN 0.0 Zero Imputation
65 Decision Trees NaN None Zero Imputation
66 Decision Trees NaN gini Zero Imputation
67 Decision Trees NaN None Zero Imputation
68 Decision Trees NaN None Zero Imputation
69 Decision Trees NaN None Zero Imputation
70 Decision Trees NaN 0.0 Zero Imputation
71 Decision Trees NaN 1 Zero Imputation
72 Decision Trees NaN 2 Zero Imputation
73 Decision Trees NaN 0.0 Zero Imputation
74 Decision Trees NaN None Zero Imputation
75 Decision Trees NaN best Zero Imputation
76 Decision Trees 0.832719 NaN Zero Imputation
77 Decision Trees 0.090020 NaN Zero Imputation
78 Decision Trees 0.212963 NaN Zero Imputation
79 Decision Trees 0.126547 NaN Zero Imputation
80 Decision Trees 0.541537 NaN Zero Imputation
81 Decision Trees 0.083075 NaN Zero Imputation
82 Decision Trees 0.083075 NaN Zero Imputation
83 Decision Trees NaN 0.0 Zero Imputation
84 Decision Trees NaN None Zero Imputation
85 Decision Trees NaN gini Zero Imputation
86 Decision Trees NaN None Zero Imputation
87 Decision Trees NaN None Zero Imputation
88 Decision Trees NaN None Zero Imputation
89 Decision Trees NaN 0.0 Zero Imputation
90 Decision Trees NaN 1 Zero Imputation
91 Decision Trees NaN 2 Zero Imputation
92 Decision Trees NaN 0.0 Zero Imputation
93 Decision Trees NaN None Zero Imputation
94 Decision Trees NaN best Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
1 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
2 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
3 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
4 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
5 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
6 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
7 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
8 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
9 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
10 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
11 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
12 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
13 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
14 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
15 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
16 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
17 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
18 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
19 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
20 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
21 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
22 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
23 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
24 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
25 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
26 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
27 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
28 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
29 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
30 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
31 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
32 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
33 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
34 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
35 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
36 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
37 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
38 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
39 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
40 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
41 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
42 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
43 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
44 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
45 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
46 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
47 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
48 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
49 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
50 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
51 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
52 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
53 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
54 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
55 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
56 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
57 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
58 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
59 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
60 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
61 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
62 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
63 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
64 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
65 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
66 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
67 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
68 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
69 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
70 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
71 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
72 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
73 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
74 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
75 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
76 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
77 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
78 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
79 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
80 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
81 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
82 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
83 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
84 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
85 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
86 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
87 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
88 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
89 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
90 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
91 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
92 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
93 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero...
94 Decision Trees_Hybrid Sampling (SMOTEENN)_Zero... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Decision Trees 0.838293 NaN Mean Imputation
1 Decision Trees 0.108108 NaN Mean Imputation
2 Decision Trees 0.219409 NaN Mean Imputation
3 Decision Trees 0.144847 NaN Mean Imputation
4 Decision Trees 0.549452 NaN Mean Imputation
5 Decision Trees 0.098904 NaN Mean Imputation
6 Decision Trees 0.098904 NaN Mean Imputation
7 Decision Trees NaN 0.0 Mean Imputation
8 Decision Trees NaN None Mean Imputation
9 Decision Trees NaN gini Mean Imputation
10 Decision Trees NaN None Mean Imputation
11 Decision Trees NaN None Mean Imputation
12 Decision Trees NaN None Mean Imputation
13 Decision Trees NaN 0.0 Mean Imputation
14 Decision Trees NaN 1 Mean Imputation
15 Decision Trees NaN 2 Mean Imputation
16 Decision Trees NaN 0.0 Mean Imputation
17 Decision Trees NaN None Mean Imputation
18 Decision Trees NaN best Mean Imputation
19 Decision Trees 0.829076 NaN Mean Imputation
20 Decision Trees 0.073814 NaN Mean Imputation
21 Decision Trees 0.256098 NaN Mean Imputation
22 Decision Trees 0.114598 NaN Mean Imputation
23 Decision Trees 0.555519 NaN Mean Imputation
24 Decision Trees 0.111038 NaN Mean Imputation
25 Decision Trees 0.111038 NaN Mean Imputation
26 Decision Trees NaN 0.0 Mean Imputation
27 Decision Trees NaN None Mean Imputation
28 Decision Trees NaN gini Mean Imputation
29 Decision Trees NaN None Mean Imputation
30 Decision Trees NaN None Mean Imputation
31 Decision Trees NaN None Mean Imputation
32 Decision Trees NaN 0.0 Mean Imputation
33 Decision Trees NaN 1 Mean Imputation
34 Decision Trees NaN 2 Mean Imputation
35 Decision Trees NaN 0.0 Mean Imputation
36 Decision Trees NaN None Mean Imputation
37 Decision Trees NaN best Mean Imputation
38 Decision Trees 0.835923 NaN Mean Imputation
39 Decision Trees 0.072549 NaN Mean Imputation
40 Decision Trees 0.197861 NaN Mean Imputation
41 Decision Trees 0.106169 NaN Mean Imputation
42 Decision Trees 0.533418 NaN Mean Imputation
43 Decision Trees 0.066836 NaN Mean Imputation
44 Decision Trees 0.066836 NaN Mean Imputation
45 Decision Trees NaN 0.0 Mean Imputation
46 Decision Trees NaN None Mean Imputation
47 Decision Trees NaN gini Mean Imputation
48 Decision Trees NaN None Mean Imputation
49 Decision Trees NaN None Mean Imputation
50 Decision Trees NaN None Mean Imputation
51 Decision Trees NaN 0.0 Mean Imputation
52 Decision Trees NaN 1 Mean Imputation
53 Decision Trees NaN 2 Mean Imputation
54 Decision Trees NaN 0.0 Mean Imputation
55 Decision Trees NaN None Mean Imputation
56 Decision Trees NaN best Mean Imputation
57 Decision Trees 0.850632 NaN Mean Imputation
58 Decision Trees 0.094092 NaN Mean Imputation
59 Decision Trees 0.219388 NaN Mean Imputation
60 Decision Trees 0.131700 NaN Mean Imputation
61 Decision Trees 0.552194 NaN Mean Imputation
62 Decision Trees 0.104388 NaN Mean Imputation
63 Decision Trees 0.104388 NaN Mean Imputation
64 Decision Trees NaN 0.0 Mean Imputation
65 Decision Trees NaN None Mean Imputation
66 Decision Trees NaN gini Mean Imputation
67 Decision Trees NaN None Mean Imputation
68 Decision Trees NaN None Mean Imputation
69 Decision Trees NaN None Mean Imputation
70 Decision Trees NaN 0.0 Mean Imputation
71 Decision Trees NaN 1 Mean Imputation
72 Decision Trees NaN 2 Mean Imputation
73 Decision Trees NaN 0.0 Mean Imputation
74 Decision Trees NaN None Mean Imputation
75 Decision Trees NaN best Mean Imputation
76 Decision Trees 0.831665 NaN Mean Imputation
77 Decision Trees 0.098672 NaN Mean Imputation
78 Decision Trees 0.240741 NaN Mean Imputation
79 Decision Trees 0.139973 NaN Mean Imputation
80 Decision Trees 0.554030 NaN Mean Imputation
81 Decision Trees 0.108059 NaN Mean Imputation
82 Decision Trees 0.108059 NaN Mean Imputation
83 Decision Trees NaN 0.0 Mean Imputation
84 Decision Trees NaN None Mean Imputation
85 Decision Trees NaN gini Mean Imputation
86 Decision Trees NaN None Mean Imputation
87 Decision Trees NaN None Mean Imputation
88 Decision Trees NaN None Mean Imputation
89 Decision Trees NaN 0.0 Mean Imputation
90 Decision Trees NaN 1 Mean Imputation
91 Decision Trees NaN 2 Mean Imputation
92 Decision Trees NaN 0.0 Mean Imputation
93 Decision Trees NaN None Mean Imputation
94 Decision Trees NaN best Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
1 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
2 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
3 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
4 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
5 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
6 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
7 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
8 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
9 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
10 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
11 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
12 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
13 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
14 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
15 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
16 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
17 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
18 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
19 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
20 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
21 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
22 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
23 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
24 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
25 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
26 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
27 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
28 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
29 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
30 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
31 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
32 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
33 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
34 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
35 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
36 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
37 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
38 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
39 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
40 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
41 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
42 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
43 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
44 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
45 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
46 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
47 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
48 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
49 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
50 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
51 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
52 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
53 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
54 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
55 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
56 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
57 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
58 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
59 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
60 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
61 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
62 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
63 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
64 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
65 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
66 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
67 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
68 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
69 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
70 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
71 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
72 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
73 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
74 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
75 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
76 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
77 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
78 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
79 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
80 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
81 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
82 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
83 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
84 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
85 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
86 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
87 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
88 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
89 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
90 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
91 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
92 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
93 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean...
94 Decision Trees_Hybrid Sampling (SMOTEENN)_Mean... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Decision Trees 0.839083 NaN Median Imputation
1 Decision Trees 0.103814 NaN Median Imputation
2 Decision Trees 0.206751 NaN Median Imputation
3 Decision Trees 0.138223 NaN Median Imputation
4 Decision Trees 0.543965 NaN Median Imputation
5 Decision Trees 0.087931 NaN Median Imputation
6 Decision Trees 0.087931 NaN Median Imputation
7 Decision Trees NaN 0.0 Median Imputation
8 Decision Trees NaN None Median Imputation
9 Decision Trees NaN gini Median Imputation
10 Decision Trees NaN None Median Imputation
11 Decision Trees NaN None Median Imputation
12 Decision Trees NaN None Median Imputation
13 Decision Trees NaN 0.0 Median Imputation
14 Decision Trees NaN 1 Median Imputation
15 Decision Trees NaN 2 Median Imputation
16 Decision Trees NaN 0.0 Median Imputation
17 Decision Trees NaN None Median Imputation
18 Decision Trees NaN best Median Imputation
19 Decision Trees 0.824072 NaN Median Imputation
20 Decision Trees 0.056338 NaN Median Imputation
21 Decision Trees 0.195122 NaN Median Imputation
22 Decision Trees 0.087432 NaN Median Imputation
23 Decision Trees 0.523793 NaN Median Imputation
24 Decision Trees 0.047585 NaN Median Imputation
25 Decision Trees 0.047585 NaN Median Imputation
26 Decision Trees NaN 0.0 Median Imputation
27 Decision Trees NaN None Median Imputation
28 Decision Trees NaN gini Median Imputation
29 Decision Trees NaN None Median Imputation
30 Decision Trees NaN None Median Imputation
31 Decision Trees NaN None Median Imputation
32 Decision Trees NaN 0.0 Median Imputation
33 Decision Trees NaN 1 Median Imputation
34 Decision Trees NaN 2 Median Imputation
35 Decision Trees NaN 0.0 Median Imputation
36 Decision Trees NaN None Median Imputation
37 Decision Trees NaN best Median Imputation
38 Decision Trees 0.831446 NaN Median Imputation
39 Decision Trees 0.079777 NaN Median Imputation
40 Decision Trees 0.229947 NaN Median Imputation
41 Decision Trees 0.118457 NaN Median Imputation
42 Decision Trees 0.546275 NaN Median Imputation
43 Decision Trees 0.092550 NaN Median Imputation
44 Decision Trees 0.092550 NaN Median Imputation
45 Decision Trees NaN 0.0 Median Imputation
46 Decision Trees NaN None Median Imputation
47 Decision Trees NaN gini Median Imputation
48 Decision Trees NaN None Median Imputation
49 Decision Trees NaN None Median Imputation
50 Decision Trees NaN None Median Imputation
51 Decision Trees NaN 0.0 Median Imputation
52 Decision Trees NaN 1 Median Imputation
53 Decision Trees NaN 2 Median Imputation
54 Decision Trees NaN 0.0 Median Imputation
55 Decision Trees NaN None Median Imputation
56 Decision Trees NaN best Median Imputation
57 Decision Trees 0.834036 NaN Median Imputation
58 Decision Trees 0.085878 NaN Median Imputation
59 Decision Trees 0.229592 NaN Median Imputation
60 Decision Trees 0.125000 NaN Median Imputation
61 Decision Trees 0.548268 NaN Median Imputation
62 Decision Trees 0.096536 NaN Median Imputation
63 Decision Trees 0.096536 NaN Median Imputation
64 Decision Trees NaN 0.0 Median Imputation
65 Decision Trees NaN None Median Imputation
66 Decision Trees NaN gini Median Imputation
67 Decision Trees NaN None Median Imputation
68 Decision Trees NaN None Median Imputation
69 Decision Trees NaN None Median Imputation
70 Decision Trees NaN 0.0 Median Imputation
71 Decision Trees NaN 1 Median Imputation
72 Decision Trees NaN 2 Median Imputation
73 Decision Trees NaN 0.0 Median Imputation
74 Decision Trees NaN None Median Imputation
75 Decision Trees NaN best Median Imputation
76 Decision Trees 0.825079 NaN Median Imputation
77 Decision Trees 0.089744 NaN Median Imputation
78 Decision Trees 0.226852 NaN Median Imputation
79 Decision Trees 0.128609 NaN Median Imputation
80 Decision Trees 0.544013 NaN Median Imputation
81 Decision Trees 0.088025 NaN Median Imputation
82 Decision Trees 0.088025 NaN Median Imputation
83 Decision Trees NaN 0.0 Median Imputation
84 Decision Trees NaN None Median Imputation
85 Decision Trees NaN gini Median Imputation
86 Decision Trees NaN None Median Imputation
87 Decision Trees NaN None Median Imputation
88 Decision Trees NaN None Median Imputation
89 Decision Trees NaN 0.0 Median Imputation
90 Decision Trees NaN 1 Median Imputation
91 Decision Trees NaN 2 Median Imputation
92 Decision Trees NaN 0.0 Median Imputation
93 Decision Trees NaN None Median Imputation
94 Decision Trees NaN best Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
1 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
2 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
3 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
4 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
5 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
6 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
7 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
8 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
9 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
10 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
11 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
12 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
13 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
14 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
15 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
16 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
17 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
18 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
19 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
20 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
21 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
22 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
23 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
24 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
25 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
26 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
27 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
28 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
29 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
30 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
31 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
32 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
33 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
34 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
35 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
36 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
37 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
38 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
39 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
40 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
41 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
42 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
43 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
44 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
45 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
46 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
47 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
48 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
49 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
50 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
51 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
52 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
53 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
54 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
55 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
56 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
57 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
58 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
59 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
60 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
61 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
62 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
63 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
64 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
65 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
66 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
67 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
68 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
69 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
70 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
71 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
72 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
73 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
74 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
75 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
76 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
77 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
78 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
79 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
80 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
81 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
82 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
83 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
84 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
85 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
86 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
87 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
88 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
89 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
90 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
91 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
92 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
93 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi...
94 Decision Trees_Hybrid Sampling (SMOTEENN)_Medi... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Decision Trees 0.854622 NaN Zero Imputation
1 Decision Trees 0.097187 NaN Zero Imputation
2 Decision Trees 0.160338 NaN Zero Imputation
3 Decision Trees 0.121019 NaN Zero Imputation
4 Decision Trees 0.541203 NaN Zero Imputation
5 Decision Trees 0.084267 NaN Zero Imputation
6 Decision Trees 0.082405 NaN Zero Imputation
7 Decision Trees NaN 0.0 Zero Imputation
8 Decision Trees NaN None Zero Imputation
9 Decision Trees NaN gini Zero Imputation
10 Decision Trees NaN None Zero Imputation
11 Decision Trees NaN None Zero Imputation
12 Decision Trees NaN None Zero Imputation
13 Decision Trees NaN 0.0 Zero Imputation
14 Decision Trees NaN 1 Zero Imputation
15 Decision Trees NaN 2 Zero Imputation
16 Decision Trees NaN 0.0 Zero Imputation
17 Decision Trees NaN None Zero Imputation
18 Decision Trees NaN best Zero Imputation
19 Decision Trees 0.861733 NaN Zero Imputation
20 Decision Trees 0.069212 NaN Zero Imputation
21 Decision Trees 0.176829 NaN Zero Imputation
22 Decision Trees 0.099485 NaN Zero Imputation
23 Decision Trees 0.570569 NaN Zero Imputation
24 Decision Trees 0.150801 NaN Zero Imputation
25 Decision Trees 0.141138 NaN Zero Imputation
26 Decision Trees NaN 0.0 Zero Imputation
27 Decision Trees NaN None Zero Imputation
28 Decision Trees NaN gini Zero Imputation
29 Decision Trees NaN None Zero Imputation
30 Decision Trees NaN None Zero Imputation
31 Decision Trees NaN None Zero Imputation
32 Decision Trees NaN 0.0 Zero Imputation
33 Decision Trees NaN 1 Zero Imputation
34 Decision Trees NaN 2 Zero Imputation
35 Decision Trees NaN 0.0 Zero Imputation
36 Decision Trees NaN None Zero Imputation
37 Decision Trees NaN best Zero Imputation
38 Decision Trees 0.861470 NaN Zero Imputation
39 Decision Trees 0.081481 NaN Zero Imputation
40 Decision Trees 0.176471 NaN Zero Imputation
41 Decision Trees 0.111486 NaN Zero Imputation
42 Decision Trees 0.537723 NaN Zero Imputation
43 Decision Trees 0.086588 NaN Zero Imputation
44 Decision Trees 0.075446 NaN Zero Imputation
45 Decision Trees NaN 0.0 Zero Imputation
46 Decision Trees NaN None Zero Imputation
47 Decision Trees NaN gini Zero Imputation
48 Decision Trees NaN None Zero Imputation
49 Decision Trees NaN None Zero Imputation
50 Decision Trees NaN None Zero Imputation
51 Decision Trees NaN 0.0 Zero Imputation
52 Decision Trees NaN 1 Zero Imputation
53 Decision Trees NaN 2 Zero Imputation
54 Decision Trees NaN 0.0 Zero Imputation
55 Decision Trees NaN None Zero Imputation
56 Decision Trees NaN best Zero Imputation
57 Decision Trees 0.863277 NaN Zero Imputation
58 Decision Trees 0.084833 NaN Zero Imputation
59 Decision Trees 0.168367 NaN Zero Imputation
60 Decision Trees 0.112821 NaN Zero Imputation
61 Decision Trees 0.531852 NaN Zero Imputation
62 Decision Trees 0.070312 NaN Zero Imputation
63 Decision Trees 0.063705 NaN Zero Imputation
64 Decision Trees NaN 0.0 Zero Imputation
65 Decision Trees NaN None Zero Imputation
66 Decision Trees NaN gini Zero Imputation
67 Decision Trees NaN None Zero Imputation
68 Decision Trees NaN None Zero Imputation
69 Decision Trees NaN None Zero Imputation
70 Decision Trees NaN 0.0 Zero Imputation
71 Decision Trees NaN 1 Zero Imputation
72 Decision Trees NaN 2 Zero Imputation
73 Decision Trees NaN 0.0 Zero Imputation
74 Decision Trees NaN None Zero Imputation
75 Decision Trees NaN best Zero Imputation
76 Decision Trees 0.865121 NaN Zero Imputation
77 Decision Trees 0.108466 NaN Zero Imputation
78 Decision Trees 0.189815 NaN Zero Imputation
79 Decision Trees 0.138047 NaN Zero Imputation
80 Decision Trees 0.548596 NaN Zero Imputation
81 Decision Trees 0.100631 NaN Zero Imputation
82 Decision Trees 0.097191 NaN Zero Imputation
83 Decision Trees NaN 0.0 Zero Imputation
84 Decision Trees NaN None Zero Imputation
85 Decision Trees NaN gini Zero Imputation
86 Decision Trees NaN None Zero Imputation
87 Decision Trees NaN None Zero Imputation
88 Decision Trees NaN None Zero Imputation
89 Decision Trees NaN 0.0 Zero Imputation
90 Decision Trees NaN 1 Zero Imputation
91 Decision Trees NaN 2 Zero Imputation
92 Decision Trees NaN 0.0 Zero Imputation
93 Decision Trees NaN None Zero Imputation
94 Decision Trees NaN best Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
1 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
2 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
3 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
4 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
5 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
6 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
7 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
8 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
9 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
10 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
11 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
12 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
13 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
14 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
15 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
16 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
17 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
18 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
19 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
20 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
21 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
22 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
23 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
24 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
25 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
26 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
27 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
28 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
29 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
30 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
31 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
32 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
33 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
34 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
35 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
36 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
37 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
38 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
39 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
40 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
41 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
42 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
43 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
44 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
45 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
46 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
47 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
48 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
49 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
50 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
51 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
52 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
53 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
54 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
55 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
56 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
57 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
58 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
59 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
60 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
61 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
62 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
63 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
64 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
65 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
66 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
67 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
68 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
69 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
70 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
71 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
72 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
73 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
74 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
75 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
76 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
77 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
78 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
79 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
80 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
81 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
82 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
83 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
84 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
85 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
86 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
87 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
88 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
89 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
90 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
91 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
92 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
93 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze...
94 Decision Trees_Hybrid Sampling (SMOTETomek)_Ze... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Decision Trees 0.854359 NaN Mean Imputation
1 Decision Trees 0.082011 NaN Mean Imputation
2 Decision Trees 0.130802 NaN Mean Imputation
3 Decision Trees 0.100813 NaN Mean Imputation
4 Decision Trees 0.523625 NaN Mean Imputation
5 Decision Trees 0.049944 NaN Mean Imputation
6 Decision Trees 0.047249 NaN Mean Imputation
7 Decision Trees NaN 0.0 Mean Imputation
8 Decision Trees NaN None Mean Imputation
9 Decision Trees NaN gini Mean Imputation
10 Decision Trees NaN None Mean Imputation
11 Decision Trees NaN None Mean Imputation
12 Decision Trees NaN None Mean Imputation
13 Decision Trees NaN 0.0 Mean Imputation
14 Decision Trees NaN 1 Mean Imputation
15 Decision Trees NaN 2 Mean Imputation
16 Decision Trees NaN 0.0 Mean Imputation
17 Decision Trees NaN None Mean Imputation
18 Decision Trees NaN best Mean Imputation
19 Decision Trees 0.858309 NaN Mean Imputation
20 Decision Trees 0.075000 NaN Mean Imputation
21 Decision Trees 0.201220 NaN Mean Imputation
22 Decision Trees 0.109272 NaN Mean Imputation
23 Decision Trees 0.571739 NaN Mean Imputation
24 Decision Trees 0.153045 NaN Mean Imputation
25 Decision Trees 0.143478 NaN Mean Imputation
26 Decision Trees NaN 0.0 Mean Imputation
27 Decision Trees NaN None Mean Imputation
28 Decision Trees NaN gini Mean Imputation
29 Decision Trees NaN None Mean Imputation
30 Decision Trees NaN None Mean Imputation
31 Decision Trees NaN None Mean Imputation
32 Decision Trees NaN 0.0 Mean Imputation
33 Decision Trees NaN 1 Mean Imputation
34 Decision Trees NaN 2 Mean Imputation
35 Decision Trees NaN 0.0 Mean Imputation
36 Decision Trees NaN None Mean Imputation
37 Decision Trees NaN best Mean Imputation
38 Decision Trees 0.864367 NaN Mean Imputation
39 Decision Trees 0.094059 NaN Mean Imputation
40 Decision Trees 0.203209 NaN Mean Imputation
41 Decision Trees 0.128596 NaN Mean Imputation
42 Decision Trees 0.552771 NaN Mean Imputation
43 Decision Trees 0.115265 NaN Mean Imputation
44 Decision Trees 0.105542 NaN Mean Imputation
45 Decision Trees NaN 0.0 Mean Imputation
46 Decision Trees NaN None Mean Imputation
47 Decision Trees NaN gini Mean Imputation
48 Decision Trees NaN None Mean Imputation
49 Decision Trees NaN None Mean Imputation
50 Decision Trees NaN None Mean Imputation
51 Decision Trees NaN 0.0 Mean Imputation
52 Decision Trees NaN 1 Mean Imputation
53 Decision Trees NaN 2 Mean Imputation
54 Decision Trees NaN 0.0 Mean Imputation
55 Decision Trees NaN None Mean Imputation
56 Decision Trees NaN best Mean Imputation
57 Decision Trees 0.860643 NaN Mean Imputation
58 Decision Trees 0.082707 NaN Mean Imputation
59 Decision Trees 0.168367 NaN Mean Imputation
60 Decision Trees 0.110924 NaN Mean Imputation
61 Decision Trees 0.528842 NaN Mean Imputation
62 Decision Trees 0.068090 NaN Mean Imputation
63 Decision Trees 0.057684 NaN Mean Imputation
64 Decision Trees NaN 0.0 Mean Imputation
65 Decision Trees NaN None Mean Imputation
66 Decision Trees NaN gini Mean Imputation
67 Decision Trees NaN None Mean Imputation
68 Decision Trees NaN None Mean Imputation
69 Decision Trees NaN None Mean Imputation
70 Decision Trees NaN 0.0 Mean Imputation
71 Decision Trees NaN 1 Mean Imputation
72 Decision Trees NaN 2 Mean Imputation
73 Decision Trees NaN 0.0 Mean Imputation
74 Decision Trees NaN None Mean Imputation
75 Decision Trees NaN best Mean Imputation
76 Decision Trees 0.858799 NaN Mean Imputation
77 Decision Trees 0.109756 NaN Mean Imputation
78 Decision Trees 0.208333 NaN Mean Imputation
79 Decision Trees 0.143770 NaN Mean Imputation
80 Decision Trees 0.554149 NaN Mean Imputation
81 Decision Trees 0.112166 NaN Mean Imputation
82 Decision Trees 0.108298 NaN Mean Imputation
83 Decision Trees NaN 0.0 Mean Imputation
84 Decision Trees NaN None Mean Imputation
85 Decision Trees NaN gini Mean Imputation
86 Decision Trees NaN None Mean Imputation
87 Decision Trees NaN None Mean Imputation
88 Decision Trees NaN None Mean Imputation
89 Decision Trees NaN 0.0 Mean Imputation
90 Decision Trees NaN 1 Mean Imputation
91 Decision Trees NaN 2 Mean Imputation
92 Decision Trees NaN 0.0 Mean Imputation
93 Decision Trees NaN None Mean Imputation
94 Decision Trees NaN best Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
1 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
2 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
3 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
4 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
5 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
6 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
7 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
8 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
9 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
10 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
11 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
12 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
13 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
14 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
15 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
16 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
17 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
18 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
19 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
20 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
21 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
22 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
23 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
24 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
25 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
26 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
27 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
28 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
29 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
30 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
31 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
32 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
33 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
34 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
35 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
36 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
37 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
38 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
39 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
40 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
41 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
42 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
43 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
44 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
45 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
46 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
47 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
48 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
49 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
50 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
51 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
52 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
53 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
54 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
55 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
56 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
57 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
58 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
59 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
60 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
61 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
62 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
63 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
64 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
65 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
66 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
67 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
68 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
69 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
70 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
71 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
72 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
73 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
74 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
75 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
76 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
77 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
78 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
79 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
80 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
81 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
82 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
83 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
84 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
85 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
86 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
87 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
88 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
89 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
90 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
91 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
92 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
93 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
94 Decision Trees_Hybrid Sampling (SMOTETomek)_Me... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Decision Trees 0.857519 NaN Median Imputation
1 Decision Trees 0.100000 NaN Median Imputation
2 Decision Trees 0.160338 NaN Median Imputation
3 Decision Trees 0.123177 NaN Median Imputation
4 Decision Trees 0.540980 NaN Median Imputation
5 Decision Trees 0.083975 NaN Median Imputation
6 Decision Trees 0.081961 NaN Median Imputation
7 Decision Trees NaN 0.0 Median Imputation
8 Decision Trees NaN None Median Imputation
9 Decision Trees NaN gini Median Imputation
10 Decision Trees NaN None Median Imputation
11 Decision Trees NaN None Median Imputation
12 Decision Trees NaN None Median Imputation
13 Decision Trees NaN 0.0 Median Imputation
14 Decision Trees NaN 1 Median Imputation
15 Decision Trees NaN 2 Median Imputation
16 Decision Trees NaN 0.0 Median Imputation
17 Decision Trees NaN None Median Imputation
18 Decision Trees NaN best Median Imputation
19 Decision Trees 0.863577 NaN Median Imputation
20 Decision Trees 0.076555 NaN Median Imputation
21 Decision Trees 0.195122 NaN Median Imputation
22 Decision Trees 0.109966 NaN Median Imputation
23 Decision Trees 0.574620 NaN Median Imputation
24 Decision Trees 0.158825 NaN Median Imputation
25 Decision Trees 0.149240 NaN Median Imputation
26 Decision Trees NaN 0.0 Median Imputation
27 Decision Trees NaN None Median Imputation
28 Decision Trees NaN gini Median Imputation
29 Decision Trees NaN None Median Imputation
30 Decision Trees NaN None Median Imputation
31 Decision Trees NaN None Median Imputation
32 Decision Trees NaN 0.0 Median Imputation
33 Decision Trees NaN 1 Median Imputation
34 Decision Trees NaN 2 Median Imputation
35 Decision Trees NaN 0.0 Median Imputation
36 Decision Trees NaN None Median Imputation
37 Decision Trees NaN best Median Imputation
38 Decision Trees 0.862260 NaN Median Imputation
39 Decision Trees 0.098086 NaN Median Imputation
40 Decision Trees 0.219251 NaN Median Imputation
41 Decision Trees 0.135537 NaN Median Imputation
42 Decision Trees 0.566766 NaN Median Imputation
43 Decision Trees 0.144581 NaN Median Imputation
44 Decision Trees 0.133531 NaN Median Imputation
45 Decision Trees NaN 0.0 Median Imputation
46 Decision Trees NaN None Median Imputation
47 Decision Trees NaN gini Median Imputation
48 Decision Trees NaN None Median Imputation
49 Decision Trees NaN None Median Imputation
50 Decision Trees NaN None Median Imputation
51 Decision Trees NaN 0.0 Median Imputation
52 Decision Trees NaN 1 Median Imputation
53 Decision Trees NaN 2 Median Imputation
54 Decision Trees NaN 0.0 Median Imputation
55 Decision Trees NaN None Median Imputation
56 Decision Trees NaN best Median Imputation
57 Decision Trees 0.858008 NaN Median Imputation
58 Decision Trees 0.082725 NaN Median Imputation
59 Decision Trees 0.173469 NaN Median Imputation
60 Decision Trees 0.112026 NaN Median Imputation
61 Decision Trees 0.531231 NaN Median Imputation
62 Decision Trees 0.069507 NaN Median Imputation
63 Decision Trees 0.062462 NaN Median Imputation
64 Decision Trees NaN 0.0 Median Imputation
65 Decision Trees NaN None Median Imputation
66 Decision Trees NaN gini Median Imputation
67 Decision Trees NaN None Median Imputation
68 Decision Trees NaN None Median Imputation
69 Decision Trees NaN None Median Imputation
70 Decision Trees NaN 0.0 Median Imputation
71 Decision Trees NaN 1 Median Imputation
72 Decision Trees NaN 2 Median Imputation
73 Decision Trees NaN 0.0 Median Imputation
74 Decision Trees NaN None Median Imputation
75 Decision Trees NaN best Median Imputation
76 Decision Trees 0.854847 NaN Median Imputation
77 Decision Trees 0.107728 NaN Median Imputation
78 Decision Trees 0.212963 NaN Median Imputation
79 Decision Trees 0.143079 NaN Median Imputation
80 Decision Trees 0.562375 NaN Median Imputation
81 Decision Trees 0.128890 NaN Median Imputation
82 Decision Trees 0.124749 NaN Median Imputation
83 Decision Trees NaN 0.0 Median Imputation
84 Decision Trees NaN None Median Imputation
85 Decision Trees NaN gini Median Imputation
86 Decision Trees NaN None Median Imputation
87 Decision Trees NaN None Median Imputation
88 Decision Trees NaN None Median Imputation
89 Decision Trees NaN 0.0 Median Imputation
90 Decision Trees NaN 1 Median Imputation
91 Decision Trees NaN 2 Median Imputation
92 Decision Trees NaN 0.0 Median Imputation
93 Decision Trees NaN None Median Imputation
94 Decision Trees NaN best Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
1 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
2 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
3 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
4 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
5 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
6 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
7 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
8 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
9 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
10 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
11 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
12 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
13 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
14 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
15 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
16 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
17 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
18 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
19 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
20 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
21 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
22 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
23 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
24 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
25 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
26 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
27 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
28 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
29 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
30 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
31 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
32 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
33 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
34 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
35 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
36 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
37 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
38 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
39 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
40 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
41 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
42 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
43 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
44 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
45 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
46 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
47 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
48 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
49 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
50 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
51 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
52 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
53 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
54 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
55 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
56 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
57 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
58 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
59 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
60 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
61 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
62 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
63 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
64 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
65 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
66 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
67 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
68 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
69 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
70 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
71 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
72 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
73 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
74 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
75 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
76 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
77 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
78 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
79 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
80 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
81 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
82 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
83 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
84 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
85 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
86 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
87 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
88 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
89 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
90 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
91 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
92 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
93 Decision Trees_Hybrid Sampling (SMOTETomek)_Me...
94 Decision Trees_Hybrid Sampling (SMOTETomek)_Me... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Random Forest 0.903345 NaN Zero Imputation
1 Random Forest 0.134831 NaN Zero Imputation
2 Random Forest 0.101266 NaN Zero Imputation
3 Random Forest 0.115663 NaN Zero Imputation
4 Random Forest 0.659104 NaN Zero Imputation
5 Random Forest 0.284878 NaN Zero Imputation
6 Random Forest 0.318209 NaN Zero Imputation
7 Random Forest NaN True Zero Imputation
8 Random Forest NaN 0.0 Zero Imputation
9 Random Forest NaN None Zero Imputation
10 Random Forest NaN gini Zero Imputation
11 Random Forest NaN None Zero Imputation
12 Random Forest NaN sqrt Zero Imputation
13 Random Forest NaN None Zero Imputation
14 Random Forest NaN None Zero Imputation
15 Random Forest NaN 0.0 Zero Imputation
16 Random Forest NaN 1 Zero Imputation
17 Random Forest NaN 2 Zero Imputation
18 Random Forest NaN 0.0 Zero Imputation
19 Random Forest NaN 100 Zero Imputation
20 Random Forest NaN None Zero Imputation
21 Random Forest NaN False Zero Imputation
22 Random Forest NaN None Zero Imputation
23 Random Forest NaN 0 Zero Imputation
24 Random Forest NaN False Zero Imputation
25 Random Forest 0.921254 NaN Zero Imputation
26 Random Forest 0.142857 NaN Zero Imputation
27 Random Forest 0.164634 NaN Zero Imputation
28 Random Forest 0.152975 NaN Zero Imputation
29 Random Forest 0.690993 NaN Zero Imputation
30 Random Forest 0.343207 NaN Zero Imputation
31 Random Forest 0.381986 NaN Zero Imputation
32 Random Forest NaN True Zero Imputation
33 Random Forest NaN 0.0 Zero Imputation
34 Random Forest NaN None Zero Imputation
35 Random Forest NaN gini Zero Imputation
36 Random Forest NaN None Zero Imputation
37 Random Forest NaN sqrt Zero Imputation
38 Random Forest NaN None Zero Imputation
39 Random Forest NaN None Zero Imputation
40 Random Forest NaN 0.0 Zero Imputation
41 Random Forest NaN 1 Zero Imputation
42 Random Forest NaN 2 Zero Imputation
43 Random Forest NaN 0.0 Zero Imputation
44 Random Forest NaN 100 Zero Imputation
45 Random Forest NaN None Zero Imputation
46 Random Forest NaN False Zero Imputation
47 Random Forest NaN None Zero Imputation
48 Random Forest NaN 0 Zero Imputation
49 Random Forest NaN False Zero Imputation
50 Random Forest 0.917830 NaN Zero Imputation
51 Random Forest 0.154696 NaN Zero Imputation
52 Random Forest 0.149733 NaN Zero Imputation
53 Random Forest 0.152174 NaN Zero Imputation
54 Random Forest 0.680292 NaN Zero Imputation
55 Random Forest 0.289813 NaN Zero Imputation
56 Random Forest 0.360585 NaN Zero Imputation
57 Random Forest NaN True Zero Imputation
58 Random Forest NaN 0.0 Zero Imputation
59 Random Forest NaN None Zero Imputation
60 Random Forest NaN gini Zero Imputation
61 Random Forest NaN None Zero Imputation
62 Random Forest NaN sqrt Zero Imputation
63 Random Forest NaN None Zero Imputation
64 Random Forest NaN None Zero Imputation
65 Random Forest NaN 0.0 Zero Imputation
66 Random Forest NaN 1 Zero Imputation
67 Random Forest NaN 2 Zero Imputation
68 Random Forest NaN 0.0 Zero Imputation
69 Random Forest NaN 100 Zero Imputation
70 Random Forest NaN None Zero Imputation
71 Random Forest NaN False Zero Imputation
72 Random Forest NaN None Zero Imputation
73 Random Forest NaN 0 Zero Imputation
74 Random Forest NaN False Zero Imputation
75 Random Forest 0.911486 NaN Zero Imputation
76 Random Forest 0.119565 NaN Zero Imputation
77 Random Forest 0.112245 NaN Zero Imputation
78 Random Forest 0.115789 NaN Zero Imputation
79 Random Forest 0.619050 NaN Zero Imputation
80 Random Forest 0.215935 NaN Zero Imputation
81 Random Forest 0.238101 NaN Zero Imputation
82 Random Forest NaN True Zero Imputation
83 Random Forest NaN 0.0 Zero Imputation
84 Random Forest NaN None Zero Imputation
85 Random Forest NaN gini Zero Imputation
86 Random Forest NaN None Zero Imputation
87 Random Forest NaN sqrt Zero Imputation
88 Random Forest NaN None Zero Imputation
89 Random Forest NaN None Zero Imputation
90 Random Forest NaN 0.0 Zero Imputation
91 Random Forest NaN 1 Zero Imputation
92 Random Forest NaN 2 Zero Imputation
93 Random Forest NaN 0.0 Zero Imputation
94 Random Forest NaN 100 Zero Imputation
95 Random Forest NaN None Zero Imputation
96 Random Forest NaN False Zero Imputation
97 Random Forest NaN None Zero Imputation
98 Random Forest NaN 0 Zero Imputation
99 Random Forest NaN False Zero Imputation
100 Random Forest 0.911222 NaN Zero Imputation
101 Random Forest 0.162011 NaN Zero Imputation
102 Random Forest 0.134259 NaN Zero Imputation
103 Random Forest 0.146835 NaN Zero Imputation
104 Random Forest 0.628811 NaN Zero Imputation
105 Random Forest 0.224866 NaN Zero Imputation
106 Random Forest 0.257622 NaN Zero Imputation
107 Random Forest NaN True Zero Imputation
108 Random Forest NaN 0.0 Zero Imputation
109 Random Forest NaN None Zero Imputation
110 Random Forest NaN gini Zero Imputation
111 Random Forest NaN None Zero Imputation
112 Random Forest NaN sqrt Zero Imputation
113 Random Forest NaN None Zero Imputation
114 Random Forest NaN None Zero Imputation
115 Random Forest NaN 0.0 Zero Imputation
116 Random Forest NaN 1 Zero Imputation
117 Random Forest NaN 2 Zero Imputation
118 Random Forest NaN 0.0 Zero Imputation
119 Random Forest NaN 100 Zero Imputation
120 Random Forest NaN None Zero Imputation
121 Random Forest NaN False Zero Imputation
122 Random Forest NaN None Zero Imputation
123 Random Forest NaN 0 Zero Imputation
124 Random Forest NaN False Zero Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
45 Oversampling
46 Oversampling
47 Oversampling
48 Oversampling
49 Oversampling
50 Oversampling
51 Oversampling
52 Oversampling
53 Oversampling
54 Oversampling
55 Oversampling
56 Oversampling
57 Oversampling
58 Oversampling
59 Oversampling
60 Oversampling
61 Oversampling
62 Oversampling
63 Oversampling
64 Oversampling
65 Oversampling
66 Oversampling
67 Oversampling
68 Oversampling
69 Oversampling
70 Oversampling
71 Oversampling
72 Oversampling
73 Oversampling
74 Oversampling
75 Oversampling
76 Oversampling
77 Oversampling
78 Oversampling
79 Oversampling
80 Oversampling
81 Oversampling
82 Oversampling
83 Oversampling
84 Oversampling
85 Oversampling
86 Oversampling
87 Oversampling
88 Oversampling
89 Oversampling
90 Oversampling
91 Oversampling
92 Oversampling
93 Oversampling
94 Oversampling
95 Oversampling
96 Oversampling
97 Oversampling
98 Oversampling
99 Oversampling
100 Oversampling
101 Oversampling
102 Oversampling
103 Oversampling
104 Oversampling
105 Oversampling
106 Oversampling
107 Oversampling
108 Oversampling
109 Oversampling
110 Oversampling
111 Oversampling
112 Oversampling
113 Oversampling
114 Oversampling
115 Oversampling
116 Oversampling
117 Oversampling
118 Oversampling
119 Oversampling
120 Oversampling
121 Oversampling
122 Oversampling
123 Oversampling
124 Oversampling
Model Unique Code
0 Random Forest_Oversampling_Zero Imputation_fold_0
1 Random Forest_Oversampling_Zero Imputation_fold_0
2 Random Forest_Oversampling_Zero Imputation_fold_0
3 Random Forest_Oversampling_Zero Imputation_fold_0
4 Random Forest_Oversampling_Zero Imputation_fold_0
5 Random Forest_Oversampling_Zero Imputation_fold_0
6 Random Forest_Oversampling_Zero Imputation_fold_0
7 Random Forest_Oversampling_Zero Imputation_fold_0
8 Random Forest_Oversampling_Zero Imputation_fold_0
9 Random Forest_Oversampling_Zero Imputation_fold_0
10 Random Forest_Oversampling_Zero Imputation_fold_0
11 Random Forest_Oversampling_Zero Imputation_fold_0
12 Random Forest_Oversampling_Zero Imputation_fold_0
13 Random Forest_Oversampling_Zero Imputation_fold_0
14 Random Forest_Oversampling_Zero Imputation_fold_0
15 Random Forest_Oversampling_Zero Imputation_fold_0
16 Random Forest_Oversampling_Zero Imputation_fold_0
17 Random Forest_Oversampling_Zero Imputation_fold_0
18 Random Forest_Oversampling_Zero Imputation_fold_0
19 Random Forest_Oversampling_Zero Imputation_fold_0
20 Random Forest_Oversampling_Zero Imputation_fold_0
21 Random Forest_Oversampling_Zero Imputation_fold_0
22 Random Forest_Oversampling_Zero Imputation_fold_0
23 Random Forest_Oversampling_Zero Imputation_fold_0
24 Random Forest_Oversampling_Zero Imputation_fold_0
25 Random Forest_Oversampling_Zero Imputation_fold_1
26 Random Forest_Oversampling_Zero Imputation_fold_1
27 Random Forest_Oversampling_Zero Imputation_fold_1
28 Random Forest_Oversampling_Zero Imputation_fold_1
29 Random Forest_Oversampling_Zero Imputation_fold_1
30 Random Forest_Oversampling_Zero Imputation_fold_1
31 Random Forest_Oversampling_Zero Imputation_fold_1
32 Random Forest_Oversampling_Zero Imputation_fold_1
33 Random Forest_Oversampling_Zero Imputation_fold_1
34 Random Forest_Oversampling_Zero Imputation_fold_1
35 Random Forest_Oversampling_Zero Imputation_fold_1
36 Random Forest_Oversampling_Zero Imputation_fold_1
37 Random Forest_Oversampling_Zero Imputation_fold_1
38 Random Forest_Oversampling_Zero Imputation_fold_1
39 Random Forest_Oversampling_Zero Imputation_fold_1
40 Random Forest_Oversampling_Zero Imputation_fold_1
41 Random Forest_Oversampling_Zero Imputation_fold_1
42 Random Forest_Oversampling_Zero Imputation_fold_1
43 Random Forest_Oversampling_Zero Imputation_fold_1
44 Random Forest_Oversampling_Zero Imputation_fold_1
45 Random Forest_Oversampling_Zero Imputation_fold_1
46 Random Forest_Oversampling_Zero Imputation_fold_1
47 Random Forest_Oversampling_Zero Imputation_fold_1
48 Random Forest_Oversampling_Zero Imputation_fold_1
49 Random Forest_Oversampling_Zero Imputation_fold_1
50 Random Forest_Oversampling_Zero Imputation_fold_2
51 Random Forest_Oversampling_Zero Imputation_fold_2
52 Random Forest_Oversampling_Zero Imputation_fold_2
53 Random Forest_Oversampling_Zero Imputation_fold_2
54 Random Forest_Oversampling_Zero Imputation_fold_2
55 Random Forest_Oversampling_Zero Imputation_fold_2
56 Random Forest_Oversampling_Zero Imputation_fold_2
57 Random Forest_Oversampling_Zero Imputation_fold_2
58 Random Forest_Oversampling_Zero Imputation_fold_2
59 Random Forest_Oversampling_Zero Imputation_fold_2
60 Random Forest_Oversampling_Zero Imputation_fold_2
61 Random Forest_Oversampling_Zero Imputation_fold_2
62 Random Forest_Oversampling_Zero Imputation_fold_2
63 Random Forest_Oversampling_Zero Imputation_fold_2
64 Random Forest_Oversampling_Zero Imputation_fold_2
65 Random Forest_Oversampling_Zero Imputation_fold_2
66 Random Forest_Oversampling_Zero Imputation_fold_2
67 Random Forest_Oversampling_Zero Imputation_fold_2
68 Random Forest_Oversampling_Zero Imputation_fold_2
69 Random Forest_Oversampling_Zero Imputation_fold_2
70 Random Forest_Oversampling_Zero Imputation_fold_2
71 Random Forest_Oversampling_Zero Imputation_fold_2
72 Random Forest_Oversampling_Zero Imputation_fold_2
73 Random Forest_Oversampling_Zero Imputation_fold_2
74 Random Forest_Oversampling_Zero Imputation_fold_2
75 Random Forest_Oversampling_Zero Imputation_fold_3
76 Random Forest_Oversampling_Zero Imputation_fold_3
77 Random Forest_Oversampling_Zero Imputation_fold_3
78 Random Forest_Oversampling_Zero Imputation_fold_3
79 Random Forest_Oversampling_Zero Imputation_fold_3
80 Random Forest_Oversampling_Zero Imputation_fold_3
81 Random Forest_Oversampling_Zero Imputation_fold_3
82 Random Forest_Oversampling_Zero Imputation_fold_3
83 Random Forest_Oversampling_Zero Imputation_fold_3
84 Random Forest_Oversampling_Zero Imputation_fold_3
85 Random Forest_Oversampling_Zero Imputation_fold_3
86 Random Forest_Oversampling_Zero Imputation_fold_3
87 Random Forest_Oversampling_Zero Imputation_fold_3
88 Random Forest_Oversampling_Zero Imputation_fold_3
89 Random Forest_Oversampling_Zero Imputation_fold_3
90 Random Forest_Oversampling_Zero Imputation_fold_3
91 Random Forest_Oversampling_Zero Imputation_fold_3
92 Random Forest_Oversampling_Zero Imputation_fold_3
93 Random Forest_Oversampling_Zero Imputation_fold_3
94 Random Forest_Oversampling_Zero Imputation_fold_3
95 Random Forest_Oversampling_Zero Imputation_fold_3
96 Random Forest_Oversampling_Zero Imputation_fold_3
97 Random Forest_Oversampling_Zero Imputation_fold_3
98 Random Forest_Oversampling_Zero Imputation_fold_3
99 Random Forest_Oversampling_Zero Imputation_fold_3
100 Random Forest_Oversampling_Zero Imputation_fold_4
101 Random Forest_Oversampling_Zero Imputation_fold_4
102 Random Forest_Oversampling_Zero Imputation_fold_4
103 Random Forest_Oversampling_Zero Imputation_fold_4
104 Random Forest_Oversampling_Zero Imputation_fold_4
105 Random Forest_Oversampling_Zero Imputation_fold_4
106 Random Forest_Oversampling_Zero Imputation_fold_4
107 Random Forest_Oversampling_Zero Imputation_fold_4
108 Random Forest_Oversampling_Zero Imputation_fold_4
109 Random Forest_Oversampling_Zero Imputation_fold_4
110 Random Forest_Oversampling_Zero Imputation_fold_4
111 Random Forest_Oversampling_Zero Imputation_fold_4
112 Random Forest_Oversampling_Zero Imputation_fold_4
113 Random Forest_Oversampling_Zero Imputation_fold_4
114 Random Forest_Oversampling_Zero Imputation_fold_4
115 Random Forest_Oversampling_Zero Imputation_fold_4
116 Random Forest_Oversampling_Zero Imputation_fold_4
117 Random Forest_Oversampling_Zero Imputation_fold_4
118 Random Forest_Oversampling_Zero Imputation_fold_4
119 Random Forest_Oversampling_Zero Imputation_fold_4
120 Random Forest_Oversampling_Zero Imputation_fold_4
121 Random Forest_Oversampling_Zero Imputation_fold_4
122 Random Forest_Oversampling_Zero Imputation_fold_4
123 Random Forest_Oversampling_Zero Imputation_fold_4
124 Random Forest_Oversampling_Zero Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Random Forest 0.900448 NaN Mean Imputation
1 Random Forest 0.126984 NaN Mean Imputation
2 Random Forest 0.101266 NaN Mean Imputation
3 Random Forest 0.112676 NaN Mean Imputation
4 Random Forest 0.649686 NaN Mean Imputation
5 Random Forest 0.265185 NaN Mean Imputation
6 Random Forest 0.299372 NaN Mean Imputation
7 Random Forest NaN True Mean Imputation
8 Random Forest NaN 0.0 Mean Imputation
9 Random Forest NaN None Mean Imputation
10 Random Forest NaN gini Mean Imputation
11 Random Forest NaN None Mean Imputation
12 Random Forest NaN sqrt Mean Imputation
13 Random Forest NaN None Mean Imputation
14 Random Forest NaN None Mean Imputation
15 Random Forest NaN 0.0 Mean Imputation
16 Random Forest NaN 1 Mean Imputation
17 Random Forest NaN 2 Mean Imputation
18 Random Forest NaN 0.0 Mean Imputation
19 Random Forest NaN 100 Mean Imputation
20 Random Forest NaN None Mean Imputation
21 Random Forest NaN False Mean Imputation
22 Random Forest NaN None Mean Imputation
23 Random Forest NaN 0 Mean Imputation
24 Random Forest NaN False Mean Imputation
25 Random Forest 0.921254 NaN Mean Imputation
26 Random Forest 0.146597 NaN Mean Imputation
27 Random Forest 0.170732 NaN Mean Imputation
28 Random Forest 0.157746 NaN Mean Imputation
29 Random Forest 0.666996 NaN Mean Imputation
30 Random Forest 0.299796 NaN Mean Imputation
31 Random Forest 0.333991 NaN Mean Imputation
32 Random Forest NaN True Mean Imputation
33 Random Forest NaN 0.0 Mean Imputation
34 Random Forest NaN None Mean Imputation
35 Random Forest NaN gini Mean Imputation
36 Random Forest NaN None Mean Imputation
37 Random Forest NaN sqrt Mean Imputation
38 Random Forest NaN None Mean Imputation
39 Random Forest NaN None Mean Imputation
40 Random Forest NaN 0.0 Mean Imputation
41 Random Forest NaN 1 Mean Imputation
42 Random Forest NaN 2 Mean Imputation
43 Random Forest NaN 0.0 Mean Imputation
44 Random Forest NaN 100 Mean Imputation
45 Random Forest NaN None Mean Imputation
46 Random Forest NaN False Mean Imputation
47 Random Forest NaN None Mean Imputation
48 Random Forest NaN 0 Mean Imputation
49 Random Forest NaN False Mean Imputation
50 Random Forest 0.917830 NaN Mean Imputation
51 Random Forest 0.162162 NaN Mean Imputation
52 Random Forest 0.160428 NaN Mean Imputation
53 Random Forest 0.161290 NaN Mean Imputation
54 Random Forest 0.677444 NaN Mean Imputation
55 Random Forest 0.291525 NaN Mean Imputation
56 Random Forest 0.354888 NaN Mean Imputation
57 Random Forest NaN True Mean Imputation
58 Random Forest NaN 0.0 Mean Imputation
59 Random Forest NaN None Mean Imputation
60 Random Forest NaN gini Mean Imputation
61 Random Forest NaN None Mean Imputation
62 Random Forest NaN sqrt Mean Imputation
63 Random Forest NaN None Mean Imputation
64 Random Forest NaN None Mean Imputation
65 Random Forest NaN 0.0 Mean Imputation
66 Random Forest NaN 1 Mean Imputation
67 Random Forest NaN 2 Mean Imputation
68 Random Forest NaN 0.0 Mean Imputation
69 Random Forest NaN 100 Mean Imputation
70 Random Forest NaN None Mean Imputation
71 Random Forest NaN False Mean Imputation
72 Random Forest NaN None Mean Imputation
73 Random Forest NaN 0 Mean Imputation
74 Random Forest NaN False Mean Imputation
75 Random Forest 0.913066 NaN Mean Imputation
76 Random Forest 0.127778 NaN Mean Imputation
77 Random Forest 0.117347 NaN Mean Imputation
78 Random Forest 0.122340 NaN Mean Imputation
79 Random Forest 0.608358 NaN Mean Imputation
80 Random Forest 0.199410 NaN Mean Imputation
81 Random Forest 0.216716 NaN Mean Imputation
82 Random Forest NaN True Mean Imputation
83 Random Forest NaN 0.0 Mean Imputation
84 Random Forest NaN None Mean Imputation
85 Random Forest NaN gini Mean Imputation
86 Random Forest NaN None Mean Imputation
87 Random Forest NaN sqrt Mean Imputation
88 Random Forest NaN None Mean Imputation
89 Random Forest NaN None Mean Imputation
90 Random Forest NaN 0.0 Mean Imputation
91 Random Forest NaN 1 Mean Imputation
92 Random Forest NaN 2 Mean Imputation
93 Random Forest NaN 0.0 Mean Imputation
94 Random Forest NaN 100 Mean Imputation
95 Random Forest NaN None Mean Imputation
96 Random Forest NaN False Mean Imputation
97 Random Forest NaN None Mean Imputation
98 Random Forest NaN 0 Mean Imputation
99 Random Forest NaN False Mean Imputation
100 Random Forest 0.909115 NaN Mean Imputation
101 Random Forest 0.151351 NaN Mean Imputation
102 Random Forest 0.129630 NaN Mean Imputation
103 Random Forest 0.139651 NaN Mean Imputation
104 Random Forest 0.623442 NaN Mean Imputation
105 Random Forest 0.258623 NaN Mean Imputation
106 Random Forest 0.246883 NaN Mean Imputation
107 Random Forest NaN True Mean Imputation
108 Random Forest NaN 0.0 Mean Imputation
109 Random Forest NaN None Mean Imputation
110 Random Forest NaN gini Mean Imputation
111 Random Forest NaN None Mean Imputation
112 Random Forest NaN sqrt Mean Imputation
113 Random Forest NaN None Mean Imputation
114 Random Forest NaN None Mean Imputation
115 Random Forest NaN 0.0 Mean Imputation
116 Random Forest NaN 1 Mean Imputation
117 Random Forest NaN 2 Mean Imputation
118 Random Forest NaN 0.0 Mean Imputation
119 Random Forest NaN 100 Mean Imputation
120 Random Forest NaN None Mean Imputation
121 Random Forest NaN False Mean Imputation
122 Random Forest NaN None Mean Imputation
123 Random Forest NaN 0 Mean Imputation
124 Random Forest NaN False Mean Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
45 Oversampling
46 Oversampling
47 Oversampling
48 Oversampling
49 Oversampling
50 Oversampling
51 Oversampling
52 Oversampling
53 Oversampling
54 Oversampling
55 Oversampling
56 Oversampling
57 Oversampling
58 Oversampling
59 Oversampling
60 Oversampling
61 Oversampling
62 Oversampling
63 Oversampling
64 Oversampling
65 Oversampling
66 Oversampling
67 Oversampling
68 Oversampling
69 Oversampling
70 Oversampling
71 Oversampling
72 Oversampling
73 Oversampling
74 Oversampling
75 Oversampling
76 Oversampling
77 Oversampling
78 Oversampling
79 Oversampling
80 Oversampling
81 Oversampling
82 Oversampling
83 Oversampling
84 Oversampling
85 Oversampling
86 Oversampling
87 Oversampling
88 Oversampling
89 Oversampling
90 Oversampling
91 Oversampling
92 Oversampling
93 Oversampling
94 Oversampling
95 Oversampling
96 Oversampling
97 Oversampling
98 Oversampling
99 Oversampling
100 Oversampling
101 Oversampling
102 Oversampling
103 Oversampling
104 Oversampling
105 Oversampling
106 Oversampling
107 Oversampling
108 Oversampling
109 Oversampling
110 Oversampling
111 Oversampling
112 Oversampling
113 Oversampling
114 Oversampling
115 Oversampling
116 Oversampling
117 Oversampling
118 Oversampling
119 Oversampling
120 Oversampling
121 Oversampling
122 Oversampling
123 Oversampling
124 Oversampling
Model Unique Code
0 Random Forest_Oversampling_Mean Imputation_fold_0
1 Random Forest_Oversampling_Mean Imputation_fold_0
2 Random Forest_Oversampling_Mean Imputation_fold_0
3 Random Forest_Oversampling_Mean Imputation_fold_0
4 Random Forest_Oversampling_Mean Imputation_fold_0
5 Random Forest_Oversampling_Mean Imputation_fold_0
6 Random Forest_Oversampling_Mean Imputation_fold_0
7 Random Forest_Oversampling_Mean Imputation_fold_0
8 Random Forest_Oversampling_Mean Imputation_fold_0
9 Random Forest_Oversampling_Mean Imputation_fold_0
10 Random Forest_Oversampling_Mean Imputation_fold_0
11 Random Forest_Oversampling_Mean Imputation_fold_0
12 Random Forest_Oversampling_Mean Imputation_fold_0
13 Random Forest_Oversampling_Mean Imputation_fold_0
14 Random Forest_Oversampling_Mean Imputation_fold_0
15 Random Forest_Oversampling_Mean Imputation_fold_0
16 Random Forest_Oversampling_Mean Imputation_fold_0
17 Random Forest_Oversampling_Mean Imputation_fold_0
18 Random Forest_Oversampling_Mean Imputation_fold_0
19 Random Forest_Oversampling_Mean Imputation_fold_0
20 Random Forest_Oversampling_Mean Imputation_fold_0
21 Random Forest_Oversampling_Mean Imputation_fold_0
22 Random Forest_Oversampling_Mean Imputation_fold_0
23 Random Forest_Oversampling_Mean Imputation_fold_0
24 Random Forest_Oversampling_Mean Imputation_fold_0
25 Random Forest_Oversampling_Mean Imputation_fold_1
26 Random Forest_Oversampling_Mean Imputation_fold_1
27 Random Forest_Oversampling_Mean Imputation_fold_1
28 Random Forest_Oversampling_Mean Imputation_fold_1
29 Random Forest_Oversampling_Mean Imputation_fold_1
30 Random Forest_Oversampling_Mean Imputation_fold_1
31 Random Forest_Oversampling_Mean Imputation_fold_1
32 Random Forest_Oversampling_Mean Imputation_fold_1
33 Random Forest_Oversampling_Mean Imputation_fold_1
34 Random Forest_Oversampling_Mean Imputation_fold_1
35 Random Forest_Oversampling_Mean Imputation_fold_1
36 Random Forest_Oversampling_Mean Imputation_fold_1
37 Random Forest_Oversampling_Mean Imputation_fold_1
38 Random Forest_Oversampling_Mean Imputation_fold_1
39 Random Forest_Oversampling_Mean Imputation_fold_1
40 Random Forest_Oversampling_Mean Imputation_fold_1
41 Random Forest_Oversampling_Mean Imputation_fold_1
42 Random Forest_Oversampling_Mean Imputation_fold_1
43 Random Forest_Oversampling_Mean Imputation_fold_1
44 Random Forest_Oversampling_Mean Imputation_fold_1
45 Random Forest_Oversampling_Mean Imputation_fold_1
46 Random Forest_Oversampling_Mean Imputation_fold_1
47 Random Forest_Oversampling_Mean Imputation_fold_1
48 Random Forest_Oversampling_Mean Imputation_fold_1
49 Random Forest_Oversampling_Mean Imputation_fold_1
50 Random Forest_Oversampling_Mean Imputation_fold_2
51 Random Forest_Oversampling_Mean Imputation_fold_2
52 Random Forest_Oversampling_Mean Imputation_fold_2
53 Random Forest_Oversampling_Mean Imputation_fold_2
54 Random Forest_Oversampling_Mean Imputation_fold_2
55 Random Forest_Oversampling_Mean Imputation_fold_2
56 Random Forest_Oversampling_Mean Imputation_fold_2
57 Random Forest_Oversampling_Mean Imputation_fold_2
58 Random Forest_Oversampling_Mean Imputation_fold_2
59 Random Forest_Oversampling_Mean Imputation_fold_2
60 Random Forest_Oversampling_Mean Imputation_fold_2
61 Random Forest_Oversampling_Mean Imputation_fold_2
62 Random Forest_Oversampling_Mean Imputation_fold_2
63 Random Forest_Oversampling_Mean Imputation_fold_2
64 Random Forest_Oversampling_Mean Imputation_fold_2
65 Random Forest_Oversampling_Mean Imputation_fold_2
66 Random Forest_Oversampling_Mean Imputation_fold_2
67 Random Forest_Oversampling_Mean Imputation_fold_2
68 Random Forest_Oversampling_Mean Imputation_fold_2
69 Random Forest_Oversampling_Mean Imputation_fold_2
70 Random Forest_Oversampling_Mean Imputation_fold_2
71 Random Forest_Oversampling_Mean Imputation_fold_2
72 Random Forest_Oversampling_Mean Imputation_fold_2
73 Random Forest_Oversampling_Mean Imputation_fold_2
74 Random Forest_Oversampling_Mean Imputation_fold_2
75 Random Forest_Oversampling_Mean Imputation_fold_3
76 Random Forest_Oversampling_Mean Imputation_fold_3
77 Random Forest_Oversampling_Mean Imputation_fold_3
78 Random Forest_Oversampling_Mean Imputation_fold_3
79 Random Forest_Oversampling_Mean Imputation_fold_3
80 Random Forest_Oversampling_Mean Imputation_fold_3
81 Random Forest_Oversampling_Mean Imputation_fold_3
82 Random Forest_Oversampling_Mean Imputation_fold_3
83 Random Forest_Oversampling_Mean Imputation_fold_3
84 Random Forest_Oversampling_Mean Imputation_fold_3
85 Random Forest_Oversampling_Mean Imputation_fold_3
86 Random Forest_Oversampling_Mean Imputation_fold_3
87 Random Forest_Oversampling_Mean Imputation_fold_3
88 Random Forest_Oversampling_Mean Imputation_fold_3
89 Random Forest_Oversampling_Mean Imputation_fold_3
90 Random Forest_Oversampling_Mean Imputation_fold_3
91 Random Forest_Oversampling_Mean Imputation_fold_3
92 Random Forest_Oversampling_Mean Imputation_fold_3
93 Random Forest_Oversampling_Mean Imputation_fold_3
94 Random Forest_Oversampling_Mean Imputation_fold_3
95 Random Forest_Oversampling_Mean Imputation_fold_3
96 Random Forest_Oversampling_Mean Imputation_fold_3
97 Random Forest_Oversampling_Mean Imputation_fold_3
98 Random Forest_Oversampling_Mean Imputation_fold_3
99 Random Forest_Oversampling_Mean Imputation_fold_3
100 Random Forest_Oversampling_Mean Imputation_fold_4
101 Random Forest_Oversampling_Mean Imputation_fold_4
102 Random Forest_Oversampling_Mean Imputation_fold_4
103 Random Forest_Oversampling_Mean Imputation_fold_4
104 Random Forest_Oversampling_Mean Imputation_fold_4
105 Random Forest_Oversampling_Mean Imputation_fold_4
106 Random Forest_Oversampling_Mean Imputation_fold_4
107 Random Forest_Oversampling_Mean Imputation_fold_4
108 Random Forest_Oversampling_Mean Imputation_fold_4
109 Random Forest_Oversampling_Mean Imputation_fold_4
110 Random Forest_Oversampling_Mean Imputation_fold_4
111 Random Forest_Oversampling_Mean Imputation_fold_4
112 Random Forest_Oversampling_Mean Imputation_fold_4
113 Random Forest_Oversampling_Mean Imputation_fold_4
114 Random Forest_Oversampling_Mean Imputation_fold_4
115 Random Forest_Oversampling_Mean Imputation_fold_4
116 Random Forest_Oversampling_Mean Imputation_fold_4
117 Random Forest_Oversampling_Mean Imputation_fold_4
118 Random Forest_Oversampling_Mean Imputation_fold_4
119 Random Forest_Oversampling_Mean Imputation_fold_4
120 Random Forest_Oversampling_Mean Imputation_fold_4
121 Random Forest_Oversampling_Mean Imputation_fold_4
122 Random Forest_Oversampling_Mean Imputation_fold_4
123 Random Forest_Oversampling_Mean Imputation_fold_4
124 Random Forest_Oversampling_Mean Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Random Forest 0.902291 NaN Median Imputation
1 Random Forest 0.119318 NaN Median Imputation
2 Random Forest 0.088608 NaN Median Imputation
3 Random Forest 0.101695 NaN Median Imputation
4 Random Forest 0.658272 NaN Median Imputation
5 Random Forest 0.264224 NaN Median Imputation
6 Random Forest 0.316545 NaN Median Imputation
7 Random Forest NaN True Median Imputation
8 Random Forest NaN 0.0 Median Imputation
9 Random Forest NaN None Median Imputation
10 Random Forest NaN gini Median Imputation
11 Random Forest NaN None Median Imputation
12 Random Forest NaN sqrt Median Imputation
13 Random Forest NaN None Median Imputation
14 Random Forest NaN None Median Imputation
15 Random Forest NaN 0.0 Median Imputation
16 Random Forest NaN 1 Median Imputation
17 Random Forest NaN 2 Median Imputation
18 Random Forest NaN 0.0 Median Imputation
19 Random Forest NaN 100 Median Imputation
20 Random Forest NaN None Median Imputation
21 Random Forest NaN False Median Imputation
22 Random Forest NaN None Median Imputation
23 Random Forest NaN 0 Median Imputation
24 Random Forest NaN False Median Imputation
25 Random Forest 0.919673 NaN Median Imputation
26 Random Forest 0.134715 NaN Median Imputation
27 Random Forest 0.158537 NaN Median Imputation
28 Random Forest 0.145658 NaN Median Imputation
29 Random Forest 0.689747 NaN Median Imputation
30 Random Forest 0.310340 NaN Median Imputation
31 Random Forest 0.379494 NaN Median Imputation
32 Random Forest NaN True Median Imputation
33 Random Forest NaN 0.0 Median Imputation
34 Random Forest NaN None Median Imputation
35 Random Forest NaN gini Median Imputation
36 Random Forest NaN None Median Imputation
37 Random Forest NaN sqrt Median Imputation
38 Random Forest NaN None Median Imputation
39 Random Forest NaN None Median Imputation
40 Random Forest NaN 0.0 Median Imputation
41 Random Forest NaN 1 Median Imputation
42 Random Forest NaN 2 Median Imputation
43 Random Forest NaN 0.0 Median Imputation
44 Random Forest NaN 100 Median Imputation
45 Random Forest NaN None Median Imputation
46 Random Forest NaN False Median Imputation
47 Random Forest NaN None Median Imputation
48 Random Forest NaN 0 Median Imputation
49 Random Forest NaN False Median Imputation
50 Random Forest 0.917830 NaN Median Imputation
51 Random Forest 0.154696 NaN Median Imputation
52 Random Forest 0.149733 NaN Median Imputation
53 Random Forest 0.152174 NaN Median Imputation
54 Random Forest 0.664705 NaN Median Imputation
55 Random Forest 0.255440 NaN Median Imputation
56 Random Forest 0.329410 NaN Median Imputation
57 Random Forest NaN True Median Imputation
58 Random Forest NaN 0.0 Median Imputation
59 Random Forest NaN None Median Imputation
60 Random Forest NaN gini Median Imputation
61 Random Forest NaN None Median Imputation
62 Random Forest NaN sqrt Median Imputation
63 Random Forest NaN None Median Imputation
64 Random Forest NaN None Median Imputation
65 Random Forest NaN 0.0 Median Imputation
66 Random Forest NaN 1 Median Imputation
67 Random Forest NaN 2 Median Imputation
68 Random Forest NaN 0.0 Median Imputation
69 Random Forest NaN 100 Median Imputation
70 Random Forest NaN None Median Imputation
71 Random Forest NaN False Median Imputation
72 Random Forest NaN None Median Imputation
73 Random Forest NaN 0 Median Imputation
74 Random Forest NaN False Median Imputation
75 Random Forest 0.912803 NaN Median Imputation
76 Random Forest 0.122905 NaN Median Imputation
77 Random Forest 0.112245 NaN Median Imputation
78 Random Forest 0.117333 NaN Median Imputation
79 Random Forest 0.623573 NaN Median Imputation
80 Random Forest 0.208968 NaN Median Imputation
81 Random Forest 0.247146 NaN Median Imputation
82 Random Forest NaN True Median Imputation
83 Random Forest NaN 0.0 Median Imputation
84 Random Forest NaN None Median Imputation
85 Random Forest NaN gini Median Imputation
86 Random Forest NaN None Median Imputation
87 Random Forest NaN sqrt Median Imputation
88 Random Forest NaN None Median Imputation
89 Random Forest NaN None Median Imputation
90 Random Forest NaN 0.0 Median Imputation
91 Random Forest NaN 1 Median Imputation
92 Random Forest NaN 2 Median Imputation
93 Random Forest NaN 0.0 Median Imputation
94 Random Forest NaN 100 Median Imputation
95 Random Forest NaN None Median Imputation
96 Random Forest NaN False Median Imputation
97 Random Forest NaN None Median Imputation
98 Random Forest NaN 0 Median Imputation
99 Random Forest NaN False Median Imputation
100 Random Forest 0.909905 NaN Median Imputation
101 Random Forest 0.157609 NaN Median Imputation
102 Random Forest 0.134259 NaN Median Imputation
103 Random Forest 0.145000 NaN Median Imputation
104 Random Forest 0.649560 NaN Median Imputation
105 Random Forest 0.260428 NaN Median Imputation
106 Random Forest 0.299121 NaN Median Imputation
107 Random Forest NaN True Median Imputation
108 Random Forest NaN 0.0 Median Imputation
109 Random Forest NaN None Median Imputation
110 Random Forest NaN gini Median Imputation
111 Random Forest NaN None Median Imputation
112 Random Forest NaN sqrt Median Imputation
113 Random Forest NaN None Median Imputation
114 Random Forest NaN None Median Imputation
115 Random Forest NaN 0.0 Median Imputation
116 Random Forest NaN 1 Median Imputation
117 Random Forest NaN 2 Median Imputation
118 Random Forest NaN 0.0 Median Imputation
119 Random Forest NaN 100 Median Imputation
120 Random Forest NaN None Median Imputation
121 Random Forest NaN False Median Imputation
122 Random Forest NaN None Median Imputation
123 Random Forest NaN 0 Median Imputation
124 Random Forest NaN False Median Imputation
Imbalance Class Technique \
0 Oversampling
1 Oversampling
2 Oversampling
3 Oversampling
4 Oversampling
5 Oversampling
6 Oversampling
7 Oversampling
8 Oversampling
9 Oversampling
10 Oversampling
11 Oversampling
12 Oversampling
13 Oversampling
14 Oversampling
15 Oversampling
16 Oversampling
17 Oversampling
18 Oversampling
19 Oversampling
20 Oversampling
21 Oversampling
22 Oversampling
23 Oversampling
24 Oversampling
25 Oversampling
26 Oversampling
27 Oversampling
28 Oversampling
29 Oversampling
30 Oversampling
31 Oversampling
32 Oversampling
33 Oversampling
34 Oversampling
35 Oversampling
36 Oversampling
37 Oversampling
38 Oversampling
39 Oversampling
40 Oversampling
41 Oversampling
42 Oversampling
43 Oversampling
44 Oversampling
45 Oversampling
46 Oversampling
47 Oversampling
48 Oversampling
49 Oversampling
50 Oversampling
51 Oversampling
52 Oversampling
53 Oversampling
54 Oversampling
55 Oversampling
56 Oversampling
57 Oversampling
58 Oversampling
59 Oversampling
60 Oversampling
61 Oversampling
62 Oversampling
63 Oversampling
64 Oversampling
65 Oversampling
66 Oversampling
67 Oversampling
68 Oversampling
69 Oversampling
70 Oversampling
71 Oversampling
72 Oversampling
73 Oversampling
74 Oversampling
75 Oversampling
76 Oversampling
77 Oversampling
78 Oversampling
79 Oversampling
80 Oversampling
81 Oversampling
82 Oversampling
83 Oversampling
84 Oversampling
85 Oversampling
86 Oversampling
87 Oversampling
88 Oversampling
89 Oversampling
90 Oversampling
91 Oversampling
92 Oversampling
93 Oversampling
94 Oversampling
95 Oversampling
96 Oversampling
97 Oversampling
98 Oversampling
99 Oversampling
100 Oversampling
101 Oversampling
102 Oversampling
103 Oversampling
104 Oversampling
105 Oversampling
106 Oversampling
107 Oversampling
108 Oversampling
109 Oversampling
110 Oversampling
111 Oversampling
112 Oversampling
113 Oversampling
114 Oversampling
115 Oversampling
116 Oversampling
117 Oversampling
118 Oversampling
119 Oversampling
120 Oversampling
121 Oversampling
122 Oversampling
123 Oversampling
124 Oversampling
Model Unique Code
0 Random Forest_Oversampling_Median Imputation_f...
1 Random Forest_Oversampling_Median Imputation_f...
2 Random Forest_Oversampling_Median Imputation_f...
3 Random Forest_Oversampling_Median Imputation_f...
4 Random Forest_Oversampling_Median Imputation_f...
5 Random Forest_Oversampling_Median Imputation_f...
6 Random Forest_Oversampling_Median Imputation_f...
7 Random Forest_Oversampling_Median Imputation_f...
8 Random Forest_Oversampling_Median Imputation_f...
9 Random Forest_Oversampling_Median Imputation_f...
10 Random Forest_Oversampling_Median Imputation_f...
11 Random Forest_Oversampling_Median Imputation_f...
12 Random Forest_Oversampling_Median Imputation_f...
13 Random Forest_Oversampling_Median Imputation_f...
14 Random Forest_Oversampling_Median Imputation_f...
15 Random Forest_Oversampling_Median Imputation_f...
16 Random Forest_Oversampling_Median Imputation_f...
17 Random Forest_Oversampling_Median Imputation_f...
18 Random Forest_Oversampling_Median Imputation_f...
19 Random Forest_Oversampling_Median Imputation_f...
20 Random Forest_Oversampling_Median Imputation_f...
21 Random Forest_Oversampling_Median Imputation_f...
22 Random Forest_Oversampling_Median Imputation_f...
23 Random Forest_Oversampling_Median Imputation_f...
24 Random Forest_Oversampling_Median Imputation_f...
25 Random Forest_Oversampling_Median Imputation_f...
26 Random Forest_Oversampling_Median Imputation_f...
27 Random Forest_Oversampling_Median Imputation_f...
28 Random Forest_Oversampling_Median Imputation_f...
29 Random Forest_Oversampling_Median Imputation_f...
30 Random Forest_Oversampling_Median Imputation_f...
31 Random Forest_Oversampling_Median Imputation_f...
32 Random Forest_Oversampling_Median Imputation_f...
33 Random Forest_Oversampling_Median Imputation_f...
34 Random Forest_Oversampling_Median Imputation_f...
35 Random Forest_Oversampling_Median Imputation_f...
36 Random Forest_Oversampling_Median Imputation_f...
37 Random Forest_Oversampling_Median Imputation_f...
38 Random Forest_Oversampling_Median Imputation_f...
39 Random Forest_Oversampling_Median Imputation_f...
40 Random Forest_Oversampling_Median Imputation_f...
41 Random Forest_Oversampling_Median Imputation_f...
42 Random Forest_Oversampling_Median Imputation_f...
43 Random Forest_Oversampling_Median Imputation_f...
44 Random Forest_Oversampling_Median Imputation_f...
45 Random Forest_Oversampling_Median Imputation_f...
46 Random Forest_Oversampling_Median Imputation_f...
47 Random Forest_Oversampling_Median Imputation_f...
48 Random Forest_Oversampling_Median Imputation_f...
49 Random Forest_Oversampling_Median Imputation_f...
50 Random Forest_Oversampling_Median Imputation_f...
51 Random Forest_Oversampling_Median Imputation_f...
52 Random Forest_Oversampling_Median Imputation_f...
53 Random Forest_Oversampling_Median Imputation_f...
54 Random Forest_Oversampling_Median Imputation_f...
55 Random Forest_Oversampling_Median Imputation_f...
56 Random Forest_Oversampling_Median Imputation_f...
57 Random Forest_Oversampling_Median Imputation_f...
58 Random Forest_Oversampling_Median Imputation_f...
59 Random Forest_Oversampling_Median Imputation_f...
60 Random Forest_Oversampling_Median Imputation_f...
61 Random Forest_Oversampling_Median Imputation_f...
62 Random Forest_Oversampling_Median Imputation_f...
63 Random Forest_Oversampling_Median Imputation_f...
64 Random Forest_Oversampling_Median Imputation_f...
65 Random Forest_Oversampling_Median Imputation_f...
66 Random Forest_Oversampling_Median Imputation_f...
67 Random Forest_Oversampling_Median Imputation_f...
68 Random Forest_Oversampling_Median Imputation_f...
69 Random Forest_Oversampling_Median Imputation_f...
70 Random Forest_Oversampling_Median Imputation_f...
71 Random Forest_Oversampling_Median Imputation_f...
72 Random Forest_Oversampling_Median Imputation_f...
73 Random Forest_Oversampling_Median Imputation_f...
74 Random Forest_Oversampling_Median Imputation_f...
75 Random Forest_Oversampling_Median Imputation_f...
76 Random Forest_Oversampling_Median Imputation_f...
77 Random Forest_Oversampling_Median Imputation_f...
78 Random Forest_Oversampling_Median Imputation_f...
79 Random Forest_Oversampling_Median Imputation_f...
80 Random Forest_Oversampling_Median Imputation_f...
81 Random Forest_Oversampling_Median Imputation_f...
82 Random Forest_Oversampling_Median Imputation_f...
83 Random Forest_Oversampling_Median Imputation_f...
84 Random Forest_Oversampling_Median Imputation_f...
85 Random Forest_Oversampling_Median Imputation_f...
86 Random Forest_Oversampling_Median Imputation_f...
87 Random Forest_Oversampling_Median Imputation_f...
88 Random Forest_Oversampling_Median Imputation_f...
89 Random Forest_Oversampling_Median Imputation_f...
90 Random Forest_Oversampling_Median Imputation_f...
91 Random Forest_Oversampling_Median Imputation_f...
92 Random Forest_Oversampling_Median Imputation_f...
93 Random Forest_Oversampling_Median Imputation_f...
94 Random Forest_Oversampling_Median Imputation_f...
95 Random Forest_Oversampling_Median Imputation_f...
96 Random Forest_Oversampling_Median Imputation_f...
97 Random Forest_Oversampling_Median Imputation_f...
98 Random Forest_Oversampling_Median Imputation_f...
99 Random Forest_Oversampling_Median Imputation_f...
100 Random Forest_Oversampling_Median Imputation_f...
101 Random Forest_Oversampling_Median Imputation_f...
102 Random Forest_Oversampling_Median Imputation_f...
103 Random Forest_Oversampling_Median Imputation_f...
104 Random Forest_Oversampling_Median Imputation_f...
105 Random Forest_Oversampling_Median Imputation_f...
106 Random Forest_Oversampling_Median Imputation_f...
107 Random Forest_Oversampling_Median Imputation_f...
108 Random Forest_Oversampling_Median Imputation_f...
109 Random Forest_Oversampling_Median Imputation_f...
110 Random Forest_Oversampling_Median Imputation_f...
111 Random Forest_Oversampling_Median Imputation_f...
112 Random Forest_Oversampling_Median Imputation_f...
113 Random Forest_Oversampling_Median Imputation_f...
114 Random Forest_Oversampling_Median Imputation_f...
115 Random Forest_Oversampling_Median Imputation_f...
116 Random Forest_Oversampling_Median Imputation_f...
117 Random Forest_Oversampling_Median Imputation_f...
118 Random Forest_Oversampling_Median Imputation_f...
119 Random Forest_Oversampling_Median Imputation_f...
120 Random Forest_Oversampling_Median Imputation_f...
121 Random Forest_Oversampling_Median Imputation_f...
122 Random Forest_Oversampling_Median Imputation_f...
123 Random Forest_Oversampling_Median Imputation_f...
124 Random Forest_Oversampling_Median Imputation_f... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Random Forest 0.688965 NaN Zero Imputation
1 Random Forest 0.118740 NaN Zero Imputation
2 Random Forest 0.620253 NaN Zero Imputation
3 Random Forest 0.199322 NaN Zero Imputation
4 Random Forest 0.696303 NaN Zero Imputation
5 Random Forest 0.320868 NaN Zero Imputation
6 Random Forest 0.392605 NaN Zero Imputation
7 Random Forest NaN True Zero Imputation
8 Random Forest NaN 0.0 Zero Imputation
9 Random Forest NaN None Zero Imputation
10 Random Forest NaN gini Zero Imputation
11 Random Forest NaN None Zero Imputation
12 Random Forest NaN sqrt Zero Imputation
13 Random Forest NaN None Zero Imputation
14 Random Forest NaN None Zero Imputation
15 Random Forest NaN 0.0 Zero Imputation
16 Random Forest NaN 1 Zero Imputation
17 Random Forest NaN 2 Zero Imputation
18 Random Forest NaN 0.0 Zero Imputation
19 Random Forest NaN 100 Zero Imputation
20 Random Forest NaN None Zero Imputation
21 Random Forest NaN False Zero Imputation
22 Random Forest NaN None Zero Imputation
23 Random Forest NaN 0 Zero Imputation
24 Random Forest NaN False Zero Imputation
25 Random Forest 0.675007 NaN Zero Imputation
26 Random Forest 0.078740 NaN Zero Imputation
27 Random Forest 0.609756 NaN Zero Imputation
28 Random Forest 0.139470 NaN Zero Imputation
29 Random Forest 0.688446 NaN Zero Imputation
30 Random Forest 0.290167 NaN Zero Imputation
31 Random Forest 0.376892 NaN Zero Imputation
32 Random Forest NaN True Zero Imputation
33 Random Forest NaN 0.0 Zero Imputation
34 Random Forest NaN None Zero Imputation
35 Random Forest NaN gini Zero Imputation
36 Random Forest NaN None Zero Imputation
37 Random Forest NaN sqrt Zero Imputation
38 Random Forest NaN None Zero Imputation
39 Random Forest NaN None Zero Imputation
40 Random Forest NaN 0.0 Zero Imputation
41 Random Forest NaN 1 Zero Imputation
42 Random Forest NaN 2 Zero Imputation
43 Random Forest NaN 0.0 Zero Imputation
44 Random Forest NaN 100 Zero Imputation
45 Random Forest NaN None Zero Imputation
46 Random Forest NaN False Zero Imputation
47 Random Forest NaN None Zero Imputation
48 Random Forest NaN 0 Zero Imputation
49 Random Forest NaN False Zero Imputation
50 Random Forest 0.652884 NaN Zero Imputation
51 Random Forest 0.085714 NaN Zero Imputation
52 Random Forest 0.625668 NaN Zero Imputation
53 Random Forest 0.150773 NaN Zero Imputation
54 Random Forest 0.675322 NaN Zero Imputation
55 Random Forest 0.310531 NaN Zero Imputation
56 Random Forest 0.350644 NaN Zero Imputation
57 Random Forest NaN True Zero Imputation
58 Random Forest NaN 0.0 Zero Imputation
59 Random Forest NaN None Zero Imputation
60 Random Forest NaN gini Zero Imputation
61 Random Forest NaN None Zero Imputation
62 Random Forest NaN sqrt Zero Imputation
63 Random Forest NaN None Zero Imputation
64 Random Forest NaN None Zero Imputation
65 Random Forest NaN 0.0 Zero Imputation
66 Random Forest NaN 1 Zero Imputation
67 Random Forest NaN 2 Zero Imputation
68 Random Forest NaN 0.0 Zero Imputation
69 Random Forest NaN 100 Zero Imputation
70 Random Forest NaN None Zero Imputation
71 Random Forest NaN False Zero Imputation
72 Random Forest NaN None Zero Imputation
73 Random Forest NaN 0 Zero Imputation
74 Random Forest NaN False Zero Imputation
75 Random Forest 0.687829 NaN Zero Imputation
76 Random Forest 0.095004 NaN Zero Imputation
77 Random Forest 0.591837 NaN Zero Imputation
78 Random Forest 0.163726 NaN Zero Imputation
79 Random Forest 0.667464 NaN Zero Imputation
80 Random Forest 0.290023 NaN Zero Imputation
81 Random Forest 0.334928 NaN Zero Imputation
82 Random Forest NaN True Zero Imputation
83 Random Forest NaN 0.0 Zero Imputation
84 Random Forest NaN None Zero Imputation
85 Random Forest NaN gini Zero Imputation
86 Random Forest NaN None Zero Imputation
87 Random Forest NaN sqrt Zero Imputation
88 Random Forest NaN None Zero Imputation
89 Random Forest NaN None Zero Imputation
90 Random Forest NaN 0.0 Zero Imputation
91 Random Forest NaN 1 Zero Imputation
92 Random Forest NaN 2 Zero Imputation
93 Random Forest NaN 0.0 Zero Imputation
94 Random Forest NaN 100 Zero Imputation
95 Random Forest NaN None Zero Imputation
96 Random Forest NaN False Zero Imputation
97 Random Forest NaN None Zero Imputation
98 Random Forest NaN 0 Zero Imputation
99 Random Forest NaN False Zero Imputation
100 Random Forest 0.678609 NaN Zero Imputation
101 Random Forest 0.100954 NaN Zero Imputation
102 Random Forest 0.587963 NaN Zero Imputation
103 Random Forest 0.172320 NaN Zero Imputation
104 Random Forest 0.683909 NaN Zero Imputation
105 Random Forest 0.299995 NaN Zero Imputation
106 Random Forest 0.367819 NaN Zero Imputation
107 Random Forest NaN True Zero Imputation
108 Random Forest NaN 0.0 Zero Imputation
109 Random Forest NaN None Zero Imputation
110 Random Forest NaN gini Zero Imputation
111 Random Forest NaN None Zero Imputation
112 Random Forest NaN sqrt Zero Imputation
113 Random Forest NaN None Zero Imputation
114 Random Forest NaN None Zero Imputation
115 Random Forest NaN 0.0 Zero Imputation
116 Random Forest NaN 1 Zero Imputation
117 Random Forest NaN 2 Zero Imputation
118 Random Forest NaN 0.0 Zero Imputation
119 Random Forest NaN 100 Zero Imputation
120 Random Forest NaN None Zero Imputation
121 Random Forest NaN False Zero Imputation
122 Random Forest NaN None Zero Imputation
123 Random Forest NaN 0 Zero Imputation
124 Random Forest NaN False Zero Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
45 Undersampling
46 Undersampling
47 Undersampling
48 Undersampling
49 Undersampling
50 Undersampling
51 Undersampling
52 Undersampling
53 Undersampling
54 Undersampling
55 Undersampling
56 Undersampling
57 Undersampling
58 Undersampling
59 Undersampling
60 Undersampling
61 Undersampling
62 Undersampling
63 Undersampling
64 Undersampling
65 Undersampling
66 Undersampling
67 Undersampling
68 Undersampling
69 Undersampling
70 Undersampling
71 Undersampling
72 Undersampling
73 Undersampling
74 Undersampling
75 Undersampling
76 Undersampling
77 Undersampling
78 Undersampling
79 Undersampling
80 Undersampling
81 Undersampling
82 Undersampling
83 Undersampling
84 Undersampling
85 Undersampling
86 Undersampling
87 Undersampling
88 Undersampling
89 Undersampling
90 Undersampling
91 Undersampling
92 Undersampling
93 Undersampling
94 Undersampling
95 Undersampling
96 Undersampling
97 Undersampling
98 Undersampling
99 Undersampling
100 Undersampling
101 Undersampling
102 Undersampling
103 Undersampling
104 Undersampling
105 Undersampling
106 Undersampling
107 Undersampling
108 Undersampling
109 Undersampling
110 Undersampling
111 Undersampling
112 Undersampling
113 Undersampling
114 Undersampling
115 Undersampling
116 Undersampling
117 Undersampling
118 Undersampling
119 Undersampling
120 Undersampling
121 Undersampling
122 Undersampling
123 Undersampling
124 Undersampling
Model Unique Code
0 Random Forest_Undersampling_Zero Imputation_fo...
1 Random Forest_Undersampling_Zero Imputation_fo...
2 Random Forest_Undersampling_Zero Imputation_fo...
3 Random Forest_Undersampling_Zero Imputation_fo...
4 Random Forest_Undersampling_Zero Imputation_fo...
5 Random Forest_Undersampling_Zero Imputation_fo...
6 Random Forest_Undersampling_Zero Imputation_fo...
7 Random Forest_Undersampling_Zero Imputation_fo...
8 Random Forest_Undersampling_Zero Imputation_fo...
9 Random Forest_Undersampling_Zero Imputation_fo...
10 Random Forest_Undersampling_Zero Imputation_fo...
11 Random Forest_Undersampling_Zero Imputation_fo...
12 Random Forest_Undersampling_Zero Imputation_fo...
13 Random Forest_Undersampling_Zero Imputation_fo...
14 Random Forest_Undersampling_Zero Imputation_fo...
15 Random Forest_Undersampling_Zero Imputation_fo...
16 Random Forest_Undersampling_Zero Imputation_fo...
17 Random Forest_Undersampling_Zero Imputation_fo...
18 Random Forest_Undersampling_Zero Imputation_fo...
19 Random Forest_Undersampling_Zero Imputation_fo...
20 Random Forest_Undersampling_Zero Imputation_fo...
21 Random Forest_Undersampling_Zero Imputation_fo...
22 Random Forest_Undersampling_Zero Imputation_fo...
23 Random Forest_Undersampling_Zero Imputation_fo...
24 Random Forest_Undersampling_Zero Imputation_fo...
25 Random Forest_Undersampling_Zero Imputation_fo...
26 Random Forest_Undersampling_Zero Imputation_fo...
27 Random Forest_Undersampling_Zero Imputation_fo...
28 Random Forest_Undersampling_Zero Imputation_fo...
29 Random Forest_Undersampling_Zero Imputation_fo...
30 Random Forest_Undersampling_Zero Imputation_fo...
31 Random Forest_Undersampling_Zero Imputation_fo...
32 Random Forest_Undersampling_Zero Imputation_fo...
33 Random Forest_Undersampling_Zero Imputation_fo...
34 Random Forest_Undersampling_Zero Imputation_fo...
35 Random Forest_Undersampling_Zero Imputation_fo...
36 Random Forest_Undersampling_Zero Imputation_fo...
37 Random Forest_Undersampling_Zero Imputation_fo...
38 Random Forest_Undersampling_Zero Imputation_fo...
39 Random Forest_Undersampling_Zero Imputation_fo...
40 Random Forest_Undersampling_Zero Imputation_fo...
41 Random Forest_Undersampling_Zero Imputation_fo...
42 Random Forest_Undersampling_Zero Imputation_fo...
43 Random Forest_Undersampling_Zero Imputation_fo...
44 Random Forest_Undersampling_Zero Imputation_fo...
45 Random Forest_Undersampling_Zero Imputation_fo...
46 Random Forest_Undersampling_Zero Imputation_fo...
47 Random Forest_Undersampling_Zero Imputation_fo...
48 Random Forest_Undersampling_Zero Imputation_fo...
49 Random Forest_Undersampling_Zero Imputation_fo...
50 Random Forest_Undersampling_Zero Imputation_fo...
51 Random Forest_Undersampling_Zero Imputation_fo...
52 Random Forest_Undersampling_Zero Imputation_fo...
53 Random Forest_Undersampling_Zero Imputation_fo...
54 Random Forest_Undersampling_Zero Imputation_fo...
55 Random Forest_Undersampling_Zero Imputation_fo...
56 Random Forest_Undersampling_Zero Imputation_fo...
57 Random Forest_Undersampling_Zero Imputation_fo...
58 Random Forest_Undersampling_Zero Imputation_fo...
59 Random Forest_Undersampling_Zero Imputation_fo...
60 Random Forest_Undersampling_Zero Imputation_fo...
61 Random Forest_Undersampling_Zero Imputation_fo...
62 Random Forest_Undersampling_Zero Imputation_fo...
63 Random Forest_Undersampling_Zero Imputation_fo...
64 Random Forest_Undersampling_Zero Imputation_fo...
65 Random Forest_Undersampling_Zero Imputation_fo...
66 Random Forest_Undersampling_Zero Imputation_fo...
67 Random Forest_Undersampling_Zero Imputation_fo...
68 Random Forest_Undersampling_Zero Imputation_fo...
69 Random Forest_Undersampling_Zero Imputation_fo...
70 Random Forest_Undersampling_Zero Imputation_fo...
71 Random Forest_Undersampling_Zero Imputation_fo...
72 Random Forest_Undersampling_Zero Imputation_fo...
73 Random Forest_Undersampling_Zero Imputation_fo...
74 Random Forest_Undersampling_Zero Imputation_fo...
75 Random Forest_Undersampling_Zero Imputation_fo...
76 Random Forest_Undersampling_Zero Imputation_fo...
77 Random Forest_Undersampling_Zero Imputation_fo...
78 Random Forest_Undersampling_Zero Imputation_fo...
79 Random Forest_Undersampling_Zero Imputation_fo...
80 Random Forest_Undersampling_Zero Imputation_fo...
81 Random Forest_Undersampling_Zero Imputation_fo...
82 Random Forest_Undersampling_Zero Imputation_fo...
83 Random Forest_Undersampling_Zero Imputation_fo...
84 Random Forest_Undersampling_Zero Imputation_fo...
85 Random Forest_Undersampling_Zero Imputation_fo...
86 Random Forest_Undersampling_Zero Imputation_fo...
87 Random Forest_Undersampling_Zero Imputation_fo...
88 Random Forest_Undersampling_Zero Imputation_fo...
89 Random Forest_Undersampling_Zero Imputation_fo...
90 Random Forest_Undersampling_Zero Imputation_fo...
91 Random Forest_Undersampling_Zero Imputation_fo...
92 Random Forest_Undersampling_Zero Imputation_fo...
93 Random Forest_Undersampling_Zero Imputation_fo...
94 Random Forest_Undersampling_Zero Imputation_fo...
95 Random Forest_Undersampling_Zero Imputation_fo...
96 Random Forest_Undersampling_Zero Imputation_fo...
97 Random Forest_Undersampling_Zero Imputation_fo...
98 Random Forest_Undersampling_Zero Imputation_fo...
99 Random Forest_Undersampling_Zero Imputation_fo...
100 Random Forest_Undersampling_Zero Imputation_fo...
101 Random Forest_Undersampling_Zero Imputation_fo...
102 Random Forest_Undersampling_Zero Imputation_fo...
103 Random Forest_Undersampling_Zero Imputation_fo...
104 Random Forest_Undersampling_Zero Imputation_fo...
105 Random Forest_Undersampling_Zero Imputation_fo...
106 Random Forest_Undersampling_Zero Imputation_fo...
107 Random Forest_Undersampling_Zero Imputation_fo...
108 Random Forest_Undersampling_Zero Imputation_fo...
109 Random Forest_Undersampling_Zero Imputation_fo...
110 Random Forest_Undersampling_Zero Imputation_fo...
111 Random Forest_Undersampling_Zero Imputation_fo...
112 Random Forest_Undersampling_Zero Imputation_fo...
113 Random Forest_Undersampling_Zero Imputation_fo...
114 Random Forest_Undersampling_Zero Imputation_fo...
115 Random Forest_Undersampling_Zero Imputation_fo...
116 Random Forest_Undersampling_Zero Imputation_fo...
117 Random Forest_Undersampling_Zero Imputation_fo...
118 Random Forest_Undersampling_Zero Imputation_fo...
119 Random Forest_Undersampling_Zero Imputation_fo...
120 Random Forest_Undersampling_Zero Imputation_fo...
121 Random Forest_Undersampling_Zero Imputation_fo...
122 Random Forest_Undersampling_Zero Imputation_fo...
123 Random Forest_Undersampling_Zero Imputation_fo...
124 Random Forest_Undersampling_Zero Imputation_fo... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Random Forest 0.684224 NaN Mean Imputation
1 Random Forest 0.110211 NaN Mean Imputation
2 Random Forest 0.573840 NaN Mean Imputation
3 Random Forest 0.184908 NaN Mean Imputation
4 Random Forest 0.686369 NaN Mean Imputation
5 Random Forest 0.304038 NaN Mean Imputation
6 Random Forest 0.372737 NaN Mean Imputation
7 Random Forest NaN True Mean Imputation
8 Random Forest NaN 0.0 Mean Imputation
9 Random Forest NaN None Mean Imputation
10 Random Forest NaN gini Mean Imputation
11 Random Forest NaN None Mean Imputation
12 Random Forest NaN sqrt Mean Imputation
13 Random Forest NaN None Mean Imputation
14 Random Forest NaN None Mean Imputation
15 Random Forest NaN 0.0 Mean Imputation
16 Random Forest NaN 1 Mean Imputation
17 Random Forest NaN 2 Mean Imputation
18 Random Forest NaN 0.0 Mean Imputation
19 Random Forest NaN 100 Mean Imputation
20 Random Forest NaN None Mean Imputation
21 Random Forest NaN False Mean Imputation
22 Random Forest NaN None Mean Imputation
23 Random Forest NaN 0 Mean Imputation
24 Random Forest NaN False Mean Imputation
25 Random Forest 0.693179 NaN Mean Imputation
26 Random Forest 0.077637 NaN Mean Imputation
27 Random Forest 0.560976 NaN Mean Imputation
28 Random Forest 0.136397 NaN Mean Imputation
29 Random Forest 0.699138 NaN Mean Imputation
30 Random Forest 0.302441 NaN Mean Imputation
31 Random Forest 0.398277 NaN Mean Imputation
32 Random Forest NaN True Mean Imputation
33 Random Forest NaN 0.0 Mean Imputation
34 Random Forest NaN None Mean Imputation
35 Random Forest NaN gini Mean Imputation
36 Random Forest NaN None Mean Imputation
37 Random Forest NaN sqrt Mean Imputation
38 Random Forest NaN None Mean Imputation
39 Random Forest NaN None Mean Imputation
40 Random Forest NaN 0.0 Mean Imputation
41 Random Forest NaN 1 Mean Imputation
42 Random Forest NaN 2 Mean Imputation
43 Random Forest NaN 0.0 Mean Imputation
44 Random Forest NaN 100 Mean Imputation
45 Random Forest NaN None Mean Imputation
46 Random Forest NaN False Mean Imputation
47 Random Forest NaN None Mean Imputation
48 Random Forest NaN 0 Mean Imputation
49 Random Forest NaN False Mean Imputation
50 Random Forest 0.657361 NaN Mean Imputation
51 Random Forest 0.089838 NaN Mean Imputation
52 Random Forest 0.652406 NaN Mean Imputation
53 Random Forest 0.157929 NaN Mean Imputation
54 Random Forest 0.692505 NaN Mean Imputation
55 Random Forest 0.310578 NaN Mean Imputation
56 Random Forest 0.385010 NaN Mean Imputation
57 Random Forest NaN True Mean Imputation
58 Random Forest NaN 0.0 Mean Imputation
59 Random Forest NaN None Mean Imputation
60 Random Forest NaN gini Mean Imputation
61 Random Forest NaN None Mean Imputation
62 Random Forest NaN sqrt Mean Imputation
63 Random Forest NaN None Mean Imputation
64 Random Forest NaN None Mean Imputation
65 Random Forest NaN 0.0 Mean Imputation
66 Random Forest NaN 1 Mean Imputation
67 Random Forest NaN 2 Mean Imputation
68 Random Forest NaN 0.0 Mean Imputation
69 Random Forest NaN 100 Mean Imputation
70 Random Forest NaN None Mean Imputation
71 Random Forest NaN False Mean Imputation
72 Random Forest NaN None Mean Imputation
73 Random Forest NaN 0 Mean Imputation
74 Random Forest NaN False Mean Imputation
75 Random Forest 0.673077 NaN Mean Imputation
76 Random Forest 0.093385 NaN Mean Imputation
77 Random Forest 0.612245 NaN Mean Imputation
78 Random Forest 0.162053 NaN Mean Imputation
79 Random Forest 0.679978 NaN Mean Imputation
80 Random Forest 0.293645 NaN Mean Imputation
81 Random Forest 0.359956 NaN Mean Imputation
82 Random Forest NaN True Mean Imputation
83 Random Forest NaN 0.0 Mean Imputation
84 Random Forest NaN None Mean Imputation
85 Random Forest NaN gini Mean Imputation
86 Random Forest NaN None Mean Imputation
87 Random Forest NaN sqrt Mean Imputation
88 Random Forest NaN None Mean Imputation
89 Random Forest NaN None Mean Imputation
90 Random Forest NaN 0.0 Mean Imputation
91 Random Forest NaN 1 Mean Imputation
92 Random Forest NaN 2 Mean Imputation
93 Random Forest NaN 0.0 Mean Imputation
94 Random Forest NaN 100 Mean Imputation
95 Random Forest NaN None Mean Imputation
96 Random Forest NaN False Mean Imputation
97 Random Forest NaN None Mean Imputation
98 Random Forest NaN 0 Mean Imputation
99 Random Forest NaN False Mean Imputation
100 Random Forest 0.689410 NaN Mean Imputation
101 Random Forest 0.106296 NaN Mean Imputation
102 Random Forest 0.601852 NaN Mean Imputation
103 Random Forest 0.180681 NaN Mean Imputation
104 Random Forest 0.684950 NaN Mean Imputation
105 Random Forest 0.307676 NaN Mean Imputation
106 Random Forest 0.369900 NaN Mean Imputation
107 Random Forest NaN True Mean Imputation
108 Random Forest NaN 0.0 Mean Imputation
109 Random Forest NaN None Mean Imputation
110 Random Forest NaN gini Mean Imputation
111 Random Forest NaN None Mean Imputation
112 Random Forest NaN sqrt Mean Imputation
113 Random Forest NaN None Mean Imputation
114 Random Forest NaN None Mean Imputation
115 Random Forest NaN 0.0 Mean Imputation
116 Random Forest NaN 1 Mean Imputation
117 Random Forest NaN 2 Mean Imputation
118 Random Forest NaN 0.0 Mean Imputation
119 Random Forest NaN 100 Mean Imputation
120 Random Forest NaN None Mean Imputation
121 Random Forest NaN False Mean Imputation
122 Random Forest NaN None Mean Imputation
123 Random Forest NaN 0 Mean Imputation
124 Random Forest NaN False Mean Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
45 Undersampling
46 Undersampling
47 Undersampling
48 Undersampling
49 Undersampling
50 Undersampling
51 Undersampling
52 Undersampling
53 Undersampling
54 Undersampling
55 Undersampling
56 Undersampling
57 Undersampling
58 Undersampling
59 Undersampling
60 Undersampling
61 Undersampling
62 Undersampling
63 Undersampling
64 Undersampling
65 Undersampling
66 Undersampling
67 Undersampling
68 Undersampling
69 Undersampling
70 Undersampling
71 Undersampling
72 Undersampling
73 Undersampling
74 Undersampling
75 Undersampling
76 Undersampling
77 Undersampling
78 Undersampling
79 Undersampling
80 Undersampling
81 Undersampling
82 Undersampling
83 Undersampling
84 Undersampling
85 Undersampling
86 Undersampling
87 Undersampling
88 Undersampling
89 Undersampling
90 Undersampling
91 Undersampling
92 Undersampling
93 Undersampling
94 Undersampling
95 Undersampling
96 Undersampling
97 Undersampling
98 Undersampling
99 Undersampling
100 Undersampling
101 Undersampling
102 Undersampling
103 Undersampling
104 Undersampling
105 Undersampling
106 Undersampling
107 Undersampling
108 Undersampling
109 Undersampling
110 Undersampling
111 Undersampling
112 Undersampling
113 Undersampling
114 Undersampling
115 Undersampling
116 Undersampling
117 Undersampling
118 Undersampling
119 Undersampling
120 Undersampling
121 Undersampling
122 Undersampling
123 Undersampling
124 Undersampling
Model Unique Code
0 Random Forest_Undersampling_Mean Imputation_fo...
1 Random Forest_Undersampling_Mean Imputation_fo...
2 Random Forest_Undersampling_Mean Imputation_fo...
3 Random Forest_Undersampling_Mean Imputation_fo...
4 Random Forest_Undersampling_Mean Imputation_fo...
5 Random Forest_Undersampling_Mean Imputation_fo...
6 Random Forest_Undersampling_Mean Imputation_fo...
7 Random Forest_Undersampling_Mean Imputation_fo...
8 Random Forest_Undersampling_Mean Imputation_fo...
9 Random Forest_Undersampling_Mean Imputation_fo...
10 Random Forest_Undersampling_Mean Imputation_fo...
11 Random Forest_Undersampling_Mean Imputation_fo...
12 Random Forest_Undersampling_Mean Imputation_fo...
13 Random Forest_Undersampling_Mean Imputation_fo...
14 Random Forest_Undersampling_Mean Imputation_fo...
15 Random Forest_Undersampling_Mean Imputation_fo...
16 Random Forest_Undersampling_Mean Imputation_fo...
17 Random Forest_Undersampling_Mean Imputation_fo...
18 Random Forest_Undersampling_Mean Imputation_fo...
19 Random Forest_Undersampling_Mean Imputation_fo...
20 Random Forest_Undersampling_Mean Imputation_fo...
21 Random Forest_Undersampling_Mean Imputation_fo...
22 Random Forest_Undersampling_Mean Imputation_fo...
23 Random Forest_Undersampling_Mean Imputation_fo...
24 Random Forest_Undersampling_Mean Imputation_fo...
25 Random Forest_Undersampling_Mean Imputation_fo...
26 Random Forest_Undersampling_Mean Imputation_fo...
27 Random Forest_Undersampling_Mean Imputation_fo...
28 Random Forest_Undersampling_Mean Imputation_fo...
29 Random Forest_Undersampling_Mean Imputation_fo...
30 Random Forest_Undersampling_Mean Imputation_fo...
31 Random Forest_Undersampling_Mean Imputation_fo...
32 Random Forest_Undersampling_Mean Imputation_fo...
33 Random Forest_Undersampling_Mean Imputation_fo...
34 Random Forest_Undersampling_Mean Imputation_fo...
35 Random Forest_Undersampling_Mean Imputation_fo...
36 Random Forest_Undersampling_Mean Imputation_fo...
37 Random Forest_Undersampling_Mean Imputation_fo...
38 Random Forest_Undersampling_Mean Imputation_fo...
39 Random Forest_Undersampling_Mean Imputation_fo...
40 Random Forest_Undersampling_Mean Imputation_fo...
41 Random Forest_Undersampling_Mean Imputation_fo...
42 Random Forest_Undersampling_Mean Imputation_fo...
43 Random Forest_Undersampling_Mean Imputation_fo...
44 Random Forest_Undersampling_Mean Imputation_fo...
45 Random Forest_Undersampling_Mean Imputation_fo...
46 Random Forest_Undersampling_Mean Imputation_fo...
47 Random Forest_Undersampling_Mean Imputation_fo...
48 Random Forest_Undersampling_Mean Imputation_fo...
49 Random Forest_Undersampling_Mean Imputation_fo...
50 Random Forest_Undersampling_Mean Imputation_fo...
51 Random Forest_Undersampling_Mean Imputation_fo...
52 Random Forest_Undersampling_Mean Imputation_fo...
53 Random Forest_Undersampling_Mean Imputation_fo...
54 Random Forest_Undersampling_Mean Imputation_fo...
55 Random Forest_Undersampling_Mean Imputation_fo...
56 Random Forest_Undersampling_Mean Imputation_fo...
57 Random Forest_Undersampling_Mean Imputation_fo...
58 Random Forest_Undersampling_Mean Imputation_fo...
59 Random Forest_Undersampling_Mean Imputation_fo...
60 Random Forest_Undersampling_Mean Imputation_fo...
61 Random Forest_Undersampling_Mean Imputation_fo...
62 Random Forest_Undersampling_Mean Imputation_fo...
63 Random Forest_Undersampling_Mean Imputation_fo...
64 Random Forest_Undersampling_Mean Imputation_fo...
65 Random Forest_Undersampling_Mean Imputation_fo...
66 Random Forest_Undersampling_Mean Imputation_fo...
67 Random Forest_Undersampling_Mean Imputation_fo...
68 Random Forest_Undersampling_Mean Imputation_fo...
69 Random Forest_Undersampling_Mean Imputation_fo...
70 Random Forest_Undersampling_Mean Imputation_fo...
71 Random Forest_Undersampling_Mean Imputation_fo...
72 Random Forest_Undersampling_Mean Imputation_fo...
73 Random Forest_Undersampling_Mean Imputation_fo...
74 Random Forest_Undersampling_Mean Imputation_fo...
75 Random Forest_Undersampling_Mean Imputation_fo...
76 Random Forest_Undersampling_Mean Imputation_fo...
77 Random Forest_Undersampling_Mean Imputation_fo...
78 Random Forest_Undersampling_Mean Imputation_fo...
79 Random Forest_Undersampling_Mean Imputation_fo...
80 Random Forest_Undersampling_Mean Imputation_fo...
81 Random Forest_Undersampling_Mean Imputation_fo...
82 Random Forest_Undersampling_Mean Imputation_fo...
83 Random Forest_Undersampling_Mean Imputation_fo...
84 Random Forest_Undersampling_Mean Imputation_fo...
85 Random Forest_Undersampling_Mean Imputation_fo...
86 Random Forest_Undersampling_Mean Imputation_fo...
87 Random Forest_Undersampling_Mean Imputation_fo...
88 Random Forest_Undersampling_Mean Imputation_fo...
89 Random Forest_Undersampling_Mean Imputation_fo...
90 Random Forest_Undersampling_Mean Imputation_fo...
91 Random Forest_Undersampling_Mean Imputation_fo...
92 Random Forest_Undersampling_Mean Imputation_fo...
93 Random Forest_Undersampling_Mean Imputation_fo...
94 Random Forest_Undersampling_Mean Imputation_fo...
95 Random Forest_Undersampling_Mean Imputation_fo...
96 Random Forest_Undersampling_Mean Imputation_fo...
97 Random Forest_Undersampling_Mean Imputation_fo...
98 Random Forest_Undersampling_Mean Imputation_fo...
99 Random Forest_Undersampling_Mean Imputation_fo...
100 Random Forest_Undersampling_Mean Imputation_fo...
101 Random Forest_Undersampling_Mean Imputation_fo...
102 Random Forest_Undersampling_Mean Imputation_fo...
103 Random Forest_Undersampling_Mean Imputation_fo...
104 Random Forest_Undersampling_Mean Imputation_fo...
105 Random Forest_Undersampling_Mean Imputation_fo...
106 Random Forest_Undersampling_Mean Imputation_fo...
107 Random Forest_Undersampling_Mean Imputation_fo...
108 Random Forest_Undersampling_Mean Imputation_fo...
109 Random Forest_Undersampling_Mean Imputation_fo...
110 Random Forest_Undersampling_Mean Imputation_fo...
111 Random Forest_Undersampling_Mean Imputation_fo...
112 Random Forest_Undersampling_Mean Imputation_fo...
113 Random Forest_Undersampling_Mean Imputation_fo...
114 Random Forest_Undersampling_Mean Imputation_fo...
115 Random Forest_Undersampling_Mean Imputation_fo...
116 Random Forest_Undersampling_Mean Imputation_fo...
117 Random Forest_Undersampling_Mean Imputation_fo...
118 Random Forest_Undersampling_Mean Imputation_fo...
119 Random Forest_Undersampling_Mean Imputation_fo...
120 Random Forest_Undersampling_Mean Imputation_fo...
121 Random Forest_Undersampling_Mean Imputation_fo...
122 Random Forest_Undersampling_Mean Imputation_fo...
123 Random Forest_Undersampling_Mean Imputation_fo...
124 Random Forest_Undersampling_Mean Imputation_fo... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Random Forest 0.678167 NaN Median Imputation
1 Random Forest 0.109437 NaN Median Imputation
2 Random Forest 0.582278 NaN Median Imputation
3 Random Forest 0.184246 NaN Median Imputation
4 Random Forest 0.683444 NaN Median Imputation
5 Random Forest 0.287010 NaN Median Imputation
6 Random Forest 0.366887 NaN Median Imputation
7 Random Forest NaN True Median Imputation
8 Random Forest NaN 0.0 Median Imputation
9 Random Forest NaN None Median Imputation
10 Random Forest NaN gini Median Imputation
11 Random Forest NaN None Median Imputation
12 Random Forest NaN sqrt Median Imputation
13 Random Forest NaN None Median Imputation
14 Random Forest NaN None Median Imputation
15 Random Forest NaN 0.0 Median Imputation
16 Random Forest NaN 1 Median Imputation
17 Random Forest NaN 2 Median Imputation
18 Random Forest NaN 0.0 Median Imputation
19 Random Forest NaN 100 Median Imputation
20 Random Forest NaN None Median Imputation
21 Random Forest NaN False Median Imputation
22 Random Forest NaN None Median Imputation
23 Random Forest NaN 0 Median Imputation
24 Random Forest NaN False Median Imputation
25 Random Forest 0.673953 NaN Median Imputation
26 Random Forest 0.077165 NaN Median Imputation
27 Random Forest 0.597561 NaN Median Imputation
28 Random Forest 0.136681 NaN Median Imputation
29 Random Forest 0.689483 NaN Median Imputation
30 Random Forest 0.296385 NaN Median Imputation
31 Random Forest 0.378967 NaN Median Imputation
32 Random Forest NaN True Median Imputation
33 Random Forest NaN 0.0 Median Imputation
34 Random Forest NaN None Median Imputation
35 Random Forest NaN gini Median Imputation
36 Random Forest NaN None Median Imputation
37 Random Forest NaN sqrt Median Imputation
38 Random Forest NaN None Median Imputation
39 Random Forest NaN None Median Imputation
40 Random Forest NaN 0.0 Median Imputation
41 Random Forest NaN 1 Median Imputation
42 Random Forest NaN 2 Median Imputation
43 Random Forest NaN 0.0 Median Imputation
44 Random Forest NaN 100 Median Imputation
45 Random Forest NaN None Median Imputation
46 Random Forest NaN False Median Imputation
47 Random Forest NaN None Median Imputation
48 Random Forest NaN 0 Median Imputation
49 Random Forest NaN False Median Imputation
50 Random Forest 0.655254 NaN Median Imputation
51 Random Forest 0.083210 NaN Median Imputation
52 Random Forest 0.598930 NaN Median Imputation
53 Random Forest 0.146119 NaN Median Imputation
54 Random Forest 0.666184 NaN Median Imputation
55 Random Forest 0.276905 NaN Median Imputation
56 Random Forest 0.332368 NaN Median Imputation
57 Random Forest NaN True Median Imputation
58 Random Forest NaN 0.0 Median Imputation
59 Random Forest NaN None Median Imputation
60 Random Forest NaN gini Median Imputation
61 Random Forest NaN None Median Imputation
62 Random Forest NaN sqrt Median Imputation
63 Random Forest NaN None Median Imputation
64 Random Forest NaN None Median Imputation
65 Random Forest NaN 0.0 Median Imputation
66 Random Forest NaN 1 Median Imputation
67 Random Forest NaN 2 Median Imputation
68 Random Forest NaN 0.0 Median Imputation
69 Random Forest NaN 100 Median Imputation
70 Random Forest NaN None Median Imputation
71 Random Forest NaN False Median Imputation
72 Random Forest NaN None Median Imputation
73 Random Forest NaN 0 Median Imputation
74 Random Forest NaN False Median Imputation
75 Random Forest 0.669652 NaN Median Imputation
76 Random Forest 0.089922 NaN Median Imputation
77 Random Forest 0.591837 NaN Median Imputation
78 Random Forest 0.156124 NaN Median Imputation
79 Random Forest 0.668080 NaN Median Imputation
80 Random Forest 0.274756 NaN Median Imputation
81 Random Forest 0.336159 NaN Median Imputation
82 Random Forest NaN True Median Imputation
83 Random Forest NaN 0.0 Median Imputation
84 Random Forest NaN None Median Imputation
85 Random Forest NaN gini Median Imputation
86 Random Forest NaN None Median Imputation
87 Random Forest NaN sqrt Median Imputation
88 Random Forest NaN None Median Imputation
89 Random Forest NaN None Median Imputation
90 Random Forest NaN 0.0 Median Imputation
91 Random Forest NaN 1 Median Imputation
92 Random Forest NaN 2 Median Imputation
93 Random Forest NaN 0.0 Median Imputation
94 Random Forest NaN 100 Median Imputation
95 Random Forest NaN None Median Imputation
96 Random Forest NaN False Median Imputation
97 Random Forest NaN None Median Imputation
98 Random Forest NaN 0 Median Imputation
99 Random Forest NaN False Median Imputation
100 Random Forest 0.679136 NaN Median Imputation
101 Random Forest 0.101115 NaN Median Imputation
102 Random Forest 0.587963 NaN Median Imputation
103 Random Forest 0.172554 NaN Median Imputation
104 Random Forest 0.684388 NaN Median Imputation
105 Random Forest 0.308494 NaN Median Imputation
106 Random Forest 0.368776 NaN Median Imputation
107 Random Forest NaN True Median Imputation
108 Random Forest NaN 0.0 Median Imputation
109 Random Forest NaN None Median Imputation
110 Random Forest NaN gini Median Imputation
111 Random Forest NaN None Median Imputation
112 Random Forest NaN sqrt Median Imputation
113 Random Forest NaN None Median Imputation
114 Random Forest NaN None Median Imputation
115 Random Forest NaN 0.0 Median Imputation
116 Random Forest NaN 1 Median Imputation
117 Random Forest NaN 2 Median Imputation
118 Random Forest NaN 0.0 Median Imputation
119 Random Forest NaN 100 Median Imputation
120 Random Forest NaN None Median Imputation
121 Random Forest NaN False Median Imputation
122 Random Forest NaN None Median Imputation
123 Random Forest NaN 0 Median Imputation
124 Random Forest NaN False Median Imputation
Imbalance Class Technique \
0 Undersampling
1 Undersampling
2 Undersampling
3 Undersampling
4 Undersampling
5 Undersampling
6 Undersampling
7 Undersampling
8 Undersampling
9 Undersampling
10 Undersampling
11 Undersampling
12 Undersampling
13 Undersampling
14 Undersampling
15 Undersampling
16 Undersampling
17 Undersampling
18 Undersampling
19 Undersampling
20 Undersampling
21 Undersampling
22 Undersampling
23 Undersampling
24 Undersampling
25 Undersampling
26 Undersampling
27 Undersampling
28 Undersampling
29 Undersampling
30 Undersampling
31 Undersampling
32 Undersampling
33 Undersampling
34 Undersampling
35 Undersampling
36 Undersampling
37 Undersampling
38 Undersampling
39 Undersampling
40 Undersampling
41 Undersampling
42 Undersampling
43 Undersampling
44 Undersampling
45 Undersampling
46 Undersampling
47 Undersampling
48 Undersampling
49 Undersampling
50 Undersampling
51 Undersampling
52 Undersampling
53 Undersampling
54 Undersampling
55 Undersampling
56 Undersampling
57 Undersampling
58 Undersampling
59 Undersampling
60 Undersampling
61 Undersampling
62 Undersampling
63 Undersampling
64 Undersampling
65 Undersampling
66 Undersampling
67 Undersampling
68 Undersampling
69 Undersampling
70 Undersampling
71 Undersampling
72 Undersampling
73 Undersampling
74 Undersampling
75 Undersampling
76 Undersampling
77 Undersampling
78 Undersampling
79 Undersampling
80 Undersampling
81 Undersampling
82 Undersampling
83 Undersampling
84 Undersampling
85 Undersampling
86 Undersampling
87 Undersampling
88 Undersampling
89 Undersampling
90 Undersampling
91 Undersampling
92 Undersampling
93 Undersampling
94 Undersampling
95 Undersampling
96 Undersampling
97 Undersampling
98 Undersampling
99 Undersampling
100 Undersampling
101 Undersampling
102 Undersampling
103 Undersampling
104 Undersampling
105 Undersampling
106 Undersampling
107 Undersampling
108 Undersampling
109 Undersampling
110 Undersampling
111 Undersampling
112 Undersampling
113 Undersampling
114 Undersampling
115 Undersampling
116 Undersampling
117 Undersampling
118 Undersampling
119 Undersampling
120 Undersampling
121 Undersampling
122 Undersampling
123 Undersampling
124 Undersampling
Model Unique Code
0 Random Forest_Undersampling_Median Imputation_...
1 Random Forest_Undersampling_Median Imputation_...
2 Random Forest_Undersampling_Median Imputation_...
3 Random Forest_Undersampling_Median Imputation_...
4 Random Forest_Undersampling_Median Imputation_...
5 Random Forest_Undersampling_Median Imputation_...
6 Random Forest_Undersampling_Median Imputation_...
7 Random Forest_Undersampling_Median Imputation_...
8 Random Forest_Undersampling_Median Imputation_...
9 Random Forest_Undersampling_Median Imputation_...
10 Random Forest_Undersampling_Median Imputation_...
11 Random Forest_Undersampling_Median Imputation_...
12 Random Forest_Undersampling_Median Imputation_...
13 Random Forest_Undersampling_Median Imputation_...
14 Random Forest_Undersampling_Median Imputation_...
15 Random Forest_Undersampling_Median Imputation_...
16 Random Forest_Undersampling_Median Imputation_...
17 Random Forest_Undersampling_Median Imputation_...
18 Random Forest_Undersampling_Median Imputation_...
19 Random Forest_Undersampling_Median Imputation_...
20 Random Forest_Undersampling_Median Imputation_...
21 Random Forest_Undersampling_Median Imputation_...
22 Random Forest_Undersampling_Median Imputation_...
23 Random Forest_Undersampling_Median Imputation_...
24 Random Forest_Undersampling_Median Imputation_...
25 Random Forest_Undersampling_Median Imputation_...
26 Random Forest_Undersampling_Median Imputation_...
27 Random Forest_Undersampling_Median Imputation_...
28 Random Forest_Undersampling_Median Imputation_...
29 Random Forest_Undersampling_Median Imputation_...
30 Random Forest_Undersampling_Median Imputation_...
31 Random Forest_Undersampling_Median Imputation_...
32 Random Forest_Undersampling_Median Imputation_...
33 Random Forest_Undersampling_Median Imputation_...
34 Random Forest_Undersampling_Median Imputation_...
35 Random Forest_Undersampling_Median Imputation_...
36 Random Forest_Undersampling_Median Imputation_...
37 Random Forest_Undersampling_Median Imputation_...
38 Random Forest_Undersampling_Median Imputation_...
39 Random Forest_Undersampling_Median Imputation_...
40 Random Forest_Undersampling_Median Imputation_...
41 Random Forest_Undersampling_Median Imputation_...
42 Random Forest_Undersampling_Median Imputation_...
43 Random Forest_Undersampling_Median Imputation_...
44 Random Forest_Undersampling_Median Imputation_...
45 Random Forest_Undersampling_Median Imputation_...
46 Random Forest_Undersampling_Median Imputation_...
47 Random Forest_Undersampling_Median Imputation_...
48 Random Forest_Undersampling_Median Imputation_...
49 Random Forest_Undersampling_Median Imputation_...
50 Random Forest_Undersampling_Median Imputation_...
51 Random Forest_Undersampling_Median Imputation_...
52 Random Forest_Undersampling_Median Imputation_...
53 Random Forest_Undersampling_Median Imputation_...
54 Random Forest_Undersampling_Median Imputation_...
55 Random Forest_Undersampling_Median Imputation_...
56 Random Forest_Undersampling_Median Imputation_...
57 Random Forest_Undersampling_Median Imputation_...
58 Random Forest_Undersampling_Median Imputation_...
59 Random Forest_Undersampling_Median Imputation_...
60 Random Forest_Undersampling_Median Imputation_...
61 Random Forest_Undersampling_Median Imputation_...
62 Random Forest_Undersampling_Median Imputation_...
63 Random Forest_Undersampling_Median Imputation_...
64 Random Forest_Undersampling_Median Imputation_...
65 Random Forest_Undersampling_Median Imputation_...
66 Random Forest_Undersampling_Median Imputation_...
67 Random Forest_Undersampling_Median Imputation_...
68 Random Forest_Undersampling_Median Imputation_...
69 Random Forest_Undersampling_Median Imputation_...
70 Random Forest_Undersampling_Median Imputation_...
71 Random Forest_Undersampling_Median Imputation_...
72 Random Forest_Undersampling_Median Imputation_...
73 Random Forest_Undersampling_Median Imputation_...
74 Random Forest_Undersampling_Median Imputation_...
75 Random Forest_Undersampling_Median Imputation_...
76 Random Forest_Undersampling_Median Imputation_...
77 Random Forest_Undersampling_Median Imputation_...
78 Random Forest_Undersampling_Median Imputation_...
79 Random Forest_Undersampling_Median Imputation_...
80 Random Forest_Undersampling_Median Imputation_...
81 Random Forest_Undersampling_Median Imputation_...
82 Random Forest_Undersampling_Median Imputation_...
83 Random Forest_Undersampling_Median Imputation_...
84 Random Forest_Undersampling_Median Imputation_...
85 Random Forest_Undersampling_Median Imputation_...
86 Random Forest_Undersampling_Median Imputation_...
87 Random Forest_Undersampling_Median Imputation_...
88 Random Forest_Undersampling_Median Imputation_...
89 Random Forest_Undersampling_Median Imputation_...
90 Random Forest_Undersampling_Median Imputation_...
91 Random Forest_Undersampling_Median Imputation_...
92 Random Forest_Undersampling_Median Imputation_...
93 Random Forest_Undersampling_Median Imputation_...
94 Random Forest_Undersampling_Median Imputation_...
95 Random Forest_Undersampling_Median Imputation_...
96 Random Forest_Undersampling_Median Imputation_...
97 Random Forest_Undersampling_Median Imputation_...
98 Random Forest_Undersampling_Median Imputation_...
99 Random Forest_Undersampling_Median Imputation_...
100 Random Forest_Undersampling_Median Imputation_...
101 Random Forest_Undersampling_Median Imputation_...
102 Random Forest_Undersampling_Median Imputation_...
103 Random Forest_Undersampling_Median Imputation_...
104 Random Forest_Undersampling_Median Imputation_...
105 Random Forest_Undersampling_Median Imputation_...
106 Random Forest_Undersampling_Median Imputation_...
107 Random Forest_Undersampling_Median Imputation_...
108 Random Forest_Undersampling_Median Imputation_...
109 Random Forest_Undersampling_Median Imputation_...
110 Random Forest_Undersampling_Median Imputation_...
111 Random Forest_Undersampling_Median Imputation_...
112 Random Forest_Undersampling_Median Imputation_...
113 Random Forest_Undersampling_Median Imputation_...
114 Random Forest_Undersampling_Median Imputation_...
115 Random Forest_Undersampling_Median Imputation_...
116 Random Forest_Undersampling_Median Imputation_...
117 Random Forest_Undersampling_Median Imputation_...
118 Random Forest_Undersampling_Median Imputation_...
119 Random Forest_Undersampling_Median Imputation_...
120 Random Forest_Undersampling_Median Imputation_...
121 Random Forest_Undersampling_Median Imputation_...
122 Random Forest_Undersampling_Median Imputation_...
123 Random Forest_Undersampling_Median Imputation_...
124 Random Forest_Undersampling_Median Imputation_... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Random Forest 0.902291 NaN Zero Imputation
1 Random Forest 0.174757 NaN Zero Imputation
2 Random Forest 0.151899 NaN Zero Imputation
3 Random Forest 0.162528 NaN Zero Imputation
4 Random Forest 0.628520 NaN Zero Imputation
5 Random Forest 0.230599 NaN Zero Imputation
6 Random Forest 0.257040 NaN Zero Imputation
7 Random Forest NaN True Zero Imputation
8 Random Forest NaN 0.0 Zero Imputation
9 Random Forest NaN None Zero Imputation
10 Random Forest NaN gini Zero Imputation
11 Random Forest NaN None Zero Imputation
12 Random Forest NaN sqrt Zero Imputation
13 Random Forest NaN None Zero Imputation
14 Random Forest NaN None Zero Imputation
15 Random Forest NaN 0.0 Zero Imputation
16 Random Forest NaN 1 Zero Imputation
17 Random Forest NaN 2 Zero Imputation
18 Random Forest NaN 0.0 Zero Imputation
19 Random Forest NaN 100 Zero Imputation
20 Random Forest NaN None Zero Imputation
21 Random Forest NaN False Zero Imputation
22 Random Forest NaN None Zero Imputation
23 Random Forest NaN 0 Zero Imputation
24 Random Forest NaN False Zero Imputation
25 Random Forest 0.898341 NaN Zero Imputation
26 Random Forest 0.114583 NaN Zero Imputation
27 Random Forest 0.201220 NaN Zero Imputation
28 Random Forest 0.146018 NaN Zero Imputation
29 Random Forest 0.650230 NaN Zero Imputation
30 Random Forest 0.308082 NaN Zero Imputation
31 Random Forest 0.300461 NaN Zero Imputation
32 Random Forest NaN True Zero Imputation
33 Random Forest NaN 0.0 Zero Imputation
34 Random Forest NaN None Zero Imputation
35 Random Forest NaN gini Zero Imputation
36 Random Forest NaN None Zero Imputation
37 Random Forest NaN sqrt Zero Imputation
38 Random Forest NaN None Zero Imputation
39 Random Forest NaN None Zero Imputation
40 Random Forest NaN 0.0 Zero Imputation
41 Random Forest NaN 1 Zero Imputation
42 Random Forest NaN 2 Zero Imputation
43 Random Forest NaN 0.0 Zero Imputation
44 Random Forest NaN 100 Zero Imputation
45 Random Forest NaN None Zero Imputation
46 Random Forest NaN False Zero Imputation
47 Random Forest NaN None Zero Imputation
48 Random Forest NaN 0 Zero Imputation
49 Random Forest NaN False Zero Imputation
50 Random Forest 0.897551 NaN Zero Imputation
51 Random Forest 0.120301 NaN Zero Imputation
52 Random Forest 0.171123 NaN Zero Imputation
53 Random Forest 0.141280 NaN Zero Imputation
54 Random Forest 0.597196 NaN Zero Imputation
55 Random Forest 0.235864 NaN Zero Imputation
56 Random Forest 0.194392 NaN Zero Imputation
57 Random Forest NaN True Zero Imputation
58 Random Forest NaN 0.0 Zero Imputation
59 Random Forest NaN None Zero Imputation
60 Random Forest NaN gini Zero Imputation
61 Random Forest NaN None Zero Imputation
62 Random Forest NaN sqrt Zero Imputation
63 Random Forest NaN None Zero Imputation
64 Random Forest NaN None Zero Imputation
65 Random Forest NaN 0.0 Zero Imputation
66 Random Forest NaN 1 Zero Imputation
67 Random Forest NaN 2 Zero Imputation
68 Random Forest NaN 0.0 Zero Imputation
69 Random Forest NaN 100 Zero Imputation
70 Random Forest NaN None Zero Imputation
71 Random Forest NaN False Zero Imputation
72 Random Forest NaN None Zero Imputation
73 Random Forest NaN 0 Zero Imputation
74 Random Forest NaN False Zero Imputation
75 Random Forest 0.899368 NaN Zero Imputation
76 Random Forest 0.118852 NaN Zero Imputation
77 Random Forest 0.147959 NaN Zero Imputation
78 Random Forest 0.131818 NaN Zero Imputation
79 Random Forest 0.582183 NaN Zero Imputation
80 Random Forest 0.166400 NaN Zero Imputation
81 Random Forest 0.164365 NaN Zero Imputation
82 Random Forest NaN True Zero Imputation
83 Random Forest NaN 0.0 Zero Imputation
84 Random Forest NaN None Zero Imputation
85 Random Forest NaN gini Zero Imputation
86 Random Forest NaN None Zero Imputation
87 Random Forest NaN sqrt Zero Imputation
88 Random Forest NaN None Zero Imputation
89 Random Forest NaN None Zero Imputation
90 Random Forest NaN 0.0 Zero Imputation
91 Random Forest NaN 1 Zero Imputation
92 Random Forest NaN 2 Zero Imputation
93 Random Forest NaN 0.0 Zero Imputation
94 Random Forest NaN 100 Zero Imputation
95 Random Forest NaN None Zero Imputation
96 Random Forest NaN False Zero Imputation
97 Random Forest NaN None Zero Imputation
98 Random Forest NaN 0 Zero Imputation
99 Random Forest NaN False Zero Imputation
100 Random Forest 0.894626 NaN Zero Imputation
101 Random Forest 0.151515 NaN Zero Imputation
102 Random Forest 0.185185 NaN Zero Imputation
103 Random Forest 0.166667 NaN Zero Imputation
104 Random Forest 0.601638 NaN Zero Imputation
105 Random Forest 0.242060 NaN Zero Imputation
106 Random Forest 0.203276 NaN Zero Imputation
107 Random Forest NaN True Zero Imputation
108 Random Forest NaN 0.0 Zero Imputation
109 Random Forest NaN None Zero Imputation
110 Random Forest NaN gini Zero Imputation
111 Random Forest NaN None Zero Imputation
112 Random Forest NaN sqrt Zero Imputation
113 Random Forest NaN None Zero Imputation
114 Random Forest NaN None Zero Imputation
115 Random Forest NaN 0.0 Zero Imputation
116 Random Forest NaN 1 Zero Imputation
117 Random Forest NaN 2 Zero Imputation
118 Random Forest NaN 0.0 Zero Imputation
119 Random Forest NaN 100 Zero Imputation
120 Random Forest NaN None Zero Imputation
121 Random Forest NaN False Zero Imputation
122 Random Forest NaN None Zero Imputation
123 Random Forest NaN 0 Zero Imputation
124 Random Forest NaN False Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
95 Hybrid Sampling (SMOTEENN)
96 Hybrid Sampling (SMOTEENN)
97 Hybrid Sampling (SMOTEENN)
98 Hybrid Sampling (SMOTEENN)
99 Hybrid Sampling (SMOTEENN)
100 Hybrid Sampling (SMOTEENN)
101 Hybrid Sampling (SMOTEENN)
102 Hybrid Sampling (SMOTEENN)
103 Hybrid Sampling (SMOTEENN)
104 Hybrid Sampling (SMOTEENN)
105 Hybrid Sampling (SMOTEENN)
106 Hybrid Sampling (SMOTEENN)
107 Hybrid Sampling (SMOTEENN)
108 Hybrid Sampling (SMOTEENN)
109 Hybrid Sampling (SMOTEENN)
110 Hybrid Sampling (SMOTEENN)
111 Hybrid Sampling (SMOTEENN)
112 Hybrid Sampling (SMOTEENN)
113 Hybrid Sampling (SMOTEENN)
114 Hybrid Sampling (SMOTEENN)
115 Hybrid Sampling (SMOTEENN)
116 Hybrid Sampling (SMOTEENN)
117 Hybrid Sampling (SMOTEENN)
118 Hybrid Sampling (SMOTEENN)
119 Hybrid Sampling (SMOTEENN)
120 Hybrid Sampling (SMOTEENN)
121 Hybrid Sampling (SMOTEENN)
122 Hybrid Sampling (SMOTEENN)
123 Hybrid Sampling (SMOTEENN)
124 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
1 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
2 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
3 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
4 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
5 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
6 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
7 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
8 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
9 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
10 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
11 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
12 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
13 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
14 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
15 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
16 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
17 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
18 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
19 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
20 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
21 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
22 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
23 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
24 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
25 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
26 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
27 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
28 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
29 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
30 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
31 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
32 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
33 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
34 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
35 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
36 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
37 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
38 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
39 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
40 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
41 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
42 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
43 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
44 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
45 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
46 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
47 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
48 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
49 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
50 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
51 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
52 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
53 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
54 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
55 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
56 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
57 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
58 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
59 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
60 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
61 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
62 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
63 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
64 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
65 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
66 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
67 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
68 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
69 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
70 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
71 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
72 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
73 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
74 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
75 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
76 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
77 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
78 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
79 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
80 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
81 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
82 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
83 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
84 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
85 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
86 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
87 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
88 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
89 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
90 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
91 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
92 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
93 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
94 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
95 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
96 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
97 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
98 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
99 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
100 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
101 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
102 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
103 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
104 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
105 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
106 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
107 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
108 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
109 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
110 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
111 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
112 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
113 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
114 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
115 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
116 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
117 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
118 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
119 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
120 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
121 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
122 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
123 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ...
124 Random Forest_Hybrid Sampling (SMOTEENN)_Zero ... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Random Forest 0.893337 NaN Mean Imputation
1 Random Forest 0.144068 NaN Mean Imputation
2 Random Forest 0.143460 NaN Mean Imputation
3 Random Forest 0.143763 NaN Mean Imputation
4 Random Forest 0.626762 NaN Mean Imputation
5 Random Forest 0.238124 NaN Mean Imputation
6 Random Forest 0.253524 NaN Mean Imputation
7 Random Forest NaN True Mean Imputation
8 Random Forest NaN 0.0 Mean Imputation
9 Random Forest NaN None Mean Imputation
10 Random Forest NaN gini Mean Imputation
11 Random Forest NaN None Mean Imputation
12 Random Forest NaN sqrt Mean Imputation
13 Random Forest NaN None Mean Imputation
14 Random Forest NaN None Mean Imputation
15 Random Forest NaN 0.0 Mean Imputation
16 Random Forest NaN 1 Mean Imputation
17 Random Forest NaN 2 Mean Imputation
18 Random Forest NaN 0.0 Mean Imputation
19 Random Forest NaN 100 Mean Imputation
20 Random Forest NaN None Mean Imputation
21 Random Forest NaN False Mean Imputation
22 Random Forest NaN None Mean Imputation
23 Random Forest NaN 0 Mean Imputation
24 Random Forest NaN False Mean Imputation
25 Random Forest 0.905452 NaN Mean Imputation
26 Random Forest 0.132075 NaN Mean Imputation
27 Random Forest 0.213415 NaN Mean Imputation
28 Random Forest 0.163170 NaN Mean Imputation
29 Random Forest 0.640181 NaN Mean Imputation
30 Random Forest 0.261583 NaN Mean Imputation
31 Random Forest 0.280362 NaN Mean Imputation
32 Random Forest NaN True Mean Imputation
33 Random Forest NaN 0.0 Mean Imputation
34 Random Forest NaN None Mean Imputation
35 Random Forest NaN gini Mean Imputation
36 Random Forest NaN None Mean Imputation
37 Random Forest NaN sqrt Mean Imputation
38 Random Forest NaN None Mean Imputation
39 Random Forest NaN None Mean Imputation
40 Random Forest NaN 0.0 Mean Imputation
41 Random Forest NaN 1 Mean Imputation
42 Random Forest NaN 2 Mean Imputation
43 Random Forest NaN 0.0 Mean Imputation
44 Random Forest NaN 100 Mean Imputation
45 Random Forest NaN None Mean Imputation
46 Random Forest NaN False Mean Imputation
47 Random Forest NaN None Mean Imputation
48 Random Forest NaN 0 Mean Imputation
49 Random Forest NaN False Mean Imputation
50 Random Forest 0.899131 NaN Mean Imputation
51 Random Forest 0.108000 NaN Mean Imputation
52 Random Forest 0.144385 NaN Mean Imputation
53 Random Forest 0.123570 NaN Mean Imputation
54 Random Forest 0.604791 NaN Mean Imputation
55 Random Forest 0.259521 NaN Mean Imputation
56 Random Forest 0.209581 NaN Mean Imputation
57 Random Forest NaN True Mean Imputation
58 Random Forest NaN 0.0 Mean Imputation
59 Random Forest NaN None Mean Imputation
60 Random Forest NaN gini Mean Imputation
61 Random Forest NaN None Mean Imputation
62 Random Forest NaN sqrt Mean Imputation
63 Random Forest NaN None Mean Imputation
64 Random Forest NaN None Mean Imputation
65 Random Forest NaN 0.0 Mean Imputation
66 Random Forest NaN 1 Mean Imputation
67 Random Forest NaN 2 Mean Imputation
68 Random Forest NaN 0.0 Mean Imputation
69 Random Forest NaN 100 Mean Imputation
70 Random Forest NaN None Mean Imputation
71 Random Forest NaN False Mean Imputation
72 Random Forest NaN None Mean Imputation
73 Random Forest NaN 0 Mean Imputation
74 Random Forest NaN False Mean Imputation
75 Random Forest 0.904636 NaN Mean Imputation
76 Random Forest 0.135965 NaN Mean Imputation
77 Random Forest 0.158163 NaN Mean Imputation
78 Random Forest 0.146226 NaN Mean Imputation
79 Random Forest 0.616870 NaN Mean Imputation
80 Random Forest 0.228418 NaN Mean Imputation
81 Random Forest 0.233740 NaN Mean Imputation
82 Random Forest NaN True Mean Imputation
83 Random Forest NaN 0.0 Mean Imputation
84 Random Forest NaN None Mean Imputation
85 Random Forest NaN gini Mean Imputation
86 Random Forest NaN None Mean Imputation
87 Random Forest NaN sqrt Mean Imputation
88 Random Forest NaN None Mean Imputation
89 Random Forest NaN None Mean Imputation
90 Random Forest NaN 0.0 Mean Imputation
91 Random Forest NaN 1 Mean Imputation
92 Random Forest NaN 2 Mean Imputation
93 Random Forest NaN 0.0 Mean Imputation
94 Random Forest NaN 100 Mean Imputation
95 Random Forest NaN None Mean Imputation
96 Random Forest NaN False Mean Imputation
97 Random Forest NaN None Mean Imputation
98 Random Forest NaN 0 Mean Imputation
99 Random Forest NaN False Mean Imputation
100 Random Forest 0.897524 NaN Mean Imputation
101 Random Forest 0.141079 NaN Mean Imputation
102 Random Forest 0.157407 NaN Mean Imputation
103 Random Forest 0.148796 NaN Mean Imputation
104 Random Forest 0.585898 NaN Mean Imputation
105 Random Forest 0.207609 NaN Mean Imputation
106 Random Forest 0.171797 NaN Mean Imputation
107 Random Forest NaN True Mean Imputation
108 Random Forest NaN 0.0 Mean Imputation
109 Random Forest NaN None Mean Imputation
110 Random Forest NaN gini Mean Imputation
111 Random Forest NaN None Mean Imputation
112 Random Forest NaN sqrt Mean Imputation
113 Random Forest NaN None Mean Imputation
114 Random Forest NaN None Mean Imputation
115 Random Forest NaN 0.0 Mean Imputation
116 Random Forest NaN 1 Mean Imputation
117 Random Forest NaN 2 Mean Imputation
118 Random Forest NaN 0.0 Mean Imputation
119 Random Forest NaN 100 Mean Imputation
120 Random Forest NaN None Mean Imputation
121 Random Forest NaN False Mean Imputation
122 Random Forest NaN None Mean Imputation
123 Random Forest NaN 0 Mean Imputation
124 Random Forest NaN False Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
95 Hybrid Sampling (SMOTEENN)
96 Hybrid Sampling (SMOTEENN)
97 Hybrid Sampling (SMOTEENN)
98 Hybrid Sampling (SMOTEENN)
99 Hybrid Sampling (SMOTEENN)
100 Hybrid Sampling (SMOTEENN)
101 Hybrid Sampling (SMOTEENN)
102 Hybrid Sampling (SMOTEENN)
103 Hybrid Sampling (SMOTEENN)
104 Hybrid Sampling (SMOTEENN)
105 Hybrid Sampling (SMOTEENN)
106 Hybrid Sampling (SMOTEENN)
107 Hybrid Sampling (SMOTEENN)
108 Hybrid Sampling (SMOTEENN)
109 Hybrid Sampling (SMOTEENN)
110 Hybrid Sampling (SMOTEENN)
111 Hybrid Sampling (SMOTEENN)
112 Hybrid Sampling (SMOTEENN)
113 Hybrid Sampling (SMOTEENN)
114 Hybrid Sampling (SMOTEENN)
115 Hybrid Sampling (SMOTEENN)
116 Hybrid Sampling (SMOTEENN)
117 Hybrid Sampling (SMOTEENN)
118 Hybrid Sampling (SMOTEENN)
119 Hybrid Sampling (SMOTEENN)
120 Hybrid Sampling (SMOTEENN)
121 Hybrid Sampling (SMOTEENN)
122 Hybrid Sampling (SMOTEENN)
123 Hybrid Sampling (SMOTEENN)
124 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
1 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
2 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
3 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
4 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
5 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
6 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
7 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
8 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
9 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
10 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
11 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
12 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
13 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
14 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
15 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
16 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
17 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
18 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
19 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
20 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
21 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
22 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
23 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
24 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
25 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
26 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
27 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
28 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
29 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
30 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
31 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
32 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
33 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
34 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
35 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
36 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
37 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
38 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
39 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
40 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
41 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
42 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
43 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
44 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
45 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
46 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
47 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
48 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
49 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
50 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
51 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
52 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
53 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
54 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
55 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
56 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
57 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
58 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
59 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
60 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
61 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
62 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
63 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
64 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
65 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
66 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
67 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
68 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
69 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
70 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
71 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
72 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
73 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
74 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
75 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
76 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
77 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
78 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
79 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
80 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
81 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
82 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
83 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
84 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
85 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
86 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
87 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
88 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
89 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
90 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
91 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
92 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
93 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
94 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
95 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
96 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
97 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
98 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
99 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
100 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
101 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
102 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
103 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
104 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
105 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
106 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
107 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
108 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
109 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
110 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
111 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
112 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
113 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
114 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
115 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
116 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
117 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
118 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
119 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
120 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
121 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
122 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
123 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ...
124 Random Forest_Hybrid Sampling (SMOTEENN)_Mean ... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Random Forest 0.899131 NaN Median Imputation
1 Random Forest 0.123711 NaN Median Imputation
2 Random Forest 0.101266 NaN Median Imputation
3 Random Forest 0.111369 NaN Median Imputation
4 Random Forest 0.614159 NaN Median Imputation
5 Random Forest 0.210064 NaN Median Imputation
6 Random Forest 0.228317 NaN Median Imputation
7 Random Forest NaN True Median Imputation
8 Random Forest NaN 0.0 Median Imputation
9 Random Forest NaN None Median Imputation
10 Random Forest NaN gini Median Imputation
11 Random Forest NaN None Median Imputation
12 Random Forest NaN sqrt Median Imputation
13 Random Forest NaN None Median Imputation
14 Random Forest NaN None Median Imputation
15 Random Forest NaN 0.0 Median Imputation
16 Random Forest NaN 1 Median Imputation
17 Random Forest NaN 2 Median Imputation
18 Random Forest NaN 0.0 Median Imputation
19 Random Forest NaN 100 Median Imputation
20 Random Forest NaN None Median Imputation
21 Random Forest NaN False Median Imputation
22 Random Forest NaN None Median Imputation
23 Random Forest NaN 0 Median Imputation
24 Random Forest NaN False Median Imputation
25 Random Forest 0.903871 NaN Median Imputation
26 Random Forest 0.111969 NaN Median Imputation
27 Random Forest 0.176829 NaN Median Imputation
28 Random Forest 0.137116 NaN Median Imputation
29 Random Forest 0.627970 NaN Median Imputation
30 Random Forest 0.237407 NaN Median Imputation
31 Random Forest 0.255940 NaN Median Imputation
32 Random Forest NaN True Median Imputation
33 Random Forest NaN 0.0 Median Imputation
34 Random Forest NaN None Median Imputation
35 Random Forest NaN gini Median Imputation
36 Random Forest NaN None Median Imputation
37 Random Forest NaN sqrt Median Imputation
38 Random Forest NaN None Median Imputation
39 Random Forest NaN None Median Imputation
40 Random Forest NaN 0.0 Median Imputation
41 Random Forest NaN 1 Median Imputation
42 Random Forest NaN 2 Median Imputation
43 Random Forest NaN 0.0 Median Imputation
44 Random Forest NaN 100 Median Imputation
45 Random Forest NaN None Median Imputation
46 Random Forest NaN False Median Imputation
47 Random Forest NaN None Median Imputation
48 Random Forest NaN 0 Median Imputation
49 Random Forest NaN False Median Imputation
50 Random Forest 0.902818 NaN Median Imputation
51 Random Forest 0.107759 NaN Median Imputation
52 Random Forest 0.133690 NaN Median Imputation
53 Random Forest 0.119332 NaN Median Imputation
54 Random Forest 0.597185 NaN Median Imputation
55 Random Forest 0.198168 NaN Median Imputation
56 Random Forest 0.194371 NaN Median Imputation
57 Random Forest NaN True Median Imputation
58 Random Forest NaN 0.0 Median Imputation
59 Random Forest NaN None Median Imputation
60 Random Forest NaN gini Median Imputation
61 Random Forest NaN None Median Imputation
62 Random Forest NaN sqrt Median Imputation
63 Random Forest NaN None Median Imputation
64 Random Forest NaN None Median Imputation
65 Random Forest NaN 0.0 Median Imputation
66 Random Forest NaN 1 Median Imputation
67 Random Forest NaN 2 Median Imputation
68 Random Forest NaN 0.0 Median Imputation
69 Random Forest NaN 100 Median Imputation
70 Random Forest NaN None Median Imputation
71 Random Forest NaN False Median Imputation
72 Random Forest NaN None Median Imputation
73 Random Forest NaN 0 Median Imputation
74 Random Forest NaN False Median Imputation
75 Random Forest 0.896470 NaN Median Imputation
76 Random Forest 0.104418 NaN Median Imputation
77 Random Forest 0.132653 NaN Median Imputation
78 Random Forest 0.116854 NaN Median Imputation
79 Random Forest 0.583388 NaN Median Imputation
80 Random Forest 0.164773 NaN Median Imputation
81 Random Forest 0.166776 NaN Median Imputation
82 Random Forest NaN True Median Imputation
83 Random Forest NaN 0.0 Median Imputation
84 Random Forest NaN None Median Imputation
85 Random Forest NaN gini Median Imputation
86 Random Forest NaN None Median Imputation
87 Random Forest NaN sqrt Median Imputation
88 Random Forest NaN None Median Imputation
89 Random Forest NaN None Median Imputation
90 Random Forest NaN 0.0 Median Imputation
91 Random Forest NaN 1 Median Imputation
92 Random Forest NaN 2 Median Imputation
93 Random Forest NaN 0.0 Median Imputation
94 Random Forest NaN 100 Median Imputation
95 Random Forest NaN None Median Imputation
96 Random Forest NaN False Median Imputation
97 Random Forest NaN None Median Imputation
98 Random Forest NaN 0 Median Imputation
99 Random Forest NaN False Median Imputation
100 Random Forest 0.892518 NaN Median Imputation
101 Random Forest 0.141791 NaN Median Imputation
102 Random Forest 0.175926 NaN Median Imputation
103 Random Forest 0.157025 NaN Median Imputation
104 Random Forest 0.599265 NaN Median Imputation
105 Random Forest 0.229092 NaN Median Imputation
106 Random Forest 0.198530 NaN Median Imputation
107 Random Forest NaN True Median Imputation
108 Random Forest NaN 0.0 Median Imputation
109 Random Forest NaN None Median Imputation
110 Random Forest NaN gini Median Imputation
111 Random Forest NaN None Median Imputation
112 Random Forest NaN sqrt Median Imputation
113 Random Forest NaN None Median Imputation
114 Random Forest NaN None Median Imputation
115 Random Forest NaN 0.0 Median Imputation
116 Random Forest NaN 1 Median Imputation
117 Random Forest NaN 2 Median Imputation
118 Random Forest NaN 0.0 Median Imputation
119 Random Forest NaN 100 Median Imputation
120 Random Forest NaN None Median Imputation
121 Random Forest NaN False Median Imputation
122 Random Forest NaN None Median Imputation
123 Random Forest NaN 0 Median Imputation
124 Random Forest NaN False Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
95 Hybrid Sampling (SMOTEENN)
96 Hybrid Sampling (SMOTEENN)
97 Hybrid Sampling (SMOTEENN)
98 Hybrid Sampling (SMOTEENN)
99 Hybrid Sampling (SMOTEENN)
100 Hybrid Sampling (SMOTEENN)
101 Hybrid Sampling (SMOTEENN)
102 Hybrid Sampling (SMOTEENN)
103 Hybrid Sampling (SMOTEENN)
104 Hybrid Sampling (SMOTEENN)
105 Hybrid Sampling (SMOTEENN)
106 Hybrid Sampling (SMOTEENN)
107 Hybrid Sampling (SMOTEENN)
108 Hybrid Sampling (SMOTEENN)
109 Hybrid Sampling (SMOTEENN)
110 Hybrid Sampling (SMOTEENN)
111 Hybrid Sampling (SMOTEENN)
112 Hybrid Sampling (SMOTEENN)
113 Hybrid Sampling (SMOTEENN)
114 Hybrid Sampling (SMOTEENN)
115 Hybrid Sampling (SMOTEENN)
116 Hybrid Sampling (SMOTEENN)
117 Hybrid Sampling (SMOTEENN)
118 Hybrid Sampling (SMOTEENN)
119 Hybrid Sampling (SMOTEENN)
120 Hybrid Sampling (SMOTEENN)
121 Hybrid Sampling (SMOTEENN)
122 Hybrid Sampling (SMOTEENN)
123 Hybrid Sampling (SMOTEENN)
124 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
1 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
2 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
3 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
4 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
5 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
6 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
7 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
8 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
9 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
10 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
11 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
12 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
13 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
14 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
15 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
16 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
17 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
18 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
19 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
20 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
21 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
22 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
23 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
24 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
25 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
26 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
27 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
28 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
29 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
30 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
31 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
32 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
33 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
34 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
35 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
36 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
37 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
38 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
39 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
40 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
41 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
42 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
43 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
44 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
45 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
46 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
47 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
48 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
49 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
50 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
51 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
52 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
53 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
54 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
55 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
56 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
57 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
58 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
59 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
60 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
61 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
62 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
63 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
64 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
65 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
66 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
67 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
68 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
69 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
70 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
71 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
72 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
73 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
74 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
75 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
76 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
77 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
78 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
79 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
80 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
81 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
82 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
83 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
84 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
85 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
86 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
87 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
88 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
89 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
90 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
91 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
92 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
93 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
94 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
95 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
96 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
97 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
98 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
99 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
100 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
101 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
102 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
103 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
104 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
105 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
106 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
107 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
108 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
109 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
110 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
111 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
112 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
113 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
114 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
115 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
116 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
117 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
118 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
119 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
120 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
121 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
122 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
123 Random Forest_Hybrid Sampling (SMOTEENN)_Media...
124 Random Forest_Hybrid Sampling (SMOTEENN)_Media... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Random Forest 0.903871 NaN Zero Imputation
1 Random Forest 0.132184 NaN Zero Imputation
2 Random Forest 0.097046 NaN Zero Imputation
3 Random Forest 0.111922 NaN Zero Imputation
4 Random Forest 0.638794 NaN Zero Imputation
5 Random Forest 0.228166 NaN Zero Imputation
6 Random Forest 0.277587 NaN Zero Imputation
7 Random Forest NaN True Zero Imputation
8 Random Forest NaN 0.0 Zero Imputation
9 Random Forest NaN None Zero Imputation
10 Random Forest NaN gini Zero Imputation
11 Random Forest NaN None Zero Imputation
12 Random Forest NaN sqrt Zero Imputation
13 Random Forest NaN None Zero Imputation
14 Random Forest NaN None Zero Imputation
15 Random Forest NaN 0.0 Zero Imputation
16 Random Forest NaN 1 Zero Imputation
17 Random Forest NaN 2 Zero Imputation
18 Random Forest NaN 0.0 Zero Imputation
19 Random Forest NaN 100 Zero Imputation
20 Random Forest NaN None Zero Imputation
21 Random Forest NaN False Zero Imputation
22 Random Forest NaN None Zero Imputation
23 Random Forest NaN 0 Zero Imputation
24 Random Forest NaN False Zero Imputation
25 Random Forest 0.920200 NaN Zero Imputation
26 Random Forest 0.116022 NaN Zero Imputation
27 Random Forest 0.128049 NaN Zero Imputation
28 Random Forest 0.121739 NaN Zero Imputation
29 Random Forest 0.662665 NaN Zero Imputation
30 Random Forest 0.280691 NaN Zero Imputation
31 Random Forest 0.325329 NaN Zero Imputation
32 Random Forest NaN True Zero Imputation
33 Random Forest NaN 0.0 Zero Imputation
34 Random Forest NaN None Zero Imputation
35 Random Forest NaN gini Zero Imputation
36 Random Forest NaN None Zero Imputation
37 Random Forest NaN sqrt Zero Imputation
38 Random Forest NaN None Zero Imputation
39 Random Forest NaN None Zero Imputation
40 Random Forest NaN 0.0 Zero Imputation
41 Random Forest NaN 1 Zero Imputation
42 Random Forest NaN 2 Zero Imputation
43 Random Forest NaN 0.0 Zero Imputation
44 Random Forest NaN 100 Zero Imputation
45 Random Forest NaN None Zero Imputation
46 Random Forest NaN False Zero Imputation
47 Random Forest NaN None Zero Imputation
48 Random Forest NaN 0 Zero Imputation
49 Random Forest NaN False Zero Imputation
50 Random Forest 0.909929 NaN Zero Imputation
51 Random Forest 0.132701 NaN Zero Imputation
52 Random Forest 0.149733 NaN Zero Imputation
53 Random Forest 0.140704 NaN Zero Imputation
54 Random Forest 0.668543 NaN Zero Imputation
55 Random Forest 0.293364 NaN Zero Imputation
56 Random Forest 0.337085 NaN Zero Imputation
57 Random Forest NaN True Zero Imputation
58 Random Forest NaN 0.0 Zero Imputation
59 Random Forest NaN None Zero Imputation
60 Random Forest NaN gini Zero Imputation
61 Random Forest NaN None Zero Imputation
62 Random Forest NaN sqrt Zero Imputation
63 Random Forest NaN None Zero Imputation
64 Random Forest NaN None Zero Imputation
65 Random Forest NaN 0.0 Zero Imputation
66 Random Forest NaN 1 Zero Imputation
67 Random Forest NaN 2 Zero Imputation
68 Random Forest NaN 0.0 Zero Imputation
69 Random Forest NaN 100 Zero Imputation
70 Random Forest NaN None Zero Imputation
71 Random Forest NaN False Zero Imputation
72 Random Forest NaN None Zero Imputation
73 Random Forest NaN 0 Zero Imputation
74 Random Forest NaN False Zero Imputation
75 Random Forest 0.909115 NaN Zero Imputation
76 Random Forest 0.109948 NaN Zero Imputation
77 Random Forest 0.107143 NaN Zero Imputation
78 Random Forest 0.108527 NaN Zero Imputation
79 Random Forest 0.608021 NaN Zero Imputation
80 Random Forest 0.201372 NaN Zero Imputation
81 Random Forest 0.216042 NaN Zero Imputation
82 Random Forest NaN True Zero Imputation
83 Random Forest NaN 0.0 Zero Imputation
84 Random Forest NaN None Zero Imputation
85 Random Forest NaN gini Zero Imputation
86 Random Forest NaN None Zero Imputation
87 Random Forest NaN sqrt Zero Imputation
88 Random Forest NaN None Zero Imputation
89 Random Forest NaN None Zero Imputation
90 Random Forest NaN 0.0 Zero Imputation
91 Random Forest NaN 1 Zero Imputation
92 Random Forest NaN 2 Zero Imputation
93 Random Forest NaN 0.0 Zero Imputation
94 Random Forest NaN 100 Zero Imputation
95 Random Forest NaN None Zero Imputation
96 Random Forest NaN False Zero Imputation
97 Random Forest NaN None Zero Imputation
98 Random Forest NaN 0 Zero Imputation
99 Random Forest NaN False Zero Imputation
100 Random Forest 0.906744 NaN Zero Imputation
101 Random Forest 0.132979 NaN Zero Imputation
102 Random Forest 0.115741 NaN Zero Imputation
103 Random Forest 0.123762 NaN Zero Imputation
104 Random Forest 0.610973 NaN Zero Imputation
105 Random Forest 0.225455 NaN Zero Imputation
106 Random Forest 0.221945 NaN Zero Imputation
107 Random Forest NaN True Zero Imputation
108 Random Forest NaN 0.0 Zero Imputation
109 Random Forest NaN None Zero Imputation
110 Random Forest NaN gini Zero Imputation
111 Random Forest NaN None Zero Imputation
112 Random Forest NaN sqrt Zero Imputation
113 Random Forest NaN None Zero Imputation
114 Random Forest NaN None Zero Imputation
115 Random Forest NaN 0.0 Zero Imputation
116 Random Forest NaN 1 Zero Imputation
117 Random Forest NaN 2 Zero Imputation
118 Random Forest NaN 0.0 Zero Imputation
119 Random Forest NaN 100 Zero Imputation
120 Random Forest NaN None Zero Imputation
121 Random Forest NaN False Zero Imputation
122 Random Forest NaN None Zero Imputation
123 Random Forest NaN 0 Zero Imputation
124 Random Forest NaN False Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
95 Hybrid Sampling (SMOTETomek)
96 Hybrid Sampling (SMOTETomek)
97 Hybrid Sampling (SMOTETomek)
98 Hybrid Sampling (SMOTETomek)
99 Hybrid Sampling (SMOTETomek)
100 Hybrid Sampling (SMOTETomek)
101 Hybrid Sampling (SMOTETomek)
102 Hybrid Sampling (SMOTETomek)
103 Hybrid Sampling (SMOTETomek)
104 Hybrid Sampling (SMOTETomek)
105 Hybrid Sampling (SMOTETomek)
106 Hybrid Sampling (SMOTETomek)
107 Hybrid Sampling (SMOTETomek)
108 Hybrid Sampling (SMOTETomek)
109 Hybrid Sampling (SMOTETomek)
110 Hybrid Sampling (SMOTETomek)
111 Hybrid Sampling (SMOTETomek)
112 Hybrid Sampling (SMOTETomek)
113 Hybrid Sampling (SMOTETomek)
114 Hybrid Sampling (SMOTETomek)
115 Hybrid Sampling (SMOTETomek)
116 Hybrid Sampling (SMOTETomek)
117 Hybrid Sampling (SMOTETomek)
118 Hybrid Sampling (SMOTETomek)
119 Hybrid Sampling (SMOTETomek)
120 Hybrid Sampling (SMOTETomek)
121 Hybrid Sampling (SMOTETomek)
122 Hybrid Sampling (SMOTETomek)
123 Hybrid Sampling (SMOTETomek)
124 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
1 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
2 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
3 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
4 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
5 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
6 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
7 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
8 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
9 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
10 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
11 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
12 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
13 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
14 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
15 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
16 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
17 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
18 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
19 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
20 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
21 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
22 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
23 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
24 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
25 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
26 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
27 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
28 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
29 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
30 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
31 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
32 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
33 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
34 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
35 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
36 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
37 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
38 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
39 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
40 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
41 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
42 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
43 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
44 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
45 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
46 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
47 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
48 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
49 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
50 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
51 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
52 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
53 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
54 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
55 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
56 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
57 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
58 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
59 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
60 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
61 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
62 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
63 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
64 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
65 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
66 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
67 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
68 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
69 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
70 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
71 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
72 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
73 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
74 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
75 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
76 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
77 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
78 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
79 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
80 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
81 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
82 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
83 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
84 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
85 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
86 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
87 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
88 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
89 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
90 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
91 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
92 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
93 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
94 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
95 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
96 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
97 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
98 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
99 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
100 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
101 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
102 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
103 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
104 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
105 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
106 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
107 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
108 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
109 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
110 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
111 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
112 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
113 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
114 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
115 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
116 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
117 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
118 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
119 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
120 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
121 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
122 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
123 Random Forest_Hybrid Sampling (SMOTETomek)_Zer...
124 Random Forest_Hybrid Sampling (SMOTETomek)_Zer... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Random Forest 0.904662 NaN Mean Imputation
1 Random Forest 0.121212 NaN Mean Imputation
2 Random Forest 0.084388 NaN Mean Imputation
3 Random Forest 0.099502 NaN Mean Imputation
4 Random Forest 0.631506 NaN Mean Imputation
5 Random Forest 0.233609 NaN Mean Imputation
6 Random Forest 0.263011 NaN Mean Imputation
7 Random Forest NaN True Mean Imputation
8 Random Forest NaN 0.0 Mean Imputation
9 Random Forest NaN None Mean Imputation
10 Random Forest NaN gini Mean Imputation
11 Random Forest NaN None Mean Imputation
12 Random Forest NaN sqrt Mean Imputation
13 Random Forest NaN None Mean Imputation
14 Random Forest NaN None Mean Imputation
15 Random Forest NaN 0.0 Mean Imputation
16 Random Forest NaN 1 Mean Imputation
17 Random Forest NaN 2 Mean Imputation
18 Random Forest NaN 0.0 Mean Imputation
19 Random Forest NaN 100 Mean Imputation
20 Random Forest NaN None Mean Imputation
21 Random Forest NaN False Mean Imputation
22 Random Forest NaN None Mean Imputation
23 Random Forest NaN 0 Mean Imputation
24 Random Forest NaN False Mean Imputation
25 Random Forest 0.920727 NaN Mean Imputation
26 Random Forest 0.125683 NaN Mean Imputation
27 Random Forest 0.140244 NaN Mean Imputation
28 Random Forest 0.132565 NaN Mean Imputation
29 Random Forest 0.654877 NaN Mean Imputation
30 Random Forest 0.283868 NaN Mean Imputation
31 Random Forest 0.309754 NaN Mean Imputation
32 Random Forest NaN True Mean Imputation
33 Random Forest NaN 0.0 Mean Imputation
34 Random Forest NaN None Mean Imputation
35 Random Forest NaN gini Mean Imputation
36 Random Forest NaN None Mean Imputation
37 Random Forest NaN sqrt Mean Imputation
38 Random Forest NaN None Mean Imputation
39 Random Forest NaN None Mean Imputation
40 Random Forest NaN 0.0 Mean Imputation
41 Random Forest NaN 1 Mean Imputation
42 Random Forest NaN 2 Mean Imputation
43 Random Forest NaN 0.0 Mean Imputation
44 Random Forest NaN 100 Mean Imputation
45 Random Forest NaN None Mean Imputation
46 Random Forest NaN False Mean Imputation
47 Random Forest NaN None Mean Imputation
48 Random Forest NaN 0 Mean Imputation
49 Random Forest NaN False Mean Imputation
50 Random Forest 0.915723 NaN Mean Imputation
51 Random Forest 0.144385 NaN Mean Imputation
52 Random Forest 0.144385 NaN Mean Imputation
53 Random Forest 0.144385 NaN Mean Imputation
54 Random Forest 0.667364 NaN Mean Imputation
55 Random Forest 0.279189 NaN Mean Imputation
56 Random Forest 0.334728 NaN Mean Imputation
57 Random Forest NaN True Mean Imputation
58 Random Forest NaN 0.0 Mean Imputation
59 Random Forest NaN None Mean Imputation
60 Random Forest NaN gini Mean Imputation
61 Random Forest NaN None Mean Imputation
62 Random Forest NaN sqrt Mean Imputation
63 Random Forest NaN None Mean Imputation
64 Random Forest NaN None Mean Imputation
65 Random Forest NaN 0.0 Mean Imputation
66 Random Forest NaN 1 Mean Imputation
67 Random Forest NaN 2 Mean Imputation
68 Random Forest NaN 0.0 Mean Imputation
69 Random Forest NaN 100 Mean Imputation
70 Random Forest NaN None Mean Imputation
71 Random Forest NaN False Mean Imputation
72 Random Forest NaN None Mean Imputation
73 Random Forest NaN 0 Mean Imputation
74 Random Forest NaN False Mean Imputation
75 Random Forest 0.912276 NaN Mean Imputation
76 Random Forest 0.141361 NaN Mean Imputation
77 Random Forest 0.137755 NaN Mean Imputation
78 Random Forest 0.139535 NaN Mean Imputation
79 Random Forest 0.621750 NaN Mean Imputation
80 Random Forest 0.218044 NaN Mean Imputation
81 Random Forest 0.243499 NaN Mean Imputation
82 Random Forest NaN True Mean Imputation
83 Random Forest NaN 0.0 Mean Imputation
84 Random Forest NaN None Mean Imputation
85 Random Forest NaN gini Mean Imputation
86 Random Forest NaN None Mean Imputation
87 Random Forest NaN sqrt Mean Imputation
88 Random Forest NaN None Mean Imputation
89 Random Forest NaN None Mean Imputation
90 Random Forest NaN 0.0 Mean Imputation
91 Random Forest NaN 1 Mean Imputation
92 Random Forest NaN 2 Mean Imputation
93 Random Forest NaN 0.0 Mean Imputation
94 Random Forest NaN 100 Mean Imputation
95 Random Forest NaN None Mean Imputation
96 Random Forest NaN False Mean Imputation
97 Random Forest NaN None Mean Imputation
98 Random Forest NaN 0 Mean Imputation
99 Random Forest NaN False Mean Imputation
100 Random Forest 0.908325 NaN Mean Imputation
101 Random Forest 0.159794 NaN Mean Imputation
102 Random Forest 0.143519 NaN Mean Imputation
103 Random Forest 0.151220 NaN Mean Imputation
104 Random Forest 0.608478 NaN Mean Imputation
105 Random Forest 0.186251 NaN Mean Imputation
106 Random Forest 0.216956 NaN Mean Imputation
107 Random Forest NaN True Mean Imputation
108 Random Forest NaN 0.0 Mean Imputation
109 Random Forest NaN None Mean Imputation
110 Random Forest NaN gini Mean Imputation
111 Random Forest NaN None Mean Imputation
112 Random Forest NaN sqrt Mean Imputation
113 Random Forest NaN None Mean Imputation
114 Random Forest NaN None Mean Imputation
115 Random Forest NaN 0.0 Mean Imputation
116 Random Forest NaN 1 Mean Imputation
117 Random Forest NaN 2 Mean Imputation
118 Random Forest NaN 0.0 Mean Imputation
119 Random Forest NaN 100 Mean Imputation
120 Random Forest NaN None Mean Imputation
121 Random Forest NaN False Mean Imputation
122 Random Forest NaN None Mean Imputation
123 Random Forest NaN 0 Mean Imputation
124 Random Forest NaN False Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
95 Hybrid Sampling (SMOTETomek)
96 Hybrid Sampling (SMOTETomek)
97 Hybrid Sampling (SMOTETomek)
98 Hybrid Sampling (SMOTETomek)
99 Hybrid Sampling (SMOTETomek)
100 Hybrid Sampling (SMOTETomek)
101 Hybrid Sampling (SMOTETomek)
102 Hybrid Sampling (SMOTETomek)
103 Hybrid Sampling (SMOTETomek)
104 Hybrid Sampling (SMOTETomek)
105 Hybrid Sampling (SMOTETomek)
106 Hybrid Sampling (SMOTETomek)
107 Hybrid Sampling (SMOTETomek)
108 Hybrid Sampling (SMOTETomek)
109 Hybrid Sampling (SMOTETomek)
110 Hybrid Sampling (SMOTETomek)
111 Hybrid Sampling (SMOTETomek)
112 Hybrid Sampling (SMOTETomek)
113 Hybrid Sampling (SMOTETomek)
114 Hybrid Sampling (SMOTETomek)
115 Hybrid Sampling (SMOTETomek)
116 Hybrid Sampling (SMOTETomek)
117 Hybrid Sampling (SMOTETomek)
118 Hybrid Sampling (SMOTETomek)
119 Hybrid Sampling (SMOTETomek)
120 Hybrid Sampling (SMOTETomek)
121 Hybrid Sampling (SMOTETomek)
122 Hybrid Sampling (SMOTETomek)
123 Hybrid Sampling (SMOTETomek)
124 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
1 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
2 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
3 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
4 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
5 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
6 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
7 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
8 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
9 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
10 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
11 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
12 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
13 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
14 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
15 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
16 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
17 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
18 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
19 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
20 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
21 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
22 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
23 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
24 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
25 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
26 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
27 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
28 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
29 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
30 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
31 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
32 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
33 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
34 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
35 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
36 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
37 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
38 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
39 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
40 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
41 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
42 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
43 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
44 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
45 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
46 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
47 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
48 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
49 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
50 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
51 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
52 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
53 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
54 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
55 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
56 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
57 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
58 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
59 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
60 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
61 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
62 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
63 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
64 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
65 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
66 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
67 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
68 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
69 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
70 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
71 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
72 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
73 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
74 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
75 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
76 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
77 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
78 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
79 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
80 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
81 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
82 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
83 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
84 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
85 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
86 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
87 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
88 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
89 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
90 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
91 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
92 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
93 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
94 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
95 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
96 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
97 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
98 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
99 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
100 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
101 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
102 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
103 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
104 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
105 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
106 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
107 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
108 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
109 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
110 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
111 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
112 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
113 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
114 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
115 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
116 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
117 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
118 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
119 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
120 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
121 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
122 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
123 Random Forest_Hybrid Sampling (SMOTETomek)_Mea...
124 Random Forest_Hybrid Sampling (SMOTETomek)_Mea... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 Random Forest 0.903608 NaN Median Imputation
1 Random Forest 0.109091 NaN Median Imputation
2 Random Forest 0.075949 NaN Median Imputation
3 Random Forest 0.089552 NaN Median Imputation
4 Random Forest 0.616043 NaN Median Imputation
5 Random Forest 0.187701 NaN Median Imputation
6 Random Forest 0.232086 NaN Median Imputation
7 Random Forest NaN True Median Imputation
8 Random Forest NaN 0.0 Median Imputation
9 Random Forest NaN None Median Imputation
10 Random Forest NaN gini Median Imputation
11 Random Forest NaN None Median Imputation
12 Random Forest NaN sqrt Median Imputation
13 Random Forest NaN None Median Imputation
14 Random Forest NaN None Median Imputation
15 Random Forest NaN 0.0 Median Imputation
16 Random Forest NaN 1 Median Imputation
17 Random Forest NaN 2 Median Imputation
18 Random Forest NaN 0.0 Median Imputation
19 Random Forest NaN 100 Median Imputation
20 Random Forest NaN None Median Imputation
21 Random Forest NaN False Median Imputation
22 Random Forest NaN None Median Imputation
23 Random Forest NaN 0 Median Imputation
24 Random Forest NaN False Median Imputation
25 Random Forest 0.916776 NaN Median Imputation
26 Random Forest 0.108247 NaN Median Imputation
27 Random Forest 0.128049 NaN Median Imputation
28 Random Forest 0.117318 NaN Median Imputation
29 Random Forest 0.644780 NaN Median Imputation
30 Random Forest 0.230455 NaN Median Imputation
31 Random Forest 0.289559 NaN Median Imputation
32 Random Forest NaN True Median Imputation
33 Random Forest NaN 0.0 Median Imputation
34 Random Forest NaN None Median Imputation
35 Random Forest NaN gini Median Imputation
36 Random Forest NaN None Median Imputation
37 Random Forest NaN sqrt Median Imputation
38 Random Forest NaN None Median Imputation
39 Random Forest NaN None Median Imputation
40 Random Forest NaN 0.0 Median Imputation
41 Random Forest NaN 1 Median Imputation
42 Random Forest NaN 2 Median Imputation
43 Random Forest NaN 0.0 Median Imputation
44 Random Forest NaN 100 Median Imputation
45 Random Forest NaN None Median Imputation
46 Random Forest NaN False Median Imputation
47 Random Forest NaN None Median Imputation
48 Random Forest NaN 0 Median Imputation
49 Random Forest NaN False Median Imputation
50 Random Forest 0.916250 NaN Median Imputation
51 Random Forest 0.149733 NaN Median Imputation
52 Random Forest 0.149733 NaN Median Imputation
53 Random Forest 0.149733 NaN Median Imputation
54 Random Forest 0.655823 NaN Median Imputation
55 Random Forest 0.281935 NaN Median Imputation
56 Random Forest 0.311646 NaN Median Imputation
57 Random Forest NaN True Median Imputation
58 Random Forest NaN 0.0 Median Imputation
59 Random Forest NaN None Median Imputation
60 Random Forest NaN gini Median Imputation
61 Random Forest NaN None Median Imputation
62 Random Forest NaN sqrt Median Imputation
63 Random Forest NaN None Median Imputation
64 Random Forest NaN None Median Imputation
65 Random Forest NaN 0.0 Median Imputation
66 Random Forest NaN 1 Median Imputation
67 Random Forest NaN 2 Median Imputation
68 Random Forest NaN 0.0 Median Imputation
69 Random Forest NaN 100 Median Imputation
70 Random Forest NaN None Median Imputation
71 Random Forest NaN False Median Imputation
72 Random Forest NaN None Median Imputation
73 Random Forest NaN 0 Median Imputation
74 Random Forest NaN False Median Imputation
75 Random Forest 0.913330 NaN Median Imputation
76 Random Forest 0.144385 NaN Median Imputation
77 Random Forest 0.137755 NaN Median Imputation
78 Random Forest 0.140992 NaN Median Imputation
79 Random Forest 0.597423 NaN Median Imputation
80 Random Forest 0.200675 NaN Median Imputation
81 Random Forest 0.194846 NaN Median Imputation
82 Random Forest NaN True Median Imputation
83 Random Forest NaN 0.0 Median Imputation
84 Random Forest NaN None Median Imputation
85 Random Forest NaN gini Median Imputation
86 Random Forest NaN None Median Imputation
87 Random Forest NaN sqrt Median Imputation
88 Random Forest NaN None Median Imputation
89 Random Forest NaN None Median Imputation
90 Random Forest NaN 0.0 Median Imputation
91 Random Forest NaN 1 Median Imputation
92 Random Forest NaN 2 Median Imputation
93 Random Forest NaN 0.0 Median Imputation
94 Random Forest NaN 100 Median Imputation
95 Random Forest NaN None Median Imputation
96 Random Forest NaN False Median Imputation
97 Random Forest NaN None Median Imputation
98 Random Forest NaN 0 Median Imputation
99 Random Forest NaN False Median Imputation
100 Random Forest 0.904900 NaN Median Imputation
101 Random Forest 0.146341 NaN Median Imputation
102 Random Forest 0.138889 NaN Median Imputation
103 Random Forest 0.142518 NaN Median Imputation
104 Random Forest 0.619271 NaN Median Imputation
105 Random Forest 0.211799 NaN Median Imputation
106 Random Forest 0.238541 NaN Median Imputation
107 Random Forest NaN True Median Imputation
108 Random Forest NaN 0.0 Median Imputation
109 Random Forest NaN None Median Imputation
110 Random Forest NaN gini Median Imputation
111 Random Forest NaN None Median Imputation
112 Random Forest NaN sqrt Median Imputation
113 Random Forest NaN None Median Imputation
114 Random Forest NaN None Median Imputation
115 Random Forest NaN 0.0 Median Imputation
116 Random Forest NaN 1 Median Imputation
117 Random Forest NaN 2 Median Imputation
118 Random Forest NaN 0.0 Median Imputation
119 Random Forest NaN 100 Median Imputation
120 Random Forest NaN None Median Imputation
121 Random Forest NaN False Median Imputation
122 Random Forest NaN None Median Imputation
123 Random Forest NaN 0 Median Imputation
124 Random Forest NaN False Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
95 Hybrid Sampling (SMOTETomek)
96 Hybrid Sampling (SMOTETomek)
97 Hybrid Sampling (SMOTETomek)
98 Hybrid Sampling (SMOTETomek)
99 Hybrid Sampling (SMOTETomek)
100 Hybrid Sampling (SMOTETomek)
101 Hybrid Sampling (SMOTETomek)
102 Hybrid Sampling (SMOTETomek)
103 Hybrid Sampling (SMOTETomek)
104 Hybrid Sampling (SMOTETomek)
105 Hybrid Sampling (SMOTETomek)
106 Hybrid Sampling (SMOTETomek)
107 Hybrid Sampling (SMOTETomek)
108 Hybrid Sampling (SMOTETomek)
109 Hybrid Sampling (SMOTETomek)
110 Hybrid Sampling (SMOTETomek)
111 Hybrid Sampling (SMOTETomek)
112 Hybrid Sampling (SMOTETomek)
113 Hybrid Sampling (SMOTETomek)
114 Hybrid Sampling (SMOTETomek)
115 Hybrid Sampling (SMOTETomek)
116 Hybrid Sampling (SMOTETomek)
117 Hybrid Sampling (SMOTETomek)
118 Hybrid Sampling (SMOTETomek)
119 Hybrid Sampling (SMOTETomek)
120 Hybrid Sampling (SMOTETomek)
121 Hybrid Sampling (SMOTETomek)
122 Hybrid Sampling (SMOTETomek)
123 Hybrid Sampling (SMOTETomek)
124 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
1 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
2 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
3 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
4 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
5 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
6 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
7 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
8 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
9 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
10 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
11 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
12 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
13 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
14 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
15 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
16 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
17 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
18 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
19 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
20 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
21 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
22 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
23 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
24 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
25 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
26 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
27 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
28 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
29 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
30 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
31 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
32 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
33 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
34 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
35 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
36 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
37 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
38 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
39 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
40 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
41 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
42 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
43 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
44 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
45 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
46 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
47 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
48 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
49 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
50 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
51 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
52 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
53 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
54 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
55 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
56 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
57 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
58 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
59 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
60 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
61 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
62 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
63 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
64 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
65 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
66 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
67 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
68 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
69 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
70 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
71 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
72 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
73 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
74 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
75 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
76 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
77 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
78 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
79 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
80 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
81 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
82 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
83 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
84 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
85 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
86 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
87 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
88 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
89 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
90 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
91 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
92 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
93 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
94 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
95 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
96 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
97 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
98 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
99 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
100 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
101 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
102 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
103 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
104 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
105 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
106 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
107 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
108 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
109 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
110 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
111 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
112 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
113 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
114 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
115 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
116 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
117 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
118 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
119 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
120 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
121 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
122 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
123 Random Forest_Hybrid Sampling (SMOTETomek)_Med...
124 Random Forest_Hybrid Sampling (SMOTETomek)_Med... ,
Algorithm Metrics Hyperparameters \
0 Gradient Boosting Machines (GBM) 0.710034 NaN
1 Gradient Boosting Machines (GBM) 0.116341 NaN
2 Gradient Boosting Machines (GBM) 0.552743 NaN
3 Gradient Boosting Machines (GBM) 0.192223 NaN
4 Gradient Boosting Machines (GBM) 0.692938 NaN
5 Gradient Boosting Machines (GBM) 0.307039 NaN
6 Gradient Boosting Machines (GBM) 0.385876 NaN
7 Gradient Boosting Machines (GBM) NaN 0.0
8 Gradient Boosting Machines (GBM) NaN friedman_mse
9 Gradient Boosting Machines (GBM) NaN None
10 Gradient Boosting Machines (GBM) NaN 0.1
11 Gradient Boosting Machines (GBM) NaN log_loss
12 Gradient Boosting Machines (GBM) NaN 3
13 Gradient Boosting Machines (GBM) NaN None
14 Gradient Boosting Machines (GBM) NaN None
15 Gradient Boosting Machines (GBM) NaN 0.0
16 Gradient Boosting Machines (GBM) NaN 1
17 Gradient Boosting Machines (GBM) NaN 2
18 Gradient Boosting Machines (GBM) NaN 0.0
19 Gradient Boosting Machines (GBM) NaN 100
20 Gradient Boosting Machines (GBM) NaN None
21 Gradient Boosting Machines (GBM) NaN None
22 Gradient Boosting Machines (GBM) NaN 1.0
23 Gradient Boosting Machines (GBM) NaN 0.0001
24 Gradient Boosting Machines (GBM) NaN 0.1
25 Gradient Boosting Machines (GBM) NaN 0
26 Gradient Boosting Machines (GBM) NaN False
27 Gradient Boosting Machines (GBM) 0.706084 NaN
28 Gradient Boosting Machines (GBM) 0.082456 NaN
29 Gradient Boosting Machines (GBM) 0.573171 NaN
30 Gradient Boosting Machines (GBM) 0.144172 NaN
31 Gradient Boosting Machines (GBM) 0.713509 NaN
32 Gradient Boosting Machines (GBM) 0.339367 NaN
33 Gradient Boosting Machines (GBM) 0.427019 NaN
34 Gradient Boosting Machines (GBM) NaN 0.0
35 Gradient Boosting Machines (GBM) NaN friedman_mse
36 Gradient Boosting Machines (GBM) NaN None
37 Gradient Boosting Machines (GBM) NaN 0.1
38 Gradient Boosting Machines (GBM) NaN log_loss
39 Gradient Boosting Machines (GBM) NaN 3
40 Gradient Boosting Machines (GBM) NaN None
41 Gradient Boosting Machines (GBM) NaN None
42 Gradient Boosting Machines (GBM) NaN 0.0
43 Gradient Boosting Machines (GBM) NaN 1
44 Gradient Boosting Machines (GBM) NaN 2
45 Gradient Boosting Machines (GBM) NaN 0.0
46 Gradient Boosting Machines (GBM) NaN 100
47 Gradient Boosting Machines (GBM) NaN None
48 Gradient Boosting Machines (GBM) NaN None
49 Gradient Boosting Machines (GBM) NaN 1.0
50 Gradient Boosting Machines (GBM) NaN 0.0001
51 Gradient Boosting Machines (GBM) NaN 0.1
52 Gradient Boosting Machines (GBM) NaN 0
53 Gradient Boosting Machines (GBM) NaN False
54 Gradient Boosting Machines (GBM) 0.681064 NaN
55 Gradient Boosting Machines (GBM) 0.089744 NaN
56 Gradient Boosting Machines (GBM) 0.598930 NaN
57 Gradient Boosting Machines (GBM) 0.156098 NaN
58 Gradient Boosting Machines (GBM) 0.695709 NaN
59 Gradient Boosting Machines (GBM) 0.299498 NaN
60 Gradient Boosting Machines (GBM) 0.391419 NaN
61 Gradient Boosting Machines (GBM) NaN 0.0
62 Gradient Boosting Machines (GBM) NaN friedman_mse
63 Gradient Boosting Machines (GBM) NaN None
64 Gradient Boosting Machines (GBM) NaN 0.1
65 Gradient Boosting Machines (GBM) NaN log_loss
66 Gradient Boosting Machines (GBM) NaN 3
67 Gradient Boosting Machines (GBM) NaN None
68 Gradient Boosting Machines (GBM) NaN None
69 Gradient Boosting Machines (GBM) NaN 0.0
70 Gradient Boosting Machines (GBM) NaN 1
71 Gradient Boosting Machines (GBM) NaN 2
72 Gradient Boosting Machines (GBM) NaN 0.0
73 Gradient Boosting Machines (GBM) NaN 100
74 Gradient Boosting Machines (GBM) NaN None
75 Gradient Boosting Machines (GBM) NaN None
76 Gradient Boosting Machines (GBM) NaN 1.0
77 Gradient Boosting Machines (GBM) NaN 0.0001
78 Gradient Boosting Machines (GBM) NaN 0.1
79 Gradient Boosting Machines (GBM) NaN 0
80 Gradient Boosting Machines (GBM) NaN False
81 Gradient Boosting Machines (GBM) 0.685985 NaN
82 Gradient Boosting Machines (GBM) 0.095779 NaN
83 Gradient Boosting Machines (GBM) 0.602041 NaN
84 Gradient Boosting Machines (GBM) 0.165266 NaN
85 Gradient Boosting Machines (GBM) 0.694953 NaN
86 Gradient Boosting Machines (GBM) 0.310408 NaN
87 Gradient Boosting Machines (GBM) 0.389906 NaN
88 Gradient Boosting Machines (GBM) NaN 0.0
89 Gradient Boosting Machines (GBM) NaN friedman_mse
90 Gradient Boosting Machines (GBM) NaN None
91 Gradient Boosting Machines (GBM) NaN 0.1
92 Gradient Boosting Machines (GBM) NaN log_loss
93 Gradient Boosting Machines (GBM) NaN 3
94 Gradient Boosting Machines (GBM) NaN None
95 Gradient Boosting Machines (GBM) NaN None
96 Gradient Boosting Machines (GBM) NaN 0.0
97 Gradient Boosting Machines (GBM) NaN 1
98 Gradient Boosting Machines (GBM) NaN 2
99 Gradient Boosting Machines (GBM) NaN 0.0
100 Gradient Boosting Machines (GBM) NaN 100
101 Gradient Boosting Machines (GBM) NaN None
102 Gradient Boosting Machines (GBM) NaN None
103 Gradient Boosting Machines (GBM) NaN 1.0
104 Gradient Boosting Machines (GBM) NaN 0.0001
105 Gradient Boosting Machines (GBM) NaN 0.1
106 Gradient Boosting Machines (GBM) NaN 0
107 Gradient Boosting Machines (GBM) NaN False
108 Gradient Boosting Machines (GBM) 0.713383 NaN
109 Gradient Boosting Machines (GBM) 0.119546 NaN
110 Gradient Boosting Machines (GBM) 0.634259 NaN
111 Gradient Boosting Machines (GBM) 0.201175 NaN
112 Gradient Boosting Machines (GBM) 0.722514 NaN
113 Gradient Boosting Machines (GBM) 0.362435 NaN
114 Gradient Boosting Machines (GBM) 0.445029 NaN
115 Gradient Boosting Machines (GBM) NaN 0.0
116 Gradient Boosting Machines (GBM) NaN friedman_mse
117 Gradient Boosting Machines (GBM) NaN None
118 Gradient Boosting Machines (GBM) NaN 0.1
119 Gradient Boosting Machines (GBM) NaN log_loss
120 Gradient Boosting Machines (GBM) NaN 3
121 Gradient Boosting Machines (GBM) NaN None
122 Gradient Boosting Machines (GBM) NaN None
123 Gradient Boosting Machines (GBM) NaN 0.0
124 Gradient Boosting Machines (GBM) NaN 1
125 Gradient Boosting Machines (GBM) NaN 2
126 Gradient Boosting Machines (GBM) NaN 0.0
127 Gradient Boosting Machines (GBM) NaN 100
128 Gradient Boosting Machines (GBM) NaN None
129 Gradient Boosting Machines (GBM) NaN None
130 Gradient Boosting Machines (GBM) NaN 1.0
131 Gradient Boosting Machines (GBM) NaN 0.0001
132 Gradient Boosting Machines (GBM) NaN 0.1
133 Gradient Boosting Machines (GBM) NaN 0
134 Gradient Boosting Machines (GBM) NaN False
Imputation Technique Imbalance Class Technique \
0 Zero Imputation Oversampling
1 Zero Imputation Oversampling
2 Zero Imputation Oversampling
3 Zero Imputation Oversampling
4 Zero Imputation Oversampling
5 Zero Imputation Oversampling
6 Zero Imputation Oversampling
7 Zero Imputation Oversampling
8 Zero Imputation Oversampling
9 Zero Imputation Oversampling
10 Zero Imputation Oversampling
11 Zero Imputation Oversampling
12 Zero Imputation Oversampling
13 Zero Imputation Oversampling
14 Zero Imputation Oversampling
15 Zero Imputation Oversampling
16 Zero Imputation Oversampling
17 Zero Imputation Oversampling
18 Zero Imputation Oversampling
19 Zero Imputation Oversampling
20 Zero Imputation Oversampling
21 Zero Imputation Oversampling
22 Zero Imputation Oversampling
23 Zero Imputation Oversampling
24 Zero Imputation Oversampling
25 Zero Imputation Oversampling
26 Zero Imputation Oversampling
27 Zero Imputation Oversampling
28 Zero Imputation Oversampling
29 Zero Imputation Oversampling
30 Zero Imputation Oversampling
31 Zero Imputation Oversampling
32 Zero Imputation Oversampling
33 Zero Imputation Oversampling
34 Zero Imputation Oversampling
35 Zero Imputation Oversampling
36 Zero Imputation Oversampling
37 Zero Imputation Oversampling
38 Zero Imputation Oversampling
39 Zero Imputation Oversampling
40 Zero Imputation Oversampling
41 Zero Imputation Oversampling
42 Zero Imputation Oversampling
43 Zero Imputation Oversampling
44 Zero Imputation Oversampling
45 Zero Imputation Oversampling
46 Zero Imputation Oversampling
47 Zero Imputation Oversampling
48 Zero Imputation Oversampling
49 Zero Imputation Oversampling
50 Zero Imputation Oversampling
51 Zero Imputation Oversampling
52 Zero Imputation Oversampling
53 Zero Imputation Oversampling
54 Zero Imputation Oversampling
55 Zero Imputation Oversampling
56 Zero Imputation Oversampling
57 Zero Imputation Oversampling
58 Zero Imputation Oversampling
59 Zero Imputation Oversampling
60 Zero Imputation Oversampling
61 Zero Imputation Oversampling
62 Zero Imputation Oversampling
63 Zero Imputation Oversampling
64 Zero Imputation Oversampling
65 Zero Imputation Oversampling
66 Zero Imputation Oversampling
67 Zero Imputation Oversampling
68 Zero Imputation Oversampling
69 Zero Imputation Oversampling
70 Zero Imputation Oversampling
71 Zero Imputation Oversampling
72 Zero Imputation Oversampling
73 Zero Imputation Oversampling
74 Zero Imputation Oversampling
75 Zero Imputation Oversampling
76 Zero Imputation Oversampling
77 Zero Imputation Oversampling
78 Zero Imputation Oversampling
79 Zero Imputation Oversampling
80 Zero Imputation Oversampling
81 Zero Imputation Oversampling
82 Zero Imputation Oversampling
83 Zero Imputation Oversampling
84 Zero Imputation Oversampling
85 Zero Imputation Oversampling
86 Zero Imputation Oversampling
87 Zero Imputation Oversampling
88 Zero Imputation Oversampling
89 Zero Imputation Oversampling
90 Zero Imputation Oversampling
91 Zero Imputation Oversampling
92 Zero Imputation Oversampling
93 Zero Imputation Oversampling
94 Zero Imputation Oversampling
95 Zero Imputation Oversampling
96 Zero Imputation Oversampling
97 Zero Imputation Oversampling
98 Zero Imputation Oversampling
99 Zero Imputation Oversampling
100 Zero Imputation Oversampling
101 Zero Imputation Oversampling
102 Zero Imputation Oversampling
103 Zero Imputation Oversampling
104 Zero Imputation Oversampling
105 Zero Imputation Oversampling
106 Zero Imputation Oversampling
107 Zero Imputation Oversampling
108 Zero Imputation Oversampling
109 Zero Imputation Oversampling
110 Zero Imputation Oversampling
111 Zero Imputation Oversampling
112 Zero Imputation Oversampling
113 Zero Imputation Oversampling
114 Zero Imputation Oversampling
115 Zero Imputation Oversampling
116 Zero Imputation Oversampling
117 Zero Imputation Oversampling
118 Zero Imputation Oversampling
119 Zero Imputation Oversampling
120 Zero Imputation Oversampling
121 Zero Imputation Oversampling
122 Zero Imputation Oversampling
123 Zero Imputation Oversampling
124 Zero Imputation Oversampling
125 Zero Imputation Oversampling
126 Zero Imputation Oversampling
127 Zero Imputation Oversampling
128 Zero Imputation Oversampling
129 Zero Imputation Oversampling
130 Zero Imputation Oversampling
131 Zero Imputation Oversampling
132 Zero Imputation Oversampling
133 Zero Imputation Oversampling
134 Zero Imputation Oversampling
Model Unique Code
0 Gradient Boosting Machines (GBM)_Oversampling_...
1 Gradient Boosting Machines (GBM)_Oversampling_...
2 Gradient Boosting Machines (GBM)_Oversampling_...
3 Gradient Boosting Machines (GBM)_Oversampling_...
4 Gradient Boosting Machines (GBM)_Oversampling_...
5 Gradient Boosting Machines (GBM)_Oversampling_...
6 Gradient Boosting Machines (GBM)_Oversampling_...
7 Gradient Boosting Machines (GBM)_Oversampling_...
8 Gradient Boosting Machines (GBM)_Oversampling_...
9 Gradient Boosting Machines (GBM)_Oversampling_...
10 Gradient Boosting Machines (GBM)_Oversampling_...
11 Gradient Boosting Machines (GBM)_Oversampling_...
12 Gradient Boosting Machines (GBM)_Oversampling_...
13 Gradient Boosting Machines (GBM)_Oversampling_...
14 Gradient Boosting Machines (GBM)_Oversampling_...
15 Gradient Boosting Machines (GBM)_Oversampling_...
16 Gradient Boosting Machines (GBM)_Oversampling_...
17 Gradient Boosting Machines (GBM)_Oversampling_...
18 Gradient Boosting Machines (GBM)_Oversampling_...
19 Gradient Boosting Machines (GBM)_Oversampling_...
20 Gradient Boosting Machines (GBM)_Oversampling_...
21 Gradient Boosting Machines (GBM)_Oversampling_...
22 Gradient Boosting Machines (GBM)_Oversampling_...
23 Gradient Boosting Machines (GBM)_Oversampling_...
24 Gradient Boosting Machines (GBM)_Oversampling_...
25 Gradient Boosting Machines (GBM)_Oversampling_...
26 Gradient Boosting Machines (GBM)_Oversampling_...
27 Gradient Boosting Machines (GBM)_Oversampling_...
28 Gradient Boosting Machines (GBM)_Oversampling_...
29 Gradient Boosting Machines (GBM)_Oversampling_...
30 Gradient Boosting Machines (GBM)_Oversampling_...
31 Gradient Boosting Machines (GBM)_Oversampling_...
32 Gradient Boosting Machines (GBM)_Oversampling_...
33 Gradient Boosting Machines (GBM)_Oversampling_...
34 Gradient Boosting Machines (GBM)_Oversampling_...
35 Gradient Boosting Machines (GBM)_Oversampling_...
36 Gradient Boosting Machines (GBM)_Oversampling_...
37 Gradient Boosting Machines (GBM)_Oversampling_...
38 Gradient Boosting Machines (GBM)_Oversampling_...
39 Gradient Boosting Machines (GBM)_Oversampling_...
40 Gradient Boosting Machines (GBM)_Oversampling_...
41 Gradient Boosting Machines (GBM)_Oversampling_...
42 Gradient Boosting Machines (GBM)_Oversampling_...
43 Gradient Boosting Machines (GBM)_Oversampling_...
44 Gradient Boosting Machines (GBM)_Oversampling_...
45 Gradient Boosting Machines (GBM)_Oversampling_...
46 Gradient Boosting Machines (GBM)_Oversampling_...
47 Gradient Boosting Machines (GBM)_Oversampling_...
48 Gradient Boosting Machines (GBM)_Oversampling_...
49 Gradient Boosting Machines (GBM)_Oversampling_...
50 Gradient Boosting Machines (GBM)_Oversampling_...
51 Gradient Boosting Machines (GBM)_Oversampling_...
52 Gradient Boosting Machines (GBM)_Oversampling_...
53 Gradient Boosting Machines (GBM)_Oversampling_...
54 Gradient Boosting Machines (GBM)_Oversampling_...
55 Gradient Boosting Machines (GBM)_Oversampling_...
56 Gradient Boosting Machines (GBM)_Oversampling_...
57 Gradient Boosting Machines (GBM)_Oversampling_...
58 Gradient Boosting Machines (GBM)_Oversampling_...
59 Gradient Boosting Machines (GBM)_Oversampling_...
60 Gradient Boosting Machines (GBM)_Oversampling_...
61 Gradient Boosting Machines (GBM)_Oversampling_...
62 Gradient Boosting Machines (GBM)_Oversampling_...
63 Gradient Boosting Machines (GBM)_Oversampling_...
64 Gradient Boosting Machines (GBM)_Oversampling_...
65 Gradient Boosting Machines (GBM)_Oversampling_...
66 Gradient Boosting Machines (GBM)_Oversampling_...
67 Gradient Boosting Machines (GBM)_Oversampling_...
68 Gradient Boosting Machines (GBM)_Oversampling_...
69 Gradient Boosting Machines (GBM)_Oversampling_...
70 Gradient Boosting Machines (GBM)_Oversampling_...
71 Gradient Boosting Machines (GBM)_Oversampling_...
72 Gradient Boosting Machines (GBM)_Oversampling_...
73 Gradient Boosting Machines (GBM)_Oversampling_...
74 Gradient Boosting Machines (GBM)_Oversampling_...
75 Gradient Boosting Machines (GBM)_Oversampling_...
76 Gradient Boosting Machines (GBM)_Oversampling_...
77 Gradient Boosting Machines (GBM)_Oversampling_...
78 Gradient Boosting Machines (GBM)_Oversampling_...
79 Gradient Boosting Machines (GBM)_Oversampling_...
80 Gradient Boosting Machines (GBM)_Oversampling_...
81 Gradient Boosting Machines (GBM)_Oversampling_...
82 Gradient Boosting Machines (GBM)_Oversampling_...
83 Gradient Boosting Machines (GBM)_Oversampling_...
84 Gradient Boosting Machines (GBM)_Oversampling_...
85 Gradient Boosting Machines (GBM)_Oversampling_...
86 Gradient Boosting Machines (GBM)_Oversampling_...
87 Gradient Boosting Machines (GBM)_Oversampling_...
88 Gradient Boosting Machines (GBM)_Oversampling_...
89 Gradient Boosting Machines (GBM)_Oversampling_...
90 Gradient Boosting Machines (GBM)_Oversampling_...
91 Gradient Boosting Machines (GBM)_Oversampling_...
92 Gradient Boosting Machines (GBM)_Oversampling_...
93 Gradient Boosting Machines (GBM)_Oversampling_...
94 Gradient Boosting Machines (GBM)_Oversampling_...
95 Gradient Boosting Machines (GBM)_Oversampling_...
96 Gradient Boosting Machines (GBM)_Oversampling_...
97 Gradient Boosting Machines (GBM)_Oversampling_...
98 Gradient Boosting Machines (GBM)_Oversampling_...
99 Gradient Boosting Machines (GBM)_Oversampling_...
100 Gradient Boosting Machines (GBM)_Oversampling_...
101 Gradient Boosting Machines (GBM)_Oversampling_...
102 Gradient Boosting Machines (GBM)_Oversampling_...
103 Gradient Boosting Machines (GBM)_Oversampling_...
104 Gradient Boosting Machines (GBM)_Oversampling_...
105 Gradient Boosting Machines (GBM)_Oversampling_...
106 Gradient Boosting Machines (GBM)_Oversampling_...
107 Gradient Boosting Machines (GBM)_Oversampling_...
108 Gradient Boosting Machines (GBM)_Oversampling_...
109 Gradient Boosting Machines (GBM)_Oversampling_...
110 Gradient Boosting Machines (GBM)_Oversampling_...
111 Gradient Boosting Machines (GBM)_Oversampling_...
112 Gradient Boosting Machines (GBM)_Oversampling_...
113 Gradient Boosting Machines (GBM)_Oversampling_...
114 Gradient Boosting Machines (GBM)_Oversampling_...
115 Gradient Boosting Machines (GBM)_Oversampling_...
116 Gradient Boosting Machines (GBM)_Oversampling_...
117 Gradient Boosting Machines (GBM)_Oversampling_...
118 Gradient Boosting Machines (GBM)_Oversampling_...
119 Gradient Boosting Machines (GBM)_Oversampling_...
120 Gradient Boosting Machines (GBM)_Oversampling_...
121 Gradient Boosting Machines (GBM)_Oversampling_...
122 Gradient Boosting Machines (GBM)_Oversampling_...
123 Gradient Boosting Machines (GBM)_Oversampling_...
124 Gradient Boosting Machines (GBM)_Oversampling_...
125 Gradient Boosting Machines (GBM)_Oversampling_...
126 Gradient Boosting Machines (GBM)_Oversampling_...
127 Gradient Boosting Machines (GBM)_Oversampling_...
128 Gradient Boosting Machines (GBM)_Oversampling_...
129 Gradient Boosting Machines (GBM)_Oversampling_...
130 Gradient Boosting Machines (GBM)_Oversampling_...
131 Gradient Boosting Machines (GBM)_Oversampling_...
132 Gradient Boosting Machines (GBM)_Oversampling_...
133 Gradient Boosting Machines (GBM)_Oversampling_...
134 Gradient Boosting Machines (GBM)_Oversampling_... ,
Algorithm Metrics Hyperparameters \
0 Gradient Boosting Machines (GBM) 0.718725 NaN
1 Gradient Boosting Machines (GBM) 0.113488 NaN
2 Gradient Boosting Machines (GBM) 0.514768 NaN
3 Gradient Boosting Machines (GBM) 0.185976 NaN
4 Gradient Boosting Machines (GBM) 0.682874 NaN
5 Gradient Boosting Machines (GBM) 0.271569 NaN
6 Gradient Boosting Machines (GBM) 0.365748 NaN
7 Gradient Boosting Machines (GBM) NaN 0.0
8 Gradient Boosting Machines (GBM) NaN friedman_mse
9 Gradient Boosting Machines (GBM) NaN None
10 Gradient Boosting Machines (GBM) NaN 0.1
11 Gradient Boosting Machines (GBM) NaN log_loss
12 Gradient Boosting Machines (GBM) NaN 3
13 Gradient Boosting Machines (GBM) NaN None
14 Gradient Boosting Machines (GBM) NaN None
15 Gradient Boosting Machines (GBM) NaN 0.0
16 Gradient Boosting Machines (GBM) NaN 1
17 Gradient Boosting Machines (GBM) NaN 2
18 Gradient Boosting Machines (GBM) NaN 0.0
19 Gradient Boosting Machines (GBM) NaN 100
20 Gradient Boosting Machines (GBM) NaN None
21 Gradient Boosting Machines (GBM) NaN None
22 Gradient Boosting Machines (GBM) NaN 1.0
23 Gradient Boosting Machines (GBM) NaN 0.0001
24 Gradient Boosting Machines (GBM) NaN 0.1
25 Gradient Boosting Machines (GBM) NaN 0
26 Gradient Boosting Machines (GBM) NaN False
27 Gradient Boosting Machines (GBM) 0.706874 NaN
28 Gradient Boosting Machines (GBM) 0.084864 NaN
29 Gradient Boosting Machines (GBM) 0.591463 NaN
30 Gradient Boosting Machines (GBM) 0.148432 NaN
31 Gradient Boosting Machines (GBM) 0.708353 NaN
32 Gradient Boosting Machines (GBM) 0.335527 NaN
33 Gradient Boosting Machines (GBM) 0.416707 NaN
34 Gradient Boosting Machines (GBM) NaN 0.0
35 Gradient Boosting Machines (GBM) NaN friedman_mse
36 Gradient Boosting Machines (GBM) NaN None
37 Gradient Boosting Machines (GBM) NaN 0.1
38 Gradient Boosting Machines (GBM) NaN log_loss
39 Gradient Boosting Machines (GBM) NaN 3
40 Gradient Boosting Machines (GBM) NaN None
41 Gradient Boosting Machines (GBM) NaN None
42 Gradient Boosting Machines (GBM) NaN 0.0
43 Gradient Boosting Machines (GBM) NaN 1
44 Gradient Boosting Machines (GBM) NaN 2
45 Gradient Boosting Machines (GBM) NaN 0.0
46 Gradient Boosting Machines (GBM) NaN 100
47 Gradient Boosting Machines (GBM) NaN None
48 Gradient Boosting Machines (GBM) NaN None
49 Gradient Boosting Machines (GBM) NaN 1.0
50 Gradient Boosting Machines (GBM) NaN 0.0001
51 Gradient Boosting Machines (GBM) NaN 0.1
52 Gradient Boosting Machines (GBM) NaN 0
53 Gradient Boosting Machines (GBM) NaN False
54 Gradient Boosting Machines (GBM) 0.692389 NaN
55 Gradient Boosting Machines (GBM) 0.091590 NaN
56 Gradient Boosting Machines (GBM) 0.588235 NaN
57 Gradient Boosting Machines (GBM) 0.158501 NaN
58 Gradient Boosting Machines (GBM) 0.695575 NaN
59 Gradient Boosting Machines (GBM) 0.300278 NaN
60 Gradient Boosting Machines (GBM) 0.391151 NaN
61 Gradient Boosting Machines (GBM) NaN 0.0
62 Gradient Boosting Machines (GBM) NaN friedman_mse
63 Gradient Boosting Machines (GBM) NaN None
64 Gradient Boosting Machines (GBM) NaN 0.1
65 Gradient Boosting Machines (GBM) NaN log_loss
66 Gradient Boosting Machines (GBM) NaN 3
67 Gradient Boosting Machines (GBM) NaN None
68 Gradient Boosting Machines (GBM) NaN None
69 Gradient Boosting Machines (GBM) NaN 0.0
70 Gradient Boosting Machines (GBM) NaN 1
71 Gradient Boosting Machines (GBM) NaN 2
72 Gradient Boosting Machines (GBM) NaN 0.0
73 Gradient Boosting Machines (GBM) NaN 100
74 Gradient Boosting Machines (GBM) NaN None
75 Gradient Boosting Machines (GBM) NaN None
76 Gradient Boosting Machines (GBM) NaN 1.0
77 Gradient Boosting Machines (GBM) NaN 0.0001
78 Gradient Boosting Machines (GBM) NaN 0.1
79 Gradient Boosting Machines (GBM) NaN 0
80 Gradient Boosting Machines (GBM) NaN False
81 Gradient Boosting Machines (GBM) 0.703635 NaN
82 Gradient Boosting Machines (GBM) 0.095735 NaN
83 Gradient Boosting Machines (GBM) 0.561224 NaN
84 Gradient Boosting Machines (GBM) 0.163569 NaN
85 Gradient Boosting Machines (GBM) 0.691740 NaN
86 Gradient Boosting Machines (GBM) 0.301757 NaN
87 Gradient Boosting Machines (GBM) 0.383481 NaN
88 Gradient Boosting Machines (GBM) NaN 0.0
89 Gradient Boosting Machines (GBM) NaN friedman_mse
90 Gradient Boosting Machines (GBM) NaN None
91 Gradient Boosting Machines (GBM) NaN 0.1
92 Gradient Boosting Machines (GBM) NaN log_loss
93 Gradient Boosting Machines (GBM) NaN 3
94 Gradient Boosting Machines (GBM) NaN None
95 Gradient Boosting Machines (GBM) NaN None
96 Gradient Boosting Machines (GBM) NaN 0.0
97 Gradient Boosting Machines (GBM) NaN 1
98 Gradient Boosting Machines (GBM) NaN 2
99 Gradient Boosting Machines (GBM) NaN 0.0
100 Gradient Boosting Machines (GBM) NaN 100
101 Gradient Boosting Machines (GBM) NaN None
102 Gradient Boosting Machines (GBM) NaN None
103 Gradient Boosting Machines (GBM) NaN 1.0
104 Gradient Boosting Machines (GBM) NaN 0.0001
105 Gradient Boosting Machines (GBM) NaN 0.1
106 Gradient Boosting Machines (GBM) NaN 0
107 Gradient Boosting Machines (GBM) NaN False
108 Gradient Boosting Machines (GBM) 0.713383 NaN
109 Gradient Boosting Machines (GBM) 0.114841 NaN
110 Gradient Boosting Machines (GBM) 0.601852 NaN
111 Gradient Boosting Machines (GBM) 0.192878 NaN
112 Gradient Boosting Machines (GBM) 0.726379 NaN
113 Gradient Boosting Machines (GBM) 0.346638 NaN
114 Gradient Boosting Machines (GBM) 0.452757 NaN
115 Gradient Boosting Machines (GBM) NaN 0.0
116 Gradient Boosting Machines (GBM) NaN friedman_mse
117 Gradient Boosting Machines (GBM) NaN None
118 Gradient Boosting Machines (GBM) NaN 0.1
119 Gradient Boosting Machines (GBM) NaN log_loss
120 Gradient Boosting Machines (GBM) NaN 3
121 Gradient Boosting Machines (GBM) NaN None
122 Gradient Boosting Machines (GBM) NaN None
123 Gradient Boosting Machines (GBM) NaN 0.0
124 Gradient Boosting Machines (GBM) NaN 1
125 Gradient Boosting Machines (GBM) NaN 2
126 Gradient Boosting Machines (GBM) NaN 0.0
127 Gradient Boosting Machines (GBM) NaN 100
128 Gradient Boosting Machines (GBM) NaN None
129 Gradient Boosting Machines (GBM) NaN None
130 Gradient Boosting Machines (GBM) NaN 1.0
131 Gradient Boosting Machines (GBM) NaN 0.0001
132 Gradient Boosting Machines (GBM) NaN 0.1
133 Gradient Boosting Machines (GBM) NaN 0
134 Gradient Boosting Machines (GBM) NaN False
Imputation Technique Imbalance Class Technique \
0 Mean Imputation Oversampling
1 Mean Imputation Oversampling
2 Mean Imputation Oversampling
3 Mean Imputation Oversampling
4 Mean Imputation Oversampling
5 Mean Imputation Oversampling
6 Mean Imputation Oversampling
7 Mean Imputation Oversampling
8 Mean Imputation Oversampling
9 Mean Imputation Oversampling
10 Mean Imputation Oversampling
11 Mean Imputation Oversampling
12 Mean Imputation Oversampling
13 Mean Imputation Oversampling
14 Mean Imputation Oversampling
15 Mean Imputation Oversampling
16 Mean Imputation Oversampling
17 Mean Imputation Oversampling
18 Mean Imputation Oversampling
19 Mean Imputation Oversampling
20 Mean Imputation Oversampling
21 Mean Imputation Oversampling
22 Mean Imputation Oversampling
23 Mean Imputation Oversampling
24 Mean Imputation Oversampling
25 Mean Imputation Oversampling
26 Mean Imputation Oversampling
27 Mean Imputation Oversampling
28 Mean Imputation Oversampling
29 Mean Imputation Oversampling
30 Mean Imputation Oversampling
31 Mean Imputation Oversampling
32 Mean Imputation Oversampling
33 Mean Imputation Oversampling
34 Mean Imputation Oversampling
35 Mean Imputation Oversampling
36 Mean Imputation Oversampling
37 Mean Imputation Oversampling
38 Mean Imputation Oversampling
39 Mean Imputation Oversampling
40 Mean Imputation Oversampling
41 Mean Imputation Oversampling
42 Mean Imputation Oversampling
43 Mean Imputation Oversampling
44 Mean Imputation Oversampling
45 Mean Imputation Oversampling
46 Mean Imputation Oversampling
47 Mean Imputation Oversampling
48 Mean Imputation Oversampling
49 Mean Imputation Oversampling
50 Mean Imputation Oversampling
51 Mean Imputation Oversampling
52 Mean Imputation Oversampling
53 Mean Imputation Oversampling
54 Mean Imputation Oversampling
55 Mean Imputation Oversampling
56 Mean Imputation Oversampling
57 Mean Imputation Oversampling
58 Mean Imputation Oversampling
59 Mean Imputation Oversampling
60 Mean Imputation Oversampling
61 Mean Imputation Oversampling
62 Mean Imputation Oversampling
63 Mean Imputation Oversampling
64 Mean Imputation Oversampling
65 Mean Imputation Oversampling
66 Mean Imputation Oversampling
67 Mean Imputation Oversampling
68 Mean Imputation Oversampling
69 Mean Imputation Oversampling
70 Mean Imputation Oversampling
71 Mean Imputation Oversampling
72 Mean Imputation Oversampling
73 Mean Imputation Oversampling
74 Mean Imputation Oversampling
75 Mean Imputation Oversampling
76 Mean Imputation Oversampling
77 Mean Imputation Oversampling
78 Mean Imputation Oversampling
79 Mean Imputation Oversampling
80 Mean Imputation Oversampling
81 Mean Imputation Oversampling
82 Mean Imputation Oversampling
83 Mean Imputation Oversampling
84 Mean Imputation Oversampling
85 Mean Imputation Oversampling
86 Mean Imputation Oversampling
87 Mean Imputation Oversampling
88 Mean Imputation Oversampling
89 Mean Imputation Oversampling
90 Mean Imputation Oversampling
91 Mean Imputation Oversampling
92 Mean Imputation Oversampling
93 Mean Imputation Oversampling
94 Mean Imputation Oversampling
95 Mean Imputation Oversampling
96 Mean Imputation Oversampling
97 Mean Imputation Oversampling
98 Mean Imputation Oversampling
99 Mean Imputation Oversampling
100 Mean Imputation Oversampling
101 Mean Imputation Oversampling
102 Mean Imputation Oversampling
103 Mean Imputation Oversampling
104 Mean Imputation Oversampling
105 Mean Imputation Oversampling
106 Mean Imputation Oversampling
107 Mean Imputation Oversampling
108 Mean Imputation Oversampling
109 Mean Imputation Oversampling
110 Mean Imputation Oversampling
111 Mean Imputation Oversampling
112 Mean Imputation Oversampling
113 Mean Imputation Oversampling
114 Mean Imputation Oversampling
115 Mean Imputation Oversampling
116 Mean Imputation Oversampling
117 Mean Imputation Oversampling
118 Mean Imputation Oversampling
119 Mean Imputation Oversampling
120 Mean Imputation Oversampling
121 Mean Imputation Oversampling
122 Mean Imputation Oversampling
123 Mean Imputation Oversampling
124 Mean Imputation Oversampling
125 Mean Imputation Oversampling
126 Mean Imputation Oversampling
127 Mean Imputation Oversampling
128 Mean Imputation Oversampling
129 Mean Imputation Oversampling
130 Mean Imputation Oversampling
131 Mean Imputation Oversampling
132 Mean Imputation Oversampling
133 Mean Imputation Oversampling
134 Mean Imputation Oversampling
Model Unique Code
0 Gradient Boosting Machines (GBM)_Oversampling_...
1 Gradient Boosting Machines (GBM)_Oversampling_...
2 Gradient Boosting Machines (GBM)_Oversampling_...
3 Gradient Boosting Machines (GBM)_Oversampling_...
4 Gradient Boosting Machines (GBM)_Oversampling_...
5 Gradient Boosting Machines (GBM)_Oversampling_...
6 Gradient Boosting Machines (GBM)_Oversampling_...
7 Gradient Boosting Machines (GBM)_Oversampling_...
8 Gradient Boosting Machines (GBM)_Oversampling_...
9 Gradient Boosting Machines (GBM)_Oversampling_...
10 Gradient Boosting Machines (GBM)_Oversampling_...
11 Gradient Boosting Machines (GBM)_Oversampling_...
12 Gradient Boosting Machines (GBM)_Oversampling_...
13 Gradient Boosting Machines (GBM)_Oversampling_...
14 Gradient Boosting Machines (GBM)_Oversampling_...
15 Gradient Boosting Machines (GBM)_Oversampling_...
16 Gradient Boosting Machines (GBM)_Oversampling_...
17 Gradient Boosting Machines (GBM)_Oversampling_...
18 Gradient Boosting Machines (GBM)_Oversampling_...
19 Gradient Boosting Machines (GBM)_Oversampling_...
20 Gradient Boosting Machines (GBM)_Oversampling_...
21 Gradient Boosting Machines (GBM)_Oversampling_...
22 Gradient Boosting Machines (GBM)_Oversampling_...
23 Gradient Boosting Machines (GBM)_Oversampling_...
24 Gradient Boosting Machines (GBM)_Oversampling_...
25 Gradient Boosting Machines (GBM)_Oversampling_...
26 Gradient Boosting Machines (GBM)_Oversampling_...
27 Gradient Boosting Machines (GBM)_Oversampling_...
28 Gradient Boosting Machines (GBM)_Oversampling_...
29 Gradient Boosting Machines (GBM)_Oversampling_...
30 Gradient Boosting Machines (GBM)_Oversampling_...
31 Gradient Boosting Machines (GBM)_Oversampling_...
32 Gradient Boosting Machines (GBM)_Oversampling_...
33 Gradient Boosting Machines (GBM)_Oversampling_...
34 Gradient Boosting Machines (GBM)_Oversampling_...
35 Gradient Boosting Machines (GBM)_Oversampling_...
36 Gradient Boosting Machines (GBM)_Oversampling_...
37 Gradient Boosting Machines (GBM)_Oversampling_...
38 Gradient Boosting Machines (GBM)_Oversampling_...
39 Gradient Boosting Machines (GBM)_Oversampling_...
40 Gradient Boosting Machines (GBM)_Oversampling_...
41 Gradient Boosting Machines (GBM)_Oversampling_...
42 Gradient Boosting Machines (GBM)_Oversampling_...
43 Gradient Boosting Machines (GBM)_Oversampling_...
44 Gradient Boosting Machines (GBM)_Oversampling_...
45 Gradient Boosting Machines (GBM)_Oversampling_...
46 Gradient Boosting Machines (GBM)_Oversampling_...
47 Gradient Boosting Machines (GBM)_Oversampling_...
48 Gradient Boosting Machines (GBM)_Oversampling_...
49 Gradient Boosting Machines (GBM)_Oversampling_...
50 Gradient Boosting Machines (GBM)_Oversampling_...
51 Gradient Boosting Machines (GBM)_Oversampling_...
52 Gradient Boosting Machines (GBM)_Oversampling_...
53 Gradient Boosting Machines (GBM)_Oversampling_...
54 Gradient Boosting Machines (GBM)_Oversampling_...
55 Gradient Boosting Machines (GBM)_Oversampling_...
56 Gradient Boosting Machines (GBM)_Oversampling_...
57 Gradient Boosting Machines (GBM)_Oversampling_...
58 Gradient Boosting Machines (GBM)_Oversampling_...
59 Gradient Boosting Machines (GBM)_Oversampling_...
60 Gradient Boosting Machines (GBM)_Oversampling_...
61 Gradient Boosting Machines (GBM)_Oversampling_...
62 Gradient Boosting Machines (GBM)_Oversampling_...
63 Gradient Boosting Machines (GBM)_Oversampling_...
64 Gradient Boosting Machines (GBM)_Oversampling_...
65 Gradient Boosting Machines (GBM)_Oversampling_...
66 Gradient Boosting Machines (GBM)_Oversampling_...
67 Gradient Boosting Machines (GBM)_Oversampling_...
68 Gradient Boosting Machines (GBM)_Oversampling_...
69 Gradient Boosting Machines (GBM)_Oversampling_...
70 Gradient Boosting Machines (GBM)_Oversampling_...
71 Gradient Boosting Machines (GBM)_Oversampling_...
72 Gradient Boosting Machines (GBM)_Oversampling_...
73 Gradient Boosting Machines (GBM)_Oversampling_...
74 Gradient Boosting Machines (GBM)_Oversampling_...
75 Gradient Boosting Machines (GBM)_Oversampling_...
76 Gradient Boosting Machines (GBM)_Oversampling_...
77 Gradient Boosting Machines (GBM)_Oversampling_...
78 Gradient Boosting Machines (GBM)_Oversampling_...
79 Gradient Boosting Machines (GBM)_Oversampling_...
80 Gradient Boosting Machines (GBM)_Oversampling_...
81 Gradient Boosting Machines (GBM)_Oversampling_...
82 Gradient Boosting Machines (GBM)_Oversampling_...
83 Gradient Boosting Machines (GBM)_Oversampling_...
84 Gradient Boosting Machines (GBM)_Oversampling_...
85 Gradient Boosting Machines (GBM)_Oversampling_...
86 Gradient Boosting Machines (GBM)_Oversampling_...
87 Gradient Boosting Machines (GBM)_Oversampling_...
88 Gradient Boosting Machines (GBM)_Oversampling_...
89 Gradient Boosting Machines (GBM)_Oversampling_...
90 Gradient Boosting Machines (GBM)_Oversampling_...
91 Gradient Boosting Machines (GBM)_Oversampling_...
92 Gradient Boosting Machines (GBM)_Oversampling_...
93 Gradient Boosting Machines (GBM)_Oversampling_...
94 Gradient Boosting Machines (GBM)_Oversampling_...
95 Gradient Boosting Machines (GBM)_Oversampling_...
96 Gradient Boosting Machines (GBM)_Oversampling_...
97 Gradient Boosting Machines (GBM)_Oversampling_...
98 Gradient Boosting Machines (GBM)_Oversampling_...
99 Gradient Boosting Machines (GBM)_Oversampling_...
100 Gradient Boosting Machines (GBM)_Oversampling_...
101 Gradient Boosting Machines (GBM)_Oversampling_...
102 Gradient Boosting Machines (GBM)_Oversampling_...
103 Gradient Boosting Machines (GBM)_Oversampling_...
104 Gradient Boosting Machines (GBM)_Oversampling_...
105 Gradient Boosting Machines (GBM)_Oversampling_...
106 Gradient Boosting Machines (GBM)_Oversampling_...
107 Gradient Boosting Machines (GBM)_Oversampling_...
108 Gradient Boosting Machines (GBM)_Oversampling_...
109 Gradient Boosting Machines (GBM)_Oversampling_...
110 Gradient Boosting Machines (GBM)_Oversampling_...
111 Gradient Boosting Machines (GBM)_Oversampling_...
112 Gradient Boosting Machines (GBM)_Oversampling_...
113 Gradient Boosting Machines (GBM)_Oversampling_...
114 Gradient Boosting Machines (GBM)_Oversampling_...
115 Gradient Boosting Machines (GBM)_Oversampling_...
116 Gradient Boosting Machines (GBM)_Oversampling_...
117 Gradient Boosting Machines (GBM)_Oversampling_...
118 Gradient Boosting Machines (GBM)_Oversampling_...
119 Gradient Boosting Machines (GBM)_Oversampling_...
120 Gradient Boosting Machines (GBM)_Oversampling_...
121 Gradient Boosting Machines (GBM)_Oversampling_...
122 Gradient Boosting Machines (GBM)_Oversampling_...
123 Gradient Boosting Machines (GBM)_Oversampling_...
124 Gradient Boosting Machines (GBM)_Oversampling_...
125 Gradient Boosting Machines (GBM)_Oversampling_...
126 Gradient Boosting Machines (GBM)_Oversampling_...
127 Gradient Boosting Machines (GBM)_Oversampling_...
128 Gradient Boosting Machines (GBM)_Oversampling_...
129 Gradient Boosting Machines (GBM)_Oversampling_...
130 Gradient Boosting Machines (GBM)_Oversampling_...
131 Gradient Boosting Machines (GBM)_Oversampling_...
132 Gradient Boosting Machines (GBM)_Oversampling_...
133 Gradient Boosting Machines (GBM)_Oversampling_...
134 Gradient Boosting Machines (GBM)_Oversampling_... ,
Algorithm Metrics Hyperparameters \
0 Gradient Boosting Machines (GBM) 0.703713 NaN
1 Gradient Boosting Machines (GBM) 0.117241 NaN
2 Gradient Boosting Machines (GBM) 0.573840 NaN
3 Gradient Boosting Machines (GBM) 0.194703 NaN
4 Gradient Boosting Machines (GBM) 0.699783 NaN
5 Gradient Boosting Machines (GBM) 0.301492 NaN
6 Gradient Boosting Machines (GBM) 0.399565 NaN
7 Gradient Boosting Machines (GBM) NaN 0.0
8 Gradient Boosting Machines (GBM) NaN friedman_mse
9 Gradient Boosting Machines (GBM) NaN None
10 Gradient Boosting Machines (GBM) NaN 0.1
11 Gradient Boosting Machines (GBM) NaN log_loss
12 Gradient Boosting Machines (GBM) NaN 3
13 Gradient Boosting Machines (GBM) NaN None
14 Gradient Boosting Machines (GBM) NaN None
15 Gradient Boosting Machines (GBM) NaN 0.0
16 Gradient Boosting Machines (GBM) NaN 1
17 Gradient Boosting Machines (GBM) NaN 2
18 Gradient Boosting Machines (GBM) NaN 0.0
19 Gradient Boosting Machines (GBM) NaN 100
20 Gradient Boosting Machines (GBM) NaN None
21 Gradient Boosting Machines (GBM) NaN None
22 Gradient Boosting Machines (GBM) NaN 1.0
23 Gradient Boosting Machines (GBM) NaN 0.0001
24 Gradient Boosting Machines (GBM) NaN 0.1
25 Gradient Boosting Machines (GBM) NaN 0
26 Gradient Boosting Machines (GBM) NaN False
27 Gradient Boosting Machines (GBM) 0.695286 NaN
28 Gradient Boosting Machines (GBM) 0.080304 NaN
29 Gradient Boosting Machines (GBM) 0.579268 NaN
30 Gradient Boosting Machines (GBM) 0.141054 NaN
31 Gradient Boosting Machines (GBM) 0.706890 NaN
32 Gradient Boosting Machines (GBM) 0.339134 NaN
33 Gradient Boosting Machines (GBM) 0.413780 NaN
34 Gradient Boosting Machines (GBM) NaN 0.0
35 Gradient Boosting Machines (GBM) NaN friedman_mse
36 Gradient Boosting Machines (GBM) NaN None
37 Gradient Boosting Machines (GBM) NaN 0.1
38 Gradient Boosting Machines (GBM) NaN log_loss
39 Gradient Boosting Machines (GBM) NaN 3
40 Gradient Boosting Machines (GBM) NaN None
41 Gradient Boosting Machines (GBM) NaN None
42 Gradient Boosting Machines (GBM) NaN 0.0
43 Gradient Boosting Machines (GBM) NaN 1
44 Gradient Boosting Machines (GBM) NaN 2
45 Gradient Boosting Machines (GBM) NaN 0.0
46 Gradient Boosting Machines (GBM) NaN 100
47 Gradient Boosting Machines (GBM) NaN None
48 Gradient Boosting Machines (GBM) NaN None
49 Gradient Boosting Machines (GBM) NaN 1.0
50 Gradient Boosting Machines (GBM) NaN 0.0001
51 Gradient Boosting Machines (GBM) NaN 0.1
52 Gradient Boosting Machines (GBM) NaN 0
53 Gradient Boosting Machines (GBM) NaN False
54 Gradient Boosting Machines (GBM) 0.690809 NaN
55 Gradient Boosting Machines (GBM) 0.094495 NaN
56 Gradient Boosting Machines (GBM) 0.614973 NaN
57 Gradient Boosting Machines (GBM) 0.163818 NaN
58 Gradient Boosting Machines (GBM) 0.699653 NaN
59 Gradient Boosting Machines (GBM) 0.320946 NaN
60 Gradient Boosting Machines (GBM) 0.399307 NaN
61 Gradient Boosting Machines (GBM) NaN 0.0
62 Gradient Boosting Machines (GBM) NaN friedman_mse
63 Gradient Boosting Machines (GBM) NaN None
64 Gradient Boosting Machines (GBM) NaN 0.1
65 Gradient Boosting Machines (GBM) NaN log_loss
66 Gradient Boosting Machines (GBM) NaN 3
67 Gradient Boosting Machines (GBM) NaN None
68 Gradient Boosting Machines (GBM) NaN None
69 Gradient Boosting Machines (GBM) NaN 0.0
70 Gradient Boosting Machines (GBM) NaN 1
71 Gradient Boosting Machines (GBM) NaN 2
72 Gradient Boosting Machines (GBM) NaN 0.0
73 Gradient Boosting Machines (GBM) NaN 100
74 Gradient Boosting Machines (GBM) NaN None
75 Gradient Boosting Machines (GBM) NaN None
76 Gradient Boosting Machines (GBM) NaN 1.0
77 Gradient Boosting Machines (GBM) NaN 0.0001
78 Gradient Boosting Machines (GBM) NaN 0.1
79 Gradient Boosting Machines (GBM) NaN 0
80 Gradient Boosting Machines (GBM) NaN False
81 Gradient Boosting Machines (GBM) 0.688356 NaN
82 Gradient Boosting Machines (GBM) 0.097800 NaN
83 Gradient Boosting Machines (GBM) 0.612245 NaN
84 Gradient Boosting Machines (GBM) 0.168658 NaN
85 Gradient Boosting Machines (GBM) 0.703859 NaN
86 Gradient Boosting Machines (GBM) 0.320193 NaN
87 Gradient Boosting Machines (GBM) 0.407718 NaN
88 Gradient Boosting Machines (GBM) NaN 0.0
89 Gradient Boosting Machines (GBM) NaN friedman_mse
90 Gradient Boosting Machines (GBM) NaN None
91 Gradient Boosting Machines (GBM) NaN 0.1
92 Gradient Boosting Machines (GBM) NaN log_loss
93 Gradient Boosting Machines (GBM) NaN 3
94 Gradient Boosting Machines (GBM) NaN None
95 Gradient Boosting Machines (GBM) NaN None
96 Gradient Boosting Machines (GBM) NaN 0.0
97 Gradient Boosting Machines (GBM) NaN 1
98 Gradient Boosting Machines (GBM) NaN 2
99 Gradient Boosting Machines (GBM) NaN 0.0
100 Gradient Boosting Machines (GBM) NaN 100
101 Gradient Boosting Machines (GBM) NaN None
102 Gradient Boosting Machines (GBM) NaN None
103 Gradient Boosting Machines (GBM) NaN 1.0
104 Gradient Boosting Machines (GBM) NaN 0.0001
105 Gradient Boosting Machines (GBM) NaN 0.1
106 Gradient Boosting Machines (GBM) NaN 0
107 Gradient Boosting Machines (GBM) NaN False
108 Gradient Boosting Machines (GBM) 0.720232 NaN
109 Gradient Boosting Machines (GBM) 0.115455 NaN
110 Gradient Boosting Machines (GBM) 0.587963 NaN
111 Gradient Boosting Machines (GBM) 0.193009 NaN
112 Gradient Boosting Machines (GBM) 0.709960 NaN
113 Gradient Boosting Machines (GBM) 0.328073 NaN
114 Gradient Boosting Machines (GBM) 0.419919 NaN
115 Gradient Boosting Machines (GBM) NaN 0.0
116 Gradient Boosting Machines (GBM) NaN friedman_mse
117 Gradient Boosting Machines (GBM) NaN None
118 Gradient Boosting Machines (GBM) NaN 0.1
119 Gradient Boosting Machines (GBM) NaN log_loss
120 Gradient Boosting Machines (GBM) NaN 3
121 Gradient Boosting Machines (GBM) NaN None
122 Gradient Boosting Machines (GBM) NaN None
123 Gradient Boosting Machines (GBM) NaN 0.0
124 Gradient Boosting Machines (GBM) NaN 1
125 Gradient Boosting Machines (GBM) NaN 2
126 Gradient Boosting Machines (GBM) NaN 0.0
127 Gradient Boosting Machines (GBM) NaN 100
128 Gradient Boosting Machines (GBM) NaN None
129 Gradient Boosting Machines (GBM) NaN None
130 Gradient Boosting Machines (GBM) NaN 1.0
131 Gradient Boosting Machines (GBM) NaN 0.0001
132 Gradient Boosting Machines (GBM) NaN 0.1
133 Gradient Boosting Machines (GBM) NaN 0
134 Gradient Boosting Machines (GBM) NaN False
Imputation Technique Imbalance Class Technique \
0 Median Imputation Oversampling
1 Median Imputation Oversampling
2 Median Imputation Oversampling
3 Median Imputation Oversampling
4 Median Imputation Oversampling
5 Median Imputation Oversampling
6 Median Imputation Oversampling
7 Median Imputation Oversampling
8 Median Imputation Oversampling
9 Median Imputation Oversampling
10 Median Imputation Oversampling
11 Median Imputation Oversampling
12 Median Imputation Oversampling
13 Median Imputation Oversampling
14 Median Imputation Oversampling
15 Median Imputation Oversampling
16 Median Imputation Oversampling
17 Median Imputation Oversampling
18 Median Imputation Oversampling
19 Median Imputation Oversampling
20 Median Imputation Oversampling
21 Median Imputation Oversampling
22 Median Imputation Oversampling
23 Median Imputation Oversampling
24 Median Imputation Oversampling
25 Median Imputation Oversampling
26 Median Imputation Oversampling
27 Median Imputation Oversampling
28 Median Imputation Oversampling
29 Median Imputation Oversampling
30 Median Imputation Oversampling
31 Median Imputation Oversampling
32 Median Imputation Oversampling
33 Median Imputation Oversampling
34 Median Imputation Oversampling
35 Median Imputation Oversampling
36 Median Imputation Oversampling
37 Median Imputation Oversampling
38 Median Imputation Oversampling
39 Median Imputation Oversampling
40 Median Imputation Oversampling
41 Median Imputation Oversampling
42 Median Imputation Oversampling
43 Median Imputation Oversampling
44 Median Imputation Oversampling
45 Median Imputation Oversampling
46 Median Imputation Oversampling
47 Median Imputation Oversampling
48 Median Imputation Oversampling
49 Median Imputation Oversampling
50 Median Imputation Oversampling
51 Median Imputation Oversampling
52 Median Imputation Oversampling
53 Median Imputation Oversampling
54 Median Imputation Oversampling
55 Median Imputation Oversampling
56 Median Imputation Oversampling
57 Median Imputation Oversampling
58 Median Imputation Oversampling
59 Median Imputation Oversampling
60 Median Imputation Oversampling
61 Median Imputation Oversampling
62 Median Imputation Oversampling
63 Median Imputation Oversampling
64 Median Imputation Oversampling
65 Median Imputation Oversampling
66 Median Imputation Oversampling
67 Median Imputation Oversampling
68 Median Imputation Oversampling
69 Median Imputation Oversampling
70 Median Imputation Oversampling
71 Median Imputation Oversampling
72 Median Imputation Oversampling
73 Median Imputation Oversampling
74 Median Imputation Oversampling
75 Median Imputation Oversampling
76 Median Imputation Oversampling
77 Median Imputation Oversampling
78 Median Imputation Oversampling
79 Median Imputation Oversampling
80 Median Imputation Oversampling
81 Median Imputation Oversampling
82 Median Imputation Oversampling
83 Median Imputation Oversampling
84 Median Imputation Oversampling
85 Median Imputation Oversampling
86 Median Imputation Oversampling
87 Median Imputation Oversampling
88 Median Imputation Oversampling
89 Median Imputation Oversampling
90 Median Imputation Oversampling
91 Median Imputation Oversampling
92 Median Imputation Oversampling
93 Median Imputation Oversampling
94 Median Imputation Oversampling
95 Median Imputation Oversampling
96 Median Imputation Oversampling
97 Median Imputation Oversampling
98 Median Imputation Oversampling
99 Median Imputation Oversampling
100 Median Imputation Oversampling
101 Median Imputation Oversampling
102 Median Imputation Oversampling
103 Median Imputation Oversampling
104 Median Imputation Oversampling
105 Median Imputation Oversampling
106 Median Imputation Oversampling
107 Median Imputation Oversampling
108 Median Imputation Oversampling
109 Median Imputation Oversampling
110 Median Imputation Oversampling
111 Median Imputation Oversampling
112 Median Imputation Oversampling
113 Median Imputation Oversampling
114 Median Imputation Oversampling
115 Median Imputation Oversampling
116 Median Imputation Oversampling
117 Median Imputation Oversampling
118 Median Imputation Oversampling
119 Median Imputation Oversampling
120 Median Imputation Oversampling
121 Median Imputation Oversampling
122 Median Imputation Oversampling
123 Median Imputation Oversampling
124 Median Imputation Oversampling
125 Median Imputation Oversampling
126 Median Imputation Oversampling
127 Median Imputation Oversampling
128 Median Imputation Oversampling
129 Median Imputation Oversampling
130 Median Imputation Oversampling
131 Median Imputation Oversampling
132 Median Imputation Oversampling
133 Median Imputation Oversampling
134 Median Imputation Oversampling
Model Unique Code
0 Gradient Boosting Machines (GBM)_Oversampling_...
1 Gradient Boosting Machines (GBM)_Oversampling_...
2 Gradient Boosting Machines (GBM)_Oversampling_...
3 Gradient Boosting Machines (GBM)_Oversampling_...
4 Gradient Boosting Machines (GBM)_Oversampling_...
5 Gradient Boosting Machines (GBM)_Oversampling_...
6 Gradient Boosting Machines (GBM)_Oversampling_...
7 Gradient Boosting Machines (GBM)_Oversampling_...
8 Gradient Boosting Machines (GBM)_Oversampling_...
9 Gradient Boosting Machines (GBM)_Oversampling_...
10 Gradient Boosting Machines (GBM)_Oversampling_...
11 Gradient Boosting Machines (GBM)_Oversampling_...
12 Gradient Boosting Machines (GBM)_Oversampling_...
13 Gradient Boosting Machines (GBM)_Oversampling_...
14 Gradient Boosting Machines (GBM)_Oversampling_...
15 Gradient Boosting Machines (GBM)_Oversampling_...
16 Gradient Boosting Machines (GBM)_Oversampling_...
17 Gradient Boosting Machines (GBM)_Oversampling_...
18 Gradient Boosting Machines (GBM)_Oversampling_...
19 Gradient Boosting Machines (GBM)_Oversampling_...
20 Gradient Boosting Machines (GBM)_Oversampling_...
21 Gradient Boosting Machines (GBM)_Oversampling_...
22 Gradient Boosting Machines (GBM)_Oversampling_...
23 Gradient Boosting Machines (GBM)_Oversampling_...
24 Gradient Boosting Machines (GBM)_Oversampling_...
25 Gradient Boosting Machines (GBM)_Oversampling_...
26 Gradient Boosting Machines (GBM)_Oversampling_...
27 Gradient Boosting Machines (GBM)_Oversampling_...
28 Gradient Boosting Machines (GBM)_Oversampling_...
29 Gradient Boosting Machines (GBM)_Oversampling_...
30 Gradient Boosting Machines (GBM)_Oversampling_...
31 Gradient Boosting Machines (GBM)_Oversampling_...
32 Gradient Boosting Machines (GBM)_Oversampling_...
33 Gradient Boosting Machines (GBM)_Oversampling_...
34 Gradient Boosting Machines (GBM)_Oversampling_...
35 Gradient Boosting Machines (GBM)_Oversampling_...
36 Gradient Boosting Machines (GBM)_Oversampling_...
37 Gradient Boosting Machines (GBM)_Oversampling_...
38 Gradient Boosting Machines (GBM)_Oversampling_...
39 Gradient Boosting Machines (GBM)_Oversampling_...
40 Gradient Boosting Machines (GBM)_Oversampling_...
41 Gradient Boosting Machines (GBM)_Oversampling_...
42 Gradient Boosting Machines (GBM)_Oversampling_...
43 Gradient Boosting Machines (GBM)_Oversampling_...
44 Gradient Boosting Machines (GBM)_Oversampling_...
45 Gradient Boosting Machines (GBM)_Oversampling_...
46 Gradient Boosting Machines (GBM)_Oversampling_...
47 Gradient Boosting Machines (GBM)_Oversampling_...
48 Gradient Boosting Machines (GBM)_Oversampling_...
49 Gradient Boosting Machines (GBM)_Oversampling_...
50 Gradient Boosting Machines (GBM)_Oversampling_...
51 Gradient Boosting Machines (GBM)_Oversampling_...
52 Gradient Boosting Machines (GBM)_Oversampling_...
53 Gradient Boosting Machines (GBM)_Oversampling_...
54 Gradient Boosting Machines (GBM)_Oversampling_...
55 Gradient Boosting Machines (GBM)_Oversampling_...
56 Gradient Boosting Machines (GBM)_Oversampling_...
57 Gradient Boosting Machines (GBM)_Oversampling_...
58 Gradient Boosting Machines (GBM)_Oversampling_...
59 Gradient Boosting Machines (GBM)_Oversampling_...
60 Gradient Boosting Machines (GBM)_Oversampling_...
61 Gradient Boosting Machines (GBM)_Oversampling_...
62 Gradient Boosting Machines (GBM)_Oversampling_...
63 Gradient Boosting Machines (GBM)_Oversampling_...
64 Gradient Boosting Machines (GBM)_Oversampling_...
65 Gradient Boosting Machines (GBM)_Oversampling_...
66 Gradient Boosting Machines (GBM)_Oversampling_...
67 Gradient Boosting Machines (GBM)_Oversampling_...
68 Gradient Boosting Machines (GBM)_Oversampling_...
69 Gradient Boosting Machines (GBM)_Oversampling_...
70 Gradient Boosting Machines (GBM)_Oversampling_...
71 Gradient Boosting Machines (GBM)_Oversampling_...
72 Gradient Boosting Machines (GBM)_Oversampling_...
73 Gradient Boosting Machines (GBM)_Oversampling_...
74 Gradient Boosting Machines (GBM)_Oversampling_...
75 Gradient Boosting Machines (GBM)_Oversampling_...
76 Gradient Boosting Machines (GBM)_Oversampling_...
77 Gradient Boosting Machines (GBM)_Oversampling_...
78 Gradient Boosting Machines (GBM)_Oversampling_...
79 Gradient Boosting Machines (GBM)_Oversampling_...
80 Gradient Boosting Machines (GBM)_Oversampling_...
81 Gradient Boosting Machines (GBM)_Oversampling_...
82 Gradient Boosting Machines (GBM)_Oversampling_...
83 Gradient Boosting Machines (GBM)_Oversampling_...
84 Gradient Boosting Machines (GBM)_Oversampling_...
85 Gradient Boosting Machines (GBM)_Oversampling_...
86 Gradient Boosting Machines (GBM)_Oversampling_...
87 Gradient Boosting Machines (GBM)_Oversampling_...
88 Gradient Boosting Machines (GBM)_Oversampling_...
89 Gradient Boosting Machines (GBM)_Oversampling_...
90 Gradient Boosting Machines (GBM)_Oversampling_...
91 Gradient Boosting Machines (GBM)_Oversampling_...
92 Gradient Boosting Machines (GBM)_Oversampling_...
93 Gradient Boosting Machines (GBM)_Oversampling_...
94 Gradient Boosting Machines (GBM)_Oversampling_...
95 Gradient Boosting Machines (GBM)_Oversampling_...
96 Gradient Boosting Machines (GBM)_Oversampling_...
97 Gradient Boosting Machines (GBM)_Oversampling_...
98 Gradient Boosting Machines (GBM)_Oversampling_...
99 Gradient Boosting Machines (GBM)_Oversampling_...
100 Gradient Boosting Machines (GBM)_Oversampling_...
101 Gradient Boosting Machines (GBM)_Oversampling_...
102 Gradient Boosting Machines (GBM)_Oversampling_...
103 Gradient Boosting Machines (GBM)_Oversampling_...
104 Gradient Boosting Machines (GBM)_Oversampling_...
105 Gradient Boosting Machines (GBM)_Oversampling_...
106 Gradient Boosting Machines (GBM)_Oversampling_...
107 Gradient Boosting Machines (GBM)_Oversampling_...
108 Gradient Boosting Machines (GBM)_Oversampling_...
109 Gradient Boosting Machines (GBM)_Oversampling_...
110 Gradient Boosting Machines (GBM)_Oversampling_...
111 Gradient Boosting Machines (GBM)_Oversampling_...
112 Gradient Boosting Machines (GBM)_Oversampling_...
113 Gradient Boosting Machines (GBM)_Oversampling_...
114 Gradient Boosting Machines (GBM)_Oversampling_...
115 Gradient Boosting Machines (GBM)_Oversampling_...
116 Gradient Boosting Machines (GBM)_Oversampling_...
117 Gradient Boosting Machines (GBM)_Oversampling_...
118 Gradient Boosting Machines (GBM)_Oversampling_...
119 Gradient Boosting Machines (GBM)_Oversampling_...
120 Gradient Boosting Machines (GBM)_Oversampling_...
121 Gradient Boosting Machines (GBM)_Oversampling_...
122 Gradient Boosting Machines (GBM)_Oversampling_...
123 Gradient Boosting Machines (GBM)_Oversampling_...
124 Gradient Boosting Machines (GBM)_Oversampling_...
125 Gradient Boosting Machines (GBM)_Oversampling_...
126 Gradient Boosting Machines (GBM)_Oversampling_...
127 Gradient Boosting Machines (GBM)_Oversampling_...
128 Gradient Boosting Machines (GBM)_Oversampling_...
129 Gradient Boosting Machines (GBM)_Oversampling_...
130 Gradient Boosting Machines (GBM)_Oversampling_...
131 Gradient Boosting Machines (GBM)_Oversampling_...
132 Gradient Boosting Machines (GBM)_Oversampling_...
133 Gradient Boosting Machines (GBM)_Oversampling_...
134 Gradient Boosting Machines (GBM)_Oversampling_... ,
Algorithm Metrics Hyperparameters \
0 Gradient Boosting Machines (GBM) 0.624967 NaN
1 Gradient Boosting Machines (GBM) 0.106171 NaN
2 Gradient Boosting Machines (GBM) 0.675105 NaN
3 Gradient Boosting Machines (GBM) 0.183486 NaN
4 Gradient Boosting Machines (GBM) 0.691533 NaN
5 Gradient Boosting Machines (GBM) 0.305490 NaN
6 Gradient Boosting Machines (GBM) 0.383067 NaN
7 Gradient Boosting Machines (GBM) NaN 0.0
8 Gradient Boosting Machines (GBM) NaN friedman_mse
9 Gradient Boosting Machines (GBM) NaN None
10 Gradient Boosting Machines (GBM) NaN 0.1
11 Gradient Boosting Machines (GBM) NaN log_loss
12 Gradient Boosting Machines (GBM) NaN 3
13 Gradient Boosting Machines (GBM) NaN None
14 Gradient Boosting Machines (GBM) NaN None
15 Gradient Boosting Machines (GBM) NaN 0.0
16 Gradient Boosting Machines (GBM) NaN 1
17 Gradient Boosting Machines (GBM) NaN 2
18 Gradient Boosting Machines (GBM) NaN 0.0
19 Gradient Boosting Machines (GBM) NaN 100
20 Gradient Boosting Machines (GBM) NaN None
21 Gradient Boosting Machines (GBM) NaN None
22 Gradient Boosting Machines (GBM) NaN 1.0
23 Gradient Boosting Machines (GBM) NaN 0.0001
24 Gradient Boosting Machines (GBM) NaN 0.1
25 Gradient Boosting Machines (GBM) NaN 0
26 Gradient Boosting Machines (GBM) NaN False
27 Gradient Boosting Machines (GBM) 0.640769 NaN
28 Gradient Boosting Machines (GBM) 0.079832 NaN
29 Gradient Boosting Machines (GBM) 0.695122 NaN
30 Gradient Boosting Machines (GBM) 0.143216 NaN
31 Gradient Boosting Machines (GBM) 0.707087 NaN
32 Gradient Boosting Machines (GBM) 0.339493 NaN
33 Gradient Boosting Machines (GBM) 0.414174 NaN
34 Gradient Boosting Machines (GBM) NaN 0.0
35 Gradient Boosting Machines (GBM) NaN friedman_mse
36 Gradient Boosting Machines (GBM) NaN None
37 Gradient Boosting Machines (GBM) NaN 0.1
38 Gradient Boosting Machines (GBM) NaN log_loss
39 Gradient Boosting Machines (GBM) NaN 3
40 Gradient Boosting Machines (GBM) NaN None
41 Gradient Boosting Machines (GBM) NaN None
42 Gradient Boosting Machines (GBM) NaN 0.0
43 Gradient Boosting Machines (GBM) NaN 1
44 Gradient Boosting Machines (GBM) NaN 2
45 Gradient Boosting Machines (GBM) NaN 0.0
46 Gradient Boosting Machines (GBM) NaN 100
47 Gradient Boosting Machines (GBM) NaN None
48 Gradient Boosting Machines (GBM) NaN None
49 Gradient Boosting Machines (GBM) NaN 1.0
50 Gradient Boosting Machines (GBM) NaN 0.0001
51 Gradient Boosting Machines (GBM) NaN 0.1
52 Gradient Boosting Machines (GBM) NaN 0
53 Gradient Boosting Machines (GBM) NaN False
54 Gradient Boosting Machines (GBM) 0.608638 NaN
55 Gradient Boosting Machines (GBM) 0.076321 NaN
56 Gradient Boosting Machines (GBM) 0.625668 NaN
57 Gradient Boosting Machines (GBM) 0.136047 NaN
58 Gradient Boosting Machines (GBM) 0.672355 NaN
59 Gradient Boosting Machines (GBM) 0.249623 NaN
60 Gradient Boosting Machines (GBM) 0.344709 NaN
61 Gradient Boosting Machines (GBM) NaN 0.0
62 Gradient Boosting Machines (GBM) NaN friedman_mse
63 Gradient Boosting Machines (GBM) NaN None
64 Gradient Boosting Machines (GBM) NaN 0.1
65 Gradient Boosting Machines (GBM) NaN log_loss
66 Gradient Boosting Machines (GBM) NaN 3
67 Gradient Boosting Machines (GBM) NaN None
68 Gradient Boosting Machines (GBM) NaN None
69 Gradient Boosting Machines (GBM) NaN 0.0
70 Gradient Boosting Machines (GBM) NaN 1
71 Gradient Boosting Machines (GBM) NaN 2
72 Gradient Boosting Machines (GBM) NaN 0.0
73 Gradient Boosting Machines (GBM) NaN 100
74 Gradient Boosting Machines (GBM) NaN None
75 Gradient Boosting Machines (GBM) NaN None
76 Gradient Boosting Machines (GBM) NaN 1.0
77 Gradient Boosting Machines (GBM) NaN 0.0001
78 Gradient Boosting Machines (GBM) NaN 0.1
79 Gradient Boosting Machines (GBM) NaN 0
80 Gradient Boosting Machines (GBM) NaN False
81 Gradient Boosting Machines (GBM) 0.619863 NaN
82 Gradient Boosting Machines (GBM) 0.082384 NaN
83 Gradient Boosting Machines (GBM) 0.627551 NaN
84 Gradient Boosting Machines (GBM) 0.145648 NaN
85 Gradient Boosting Machines (GBM) 0.674713 NaN
86 Gradient Boosting Machines (GBM) 0.272523 NaN
87 Gradient Boosting Machines (GBM) 0.349426 NaN
88 Gradient Boosting Machines (GBM) NaN 0.0
89 Gradient Boosting Machines (GBM) NaN friedman_mse
90 Gradient Boosting Machines (GBM) NaN None
91 Gradient Boosting Machines (GBM) NaN 0.1
92 Gradient Boosting Machines (GBM) NaN log_loss
93 Gradient Boosting Machines (GBM) NaN 3
94 Gradient Boosting Machines (GBM) NaN None
95 Gradient Boosting Machines (GBM) NaN None
96 Gradient Boosting Machines (GBM) NaN 0.0
97 Gradient Boosting Machines (GBM) NaN 1
98 Gradient Boosting Machines (GBM) NaN 2
99 Gradient Boosting Machines (GBM) NaN 0.0
100 Gradient Boosting Machines (GBM) NaN 100
101 Gradient Boosting Machines (GBM) NaN None
102 Gradient Boosting Machines (GBM) NaN None
103 Gradient Boosting Machines (GBM) NaN 1.0
104 Gradient Boosting Machines (GBM) NaN 0.0001
105 Gradient Boosting Machines (GBM) NaN 0.1
106 Gradient Boosting Machines (GBM) NaN 0
107 Gradient Boosting Machines (GBM) NaN False
108 Gradient Boosting Machines (GBM) 0.610906 NaN
109 Gradient Boosting Machines (GBM) 0.098663 NaN
110 Gradient Boosting Machines (GBM) 0.717593 NaN
111 Gradient Boosting Machines (GBM) 0.173475 NaN
112 Gradient Boosting Machines (GBM) 0.710280 NaN
113 Gradient Boosting Machines (GBM) 0.336489 NaN
114 Gradient Boosting Machines (GBM) 0.420559 NaN
115 Gradient Boosting Machines (GBM) NaN 0.0
116 Gradient Boosting Machines (GBM) NaN friedman_mse
117 Gradient Boosting Machines (GBM) NaN None
118 Gradient Boosting Machines (GBM) NaN 0.1
119 Gradient Boosting Machines (GBM) NaN log_loss
120 Gradient Boosting Machines (GBM) NaN 3
121 Gradient Boosting Machines (GBM) NaN None
122 Gradient Boosting Machines (GBM) NaN None
123 Gradient Boosting Machines (GBM) NaN 0.0
124 Gradient Boosting Machines (GBM) NaN 1
125 Gradient Boosting Machines (GBM) NaN 2
126 Gradient Boosting Machines (GBM) NaN 0.0
127 Gradient Boosting Machines (GBM) NaN 100
128 Gradient Boosting Machines (GBM) NaN None
129 Gradient Boosting Machines (GBM) NaN None
130 Gradient Boosting Machines (GBM) NaN 1.0
131 Gradient Boosting Machines (GBM) NaN 0.0001
132 Gradient Boosting Machines (GBM) NaN 0.1
133 Gradient Boosting Machines (GBM) NaN 0
134 Gradient Boosting Machines (GBM) NaN False
Imputation Technique Imbalance Class Technique \
0 Zero Imputation Undersampling
1 Zero Imputation Undersampling
2 Zero Imputation Undersampling
3 Zero Imputation Undersampling
4 Zero Imputation Undersampling
5 Zero Imputation Undersampling
6 Zero Imputation Undersampling
7 Zero Imputation Undersampling
8 Zero Imputation Undersampling
9 Zero Imputation Undersampling
10 Zero Imputation Undersampling
11 Zero Imputation Undersampling
12 Zero Imputation Undersampling
13 Zero Imputation Undersampling
14 Zero Imputation Undersampling
15 Zero Imputation Undersampling
16 Zero Imputation Undersampling
17 Zero Imputation Undersampling
18 Zero Imputation Undersampling
19 Zero Imputation Undersampling
20 Zero Imputation Undersampling
21 Zero Imputation Undersampling
22 Zero Imputation Undersampling
23 Zero Imputation Undersampling
24 Zero Imputation Undersampling
25 Zero Imputation Undersampling
26 Zero Imputation Undersampling
27 Zero Imputation Undersampling
28 Zero Imputation Undersampling
29 Zero Imputation Undersampling
30 Zero Imputation Undersampling
31 Zero Imputation Undersampling
32 Zero Imputation Undersampling
33 Zero Imputation Undersampling
34 Zero Imputation Undersampling
35 Zero Imputation Undersampling
36 Zero Imputation Undersampling
37 Zero Imputation Undersampling
38 Zero Imputation Undersampling
39 Zero Imputation Undersampling
40 Zero Imputation Undersampling
41 Zero Imputation Undersampling
42 Zero Imputation Undersampling
43 Zero Imputation Undersampling
44 Zero Imputation Undersampling
45 Zero Imputation Undersampling
46 Zero Imputation Undersampling
47 Zero Imputation Undersampling
48 Zero Imputation Undersampling
49 Zero Imputation Undersampling
50 Zero Imputation Undersampling
51 Zero Imputation Undersampling
52 Zero Imputation Undersampling
53 Zero Imputation Undersampling
54 Zero Imputation Undersampling
55 Zero Imputation Undersampling
56 Zero Imputation Undersampling
57 Zero Imputation Undersampling
58 Zero Imputation Undersampling
59 Zero Imputation Undersampling
60 Zero Imputation Undersampling
61 Zero Imputation Undersampling
62 Zero Imputation Undersampling
63 Zero Imputation Undersampling
64 Zero Imputation Undersampling
65 Zero Imputation Undersampling
66 Zero Imputation Undersampling
67 Zero Imputation Undersampling
68 Zero Imputation Undersampling
69 Zero Imputation Undersampling
70 Zero Imputation Undersampling
71 Zero Imputation Undersampling
72 Zero Imputation Undersampling
73 Zero Imputation Undersampling
74 Zero Imputation Undersampling
75 Zero Imputation Undersampling
76 Zero Imputation Undersampling
77 Zero Imputation Undersampling
78 Zero Imputation Undersampling
79 Zero Imputation Undersampling
80 Zero Imputation Undersampling
81 Zero Imputation Undersampling
82 Zero Imputation Undersampling
83 Zero Imputation Undersampling
84 Zero Imputation Undersampling
85 Zero Imputation Undersampling
86 Zero Imputation Undersampling
87 Zero Imputation Undersampling
88 Zero Imputation Undersampling
89 Zero Imputation Undersampling
90 Zero Imputation Undersampling
91 Zero Imputation Undersampling
92 Zero Imputation Undersampling
93 Zero Imputation Undersampling
94 Zero Imputation Undersampling
95 Zero Imputation Undersampling
96 Zero Imputation Undersampling
97 Zero Imputation Undersampling
98 Zero Imputation Undersampling
99 Zero Imputation Undersampling
100 Zero Imputation Undersampling
101 Zero Imputation Undersampling
102 Zero Imputation Undersampling
103 Zero Imputation Undersampling
104 Zero Imputation Undersampling
105 Zero Imputation Undersampling
106 Zero Imputation Undersampling
107 Zero Imputation Undersampling
108 Zero Imputation Undersampling
109 Zero Imputation Undersampling
110 Zero Imputation Undersampling
111 Zero Imputation Undersampling
112 Zero Imputation Undersampling
113 Zero Imputation Undersampling
114 Zero Imputation Undersampling
115 Zero Imputation Undersampling
116 Zero Imputation Undersampling
117 Zero Imputation Undersampling
118 Zero Imputation Undersampling
119 Zero Imputation Undersampling
120 Zero Imputation Undersampling
121 Zero Imputation Undersampling
122 Zero Imputation Undersampling
123 Zero Imputation Undersampling
124 Zero Imputation Undersampling
125 Zero Imputation Undersampling
126 Zero Imputation Undersampling
127 Zero Imputation Undersampling
128 Zero Imputation Undersampling
129 Zero Imputation Undersampling
130 Zero Imputation Undersampling
131 Zero Imputation Undersampling
132 Zero Imputation Undersampling
133 Zero Imputation Undersampling
134 Zero Imputation Undersampling
Model Unique Code
0 Gradient Boosting Machines (GBM)_Undersampling...
1 Gradient Boosting Machines (GBM)_Undersampling...
2 Gradient Boosting Machines (GBM)_Undersampling...
3 Gradient Boosting Machines (GBM)_Undersampling...
4 Gradient Boosting Machines (GBM)_Undersampling...
5 Gradient Boosting Machines (GBM)_Undersampling...
6 Gradient Boosting Machines (GBM)_Undersampling...
7 Gradient Boosting Machines (GBM)_Undersampling...
8 Gradient Boosting Machines (GBM)_Undersampling...
9 Gradient Boosting Machines (GBM)_Undersampling...
10 Gradient Boosting Machines (GBM)_Undersampling...
11 Gradient Boosting Machines (GBM)_Undersampling...
12 Gradient Boosting Machines (GBM)_Undersampling...
13 Gradient Boosting Machines (GBM)_Undersampling...
14 Gradient Boosting Machines (GBM)_Undersampling...
15 Gradient Boosting Machines (GBM)_Undersampling...
16 Gradient Boosting Machines (GBM)_Undersampling...
17 Gradient Boosting Machines (GBM)_Undersampling...
18 Gradient Boosting Machines (GBM)_Undersampling...
19 Gradient Boosting Machines (GBM)_Undersampling...
20 Gradient Boosting Machines (GBM)_Undersampling...
21 Gradient Boosting Machines (GBM)_Undersampling...
22 Gradient Boosting Machines (GBM)_Undersampling...
23 Gradient Boosting Machines (GBM)_Undersampling...
24 Gradient Boosting Machines (GBM)_Undersampling...
25 Gradient Boosting Machines (GBM)_Undersampling...
26 Gradient Boosting Machines (GBM)_Undersampling...
27 Gradient Boosting Machines (GBM)_Undersampling...
28 Gradient Boosting Machines (GBM)_Undersampling...
29 Gradient Boosting Machines (GBM)_Undersampling...
30 Gradient Boosting Machines (GBM)_Undersampling...
31 Gradient Boosting Machines (GBM)_Undersampling...
32 Gradient Boosting Machines (GBM)_Undersampling...
33 Gradient Boosting Machines (GBM)_Undersampling...
34 Gradient Boosting Machines (GBM)_Undersampling...
35 Gradient Boosting Machines (GBM)_Undersampling...
36 Gradient Boosting Machines (GBM)_Undersampling...
37 Gradient Boosting Machines (GBM)_Undersampling...
38 Gradient Boosting Machines (GBM)_Undersampling...
39 Gradient Boosting Machines (GBM)_Undersampling...
40 Gradient Boosting Machines (GBM)_Undersampling...
41 Gradient Boosting Machines (GBM)_Undersampling...
42 Gradient Boosting Machines (GBM)_Undersampling...
43 Gradient Boosting Machines (GBM)_Undersampling...
44 Gradient Boosting Machines (GBM)_Undersampling...
45 Gradient Boosting Machines (GBM)_Undersampling...
46 Gradient Boosting Machines (GBM)_Undersampling...
47 Gradient Boosting Machines (GBM)_Undersampling...
48 Gradient Boosting Machines (GBM)_Undersampling...
49 Gradient Boosting Machines (GBM)_Undersampling...
50 Gradient Boosting Machines (GBM)_Undersampling...
51 Gradient Boosting Machines (GBM)_Undersampling...
52 Gradient Boosting Machines (GBM)_Undersampling...
53 Gradient Boosting Machines (GBM)_Undersampling...
54 Gradient Boosting Machines (GBM)_Undersampling...
55 Gradient Boosting Machines (GBM)_Undersampling...
56 Gradient Boosting Machines (GBM)_Undersampling...
57 Gradient Boosting Machines (GBM)_Undersampling...
58 Gradient Boosting Machines (GBM)_Undersampling...
59 Gradient Boosting Machines (GBM)_Undersampling...
60 Gradient Boosting Machines (GBM)_Undersampling...
61 Gradient Boosting Machines (GBM)_Undersampling...
62 Gradient Boosting Machines (GBM)_Undersampling...
63 Gradient Boosting Machines (GBM)_Undersampling...
64 Gradient Boosting Machines (GBM)_Undersampling...
65 Gradient Boosting Machines (GBM)_Undersampling...
66 Gradient Boosting Machines (GBM)_Undersampling...
67 Gradient Boosting Machines (GBM)_Undersampling...
68 Gradient Boosting Machines (GBM)_Undersampling...
69 Gradient Boosting Machines (GBM)_Undersampling...
70 Gradient Boosting Machines (GBM)_Undersampling...
71 Gradient Boosting Machines (GBM)_Undersampling...
72 Gradient Boosting Machines (GBM)_Undersampling...
73 Gradient Boosting Machines (GBM)_Undersampling...
74 Gradient Boosting Machines (GBM)_Undersampling...
75 Gradient Boosting Machines (GBM)_Undersampling...
76 Gradient Boosting Machines (GBM)_Undersampling...
77 Gradient Boosting Machines (GBM)_Undersampling...
78 Gradient Boosting Machines (GBM)_Undersampling...
79 Gradient Boosting Machines (GBM)_Undersampling...
80 Gradient Boosting Machines (GBM)_Undersampling...
81 Gradient Boosting Machines (GBM)_Undersampling...
82 Gradient Boosting Machines (GBM)_Undersampling...
83 Gradient Boosting Machines (GBM)_Undersampling...
84 Gradient Boosting Machines (GBM)_Undersampling...
85 Gradient Boosting Machines (GBM)_Undersampling...
86 Gradient Boosting Machines (GBM)_Undersampling...
87 Gradient Boosting Machines (GBM)_Undersampling...
88 Gradient Boosting Machines (GBM)_Undersampling...
89 Gradient Boosting Machines (GBM)_Undersampling...
90 Gradient Boosting Machines (GBM)_Undersampling...
91 Gradient Boosting Machines (GBM)_Undersampling...
92 Gradient Boosting Machines (GBM)_Undersampling...
93 Gradient Boosting Machines (GBM)_Undersampling...
94 Gradient Boosting Machines (GBM)_Undersampling...
95 Gradient Boosting Machines (GBM)_Undersampling...
96 Gradient Boosting Machines (GBM)_Undersampling...
97 Gradient Boosting Machines (GBM)_Undersampling...
98 Gradient Boosting Machines (GBM)_Undersampling...
99 Gradient Boosting Machines (GBM)_Undersampling...
100 Gradient Boosting Machines (GBM)_Undersampling...
101 Gradient Boosting Machines (GBM)_Undersampling...
102 Gradient Boosting Machines (GBM)_Undersampling...
103 Gradient Boosting Machines (GBM)_Undersampling...
104 Gradient Boosting Machines (GBM)_Undersampling...
105 Gradient Boosting Machines (GBM)_Undersampling...
106 Gradient Boosting Machines (GBM)_Undersampling...
107 Gradient Boosting Machines (GBM)_Undersampling...
108 Gradient Boosting Machines (GBM)_Undersampling...
109 Gradient Boosting Machines (GBM)_Undersampling...
110 Gradient Boosting Machines (GBM)_Undersampling...
111 Gradient Boosting Machines (GBM)_Undersampling...
112 Gradient Boosting Machines (GBM)_Undersampling...
113 Gradient Boosting Machines (GBM)_Undersampling...
114 Gradient Boosting Machines (GBM)_Undersampling...
115 Gradient Boosting Machines (GBM)_Undersampling...
116 Gradient Boosting Machines (GBM)_Undersampling...
117 Gradient Boosting Machines (GBM)_Undersampling...
118 Gradient Boosting Machines (GBM)_Undersampling...
119 Gradient Boosting Machines (GBM)_Undersampling...
120 Gradient Boosting Machines (GBM)_Undersampling...
121 Gradient Boosting Machines (GBM)_Undersampling...
122 Gradient Boosting Machines (GBM)_Undersampling...
123 Gradient Boosting Machines (GBM)_Undersampling...
124 Gradient Boosting Machines (GBM)_Undersampling...
125 Gradient Boosting Machines (GBM)_Undersampling...
126 Gradient Boosting Machines (GBM)_Undersampling...
127 Gradient Boosting Machines (GBM)_Undersampling...
128 Gradient Boosting Machines (GBM)_Undersampling...
129 Gradient Boosting Machines (GBM)_Undersampling...
130 Gradient Boosting Machines (GBM)_Undersampling...
131 Gradient Boosting Machines (GBM)_Undersampling...
132 Gradient Boosting Machines (GBM)_Undersampling...
133 Gradient Boosting Machines (GBM)_Undersampling...
134 Gradient Boosting Machines (GBM)_Undersampling... ,
Algorithm Metrics Hyperparameters \
0 Gradient Boosting Machines (GBM) 0.621543 NaN
1 Gradient Boosting Machines (GBM) 0.103175 NaN
2 Gradient Boosting Machines (GBM) 0.658228 NaN
3 Gradient Boosting Machines (GBM) 0.178388 NaN
4 Gradient Boosting Machines (GBM) 0.691887 NaN
5 Gradient Boosting Machines (GBM) 0.294703 NaN
6 Gradient Boosting Machines (GBM) 0.383773 NaN
7 Gradient Boosting Machines (GBM) NaN 0.0
8 Gradient Boosting Machines (GBM) NaN friedman_mse
9 Gradient Boosting Machines (GBM) NaN None
10 Gradient Boosting Machines (GBM) NaN 0.1
11 Gradient Boosting Machines (GBM) NaN log_loss
12 Gradient Boosting Machines (GBM) NaN 3
13 Gradient Boosting Machines (GBM) NaN None
14 Gradient Boosting Machines (GBM) NaN None
15 Gradient Boosting Machines (GBM) NaN 0.0
16 Gradient Boosting Machines (GBM) NaN 1
17 Gradient Boosting Machines (GBM) NaN 2
18 Gradient Boosting Machines (GBM) NaN 0.0
19 Gradient Boosting Machines (GBM) NaN 100
20 Gradient Boosting Machines (GBM) NaN None
21 Gradient Boosting Machines (GBM) NaN None
22 Gradient Boosting Machines (GBM) NaN 1.0
23 Gradient Boosting Machines (GBM) NaN 0.0001
24 Gradient Boosting Machines (GBM) NaN 0.1
25 Gradient Boosting Machines (GBM) NaN 0
26 Gradient Boosting Machines (GBM) NaN False
27 Gradient Boosting Machines (GBM) 0.622860 NaN
28 Gradient Boosting Machines (GBM) 0.075067 NaN
29 Gradient Boosting Machines (GBM) 0.682927 NaN
30 Gradient Boosting Machines (GBM) 0.135266 NaN
31 Gradient Boosting Machines (GBM) 0.698037 NaN
32 Gradient Boosting Machines (GBM) 0.342400 NaN
33 Gradient Boosting Machines (GBM) 0.396075 NaN
34 Gradient Boosting Machines (GBM) NaN 0.0
35 Gradient Boosting Machines (GBM) NaN friedman_mse
36 Gradient Boosting Machines (GBM) NaN None
37 Gradient Boosting Machines (GBM) NaN 0.1
38 Gradient Boosting Machines (GBM) NaN log_loss
39 Gradient Boosting Machines (GBM) NaN 3
40 Gradient Boosting Machines (GBM) NaN None
41 Gradient Boosting Machines (GBM) NaN None
42 Gradient Boosting Machines (GBM) NaN 0.0
43 Gradient Boosting Machines (GBM) NaN 1
44 Gradient Boosting Machines (GBM) NaN 2
45 Gradient Boosting Machines (GBM) NaN 0.0
46 Gradient Boosting Machines (GBM) NaN 100
47 Gradient Boosting Machines (GBM) NaN None
48 Gradient Boosting Machines (GBM) NaN None
49 Gradient Boosting Machines (GBM) NaN 1.0
50 Gradient Boosting Machines (GBM) NaN 0.0001
51 Gradient Boosting Machines (GBM) NaN 0.1
52 Gradient Boosting Machines (GBM) NaN 0
53 Gradient Boosting Machines (GBM) NaN False
54 Gradient Boosting Machines (GBM) 0.612852 NaN
55 Gradient Boosting Machines (GBM) 0.079344 NaN
56 Gradient Boosting Machines (GBM) 0.647059 NaN
57 Gradient Boosting Machines (GBM) 0.141355 NaN
58 Gradient Boosting Machines (GBM) 0.677596 NaN
59 Gradient Boosting Machines (GBM) 0.271317 NaN
60 Gradient Boosting Machines (GBM) 0.355193 NaN
61 Gradient Boosting Machines (GBM) NaN 0.0
62 Gradient Boosting Machines (GBM) NaN friedman_mse
63 Gradient Boosting Machines (GBM) NaN None
64 Gradient Boosting Machines (GBM) NaN 0.1
65 Gradient Boosting Machines (GBM) NaN log_loss
66 Gradient Boosting Machines (GBM) NaN 3
67 Gradient Boosting Machines (GBM) NaN None
68 Gradient Boosting Machines (GBM) NaN None
69 Gradient Boosting Machines (GBM) NaN 0.0
70 Gradient Boosting Machines (GBM) NaN 1
71 Gradient Boosting Machines (GBM) NaN 2
72 Gradient Boosting Machines (GBM) NaN 0.0
73 Gradient Boosting Machines (GBM) NaN 100
74 Gradient Boosting Machines (GBM) NaN None
75 Gradient Boosting Machines (GBM) NaN None
76 Gradient Boosting Machines (GBM) NaN 1.0
77 Gradient Boosting Machines (GBM) NaN 0.0001
78 Gradient Boosting Machines (GBM) NaN 0.1
79 Gradient Boosting Machines (GBM) NaN 0
80 Gradient Boosting Machines (GBM) NaN False
81 Gradient Boosting Machines (GBM) 0.614331 NaN
82 Gradient Boosting Machines (GBM) 0.092545 NaN
83 Gradient Boosting Machines (GBM) 0.734694 NaN
84 Gradient Boosting Machines (GBM) 0.164384 NaN
85 Gradient Boosting Machines (GBM) 0.705125 NaN
86 Gradient Boosting Machines (GBM) 0.349314 NaN
87 Gradient Boosting Machines (GBM) 0.410249 NaN
88 Gradient Boosting Machines (GBM) NaN 0.0
89 Gradient Boosting Machines (GBM) NaN friedman_mse
90 Gradient Boosting Machines (GBM) NaN None
91 Gradient Boosting Machines (GBM) NaN 0.1
92 Gradient Boosting Machines (GBM) NaN log_loss
93 Gradient Boosting Machines (GBM) NaN 3
94 Gradient Boosting Machines (GBM) NaN None
95 Gradient Boosting Machines (GBM) NaN None
96 Gradient Boosting Machines (GBM) NaN 0.0
97 Gradient Boosting Machines (GBM) NaN 1
98 Gradient Boosting Machines (GBM) NaN 2
99 Gradient Boosting Machines (GBM) NaN 0.0
100 Gradient Boosting Machines (GBM) NaN 100
101 Gradient Boosting Machines (GBM) NaN None
102 Gradient Boosting Machines (GBM) NaN None
103 Gradient Boosting Machines (GBM) NaN 1.0
104 Gradient Boosting Machines (GBM) NaN 0.0001
105 Gradient Boosting Machines (GBM) NaN 0.1
106 Gradient Boosting Machines (GBM) NaN 0
107 Gradient Boosting Machines (GBM) NaN False
108 Gradient Boosting Machines (GBM) 0.627766 NaN
109 Gradient Boosting Machines (GBM) 0.100734 NaN
110 Gradient Boosting Machines (GBM) 0.699074 NaN
111 Gradient Boosting Machines (GBM) 0.176093 NaN
112 Gradient Boosting Machines (GBM) 0.711324 NaN
113 Gradient Boosting Machines (GBM) 0.326407 NaN
114 Gradient Boosting Machines (GBM) 0.422649 NaN
115 Gradient Boosting Machines (GBM) NaN 0.0
116 Gradient Boosting Machines (GBM) NaN friedman_mse
117 Gradient Boosting Machines (GBM) NaN None
118 Gradient Boosting Machines (GBM) NaN 0.1
119 Gradient Boosting Machines (GBM) NaN log_loss
120 Gradient Boosting Machines (GBM) NaN 3
121 Gradient Boosting Machines (GBM) NaN None
122 Gradient Boosting Machines (GBM) NaN None
123 Gradient Boosting Machines (GBM) NaN 0.0
124 Gradient Boosting Machines (GBM) NaN 1
125 Gradient Boosting Machines (GBM) NaN 2
126 Gradient Boosting Machines (GBM) NaN 0.0
127 Gradient Boosting Machines (GBM) NaN 100
128 Gradient Boosting Machines (GBM) NaN None
129 Gradient Boosting Machines (GBM) NaN None
130 Gradient Boosting Machines (GBM) NaN 1.0
131 Gradient Boosting Machines (GBM) NaN 0.0001
132 Gradient Boosting Machines (GBM) NaN 0.1
133 Gradient Boosting Machines (GBM) NaN 0
134 Gradient Boosting Machines (GBM) NaN False
Imputation Technique Imbalance Class Technique \
0 Mean Imputation Undersampling
1 Mean Imputation Undersampling
2 Mean Imputation Undersampling
3 Mean Imputation Undersampling
4 Mean Imputation Undersampling
5 Mean Imputation Undersampling
6 Mean Imputation Undersampling
7 Mean Imputation Undersampling
8 Mean Imputation Undersampling
9 Mean Imputation Undersampling
10 Mean Imputation Undersampling
11 Mean Imputation Undersampling
12 Mean Imputation Undersampling
13 Mean Imputation Undersampling
14 Mean Imputation Undersampling
15 Mean Imputation Undersampling
16 Mean Imputation Undersampling
17 Mean Imputation Undersampling
18 Mean Imputation Undersampling
19 Mean Imputation Undersampling
20 Mean Imputation Undersampling
21 Mean Imputation Undersampling
22 Mean Imputation Undersampling
23 Mean Imputation Undersampling
24 Mean Imputation Undersampling
25 Mean Imputation Undersampling
26 Mean Imputation Undersampling
27 Mean Imputation Undersampling
28 Mean Imputation Undersampling
29 Mean Imputation Undersampling
30 Mean Imputation Undersampling
31 Mean Imputation Undersampling
32 Mean Imputation Undersampling
33 Mean Imputation Undersampling
34 Mean Imputation Undersampling
35 Mean Imputation Undersampling
36 Mean Imputation Undersampling
37 Mean Imputation Undersampling
38 Mean Imputation Undersampling
39 Mean Imputation Undersampling
40 Mean Imputation Undersampling
41 Mean Imputation Undersampling
42 Mean Imputation Undersampling
43 Mean Imputation Undersampling
44 Mean Imputation Undersampling
45 Mean Imputation Undersampling
46 Mean Imputation Undersampling
47 Mean Imputation Undersampling
48 Mean Imputation Undersampling
49 Mean Imputation Undersampling
50 Mean Imputation Undersampling
51 Mean Imputation Undersampling
52 Mean Imputation Undersampling
53 Mean Imputation Undersampling
54 Mean Imputation Undersampling
55 Mean Imputation Undersampling
56 Mean Imputation Undersampling
57 Mean Imputation Undersampling
58 Mean Imputation Undersampling
59 Mean Imputation Undersampling
60 Mean Imputation Undersampling
61 Mean Imputation Undersampling
62 Mean Imputation Undersampling
63 Mean Imputation Undersampling
64 Mean Imputation Undersampling
65 Mean Imputation Undersampling
66 Mean Imputation Undersampling
67 Mean Imputation Undersampling
68 Mean Imputation Undersampling
69 Mean Imputation Undersampling
70 Mean Imputation Undersampling
71 Mean Imputation Undersampling
72 Mean Imputation Undersampling
73 Mean Imputation Undersampling
74 Mean Imputation Undersampling
75 Mean Imputation Undersampling
76 Mean Imputation Undersampling
77 Mean Imputation Undersampling
78 Mean Imputation Undersampling
79 Mean Imputation Undersampling
80 Mean Imputation Undersampling
81 Mean Imputation Undersampling
82 Mean Imputation Undersampling
83 Mean Imputation Undersampling
84 Mean Imputation Undersampling
85 Mean Imputation Undersampling
86 Mean Imputation Undersampling
87 Mean Imputation Undersampling
88 Mean Imputation Undersampling
89 Mean Imputation Undersampling
90 Mean Imputation Undersampling
91 Mean Imputation Undersampling
92 Mean Imputation Undersampling
93 Mean Imputation Undersampling
94 Mean Imputation Undersampling
95 Mean Imputation Undersampling
96 Mean Imputation Undersampling
97 Mean Imputation Undersampling
98 Mean Imputation Undersampling
99 Mean Imputation Undersampling
100 Mean Imputation Undersampling
101 Mean Imputation Undersampling
102 Mean Imputation Undersampling
103 Mean Imputation Undersampling
104 Mean Imputation Undersampling
105 Mean Imputation Undersampling
106 Mean Imputation Undersampling
107 Mean Imputation Undersampling
108 Mean Imputation Undersampling
109 Mean Imputation Undersampling
110 Mean Imputation Undersampling
111 Mean Imputation Undersampling
112 Mean Imputation Undersampling
113 Mean Imputation Undersampling
114 Mean Imputation Undersampling
115 Mean Imputation Undersampling
116 Mean Imputation Undersampling
117 Mean Imputation Undersampling
118 Mean Imputation Undersampling
119 Mean Imputation Undersampling
120 Mean Imputation Undersampling
121 Mean Imputation Undersampling
122 Mean Imputation Undersampling
123 Mean Imputation Undersampling
124 Mean Imputation Undersampling
125 Mean Imputation Undersampling
126 Mean Imputation Undersampling
127 Mean Imputation Undersampling
128 Mean Imputation Undersampling
129 Mean Imputation Undersampling
130 Mean Imputation Undersampling
131 Mean Imputation Undersampling
132 Mean Imputation Undersampling
133 Mean Imputation Undersampling
134 Mean Imputation Undersampling
Model Unique Code
0 Gradient Boosting Machines (GBM)_Undersampling...
1 Gradient Boosting Machines (GBM)_Undersampling...
2 Gradient Boosting Machines (GBM)_Undersampling...
3 Gradient Boosting Machines (GBM)_Undersampling...
4 Gradient Boosting Machines (GBM)_Undersampling...
5 Gradient Boosting Machines (GBM)_Undersampling...
6 Gradient Boosting Machines (GBM)_Undersampling...
7 Gradient Boosting Machines (GBM)_Undersampling...
8 Gradient Boosting Machines (GBM)_Undersampling...
9 Gradient Boosting Machines (GBM)_Undersampling...
10 Gradient Boosting Machines (GBM)_Undersampling...
11 Gradient Boosting Machines (GBM)_Undersampling...
12 Gradient Boosting Machines (GBM)_Undersampling...
13 Gradient Boosting Machines (GBM)_Undersampling...
14 Gradient Boosting Machines (GBM)_Undersampling...
15 Gradient Boosting Machines (GBM)_Undersampling...
16 Gradient Boosting Machines (GBM)_Undersampling...
17 Gradient Boosting Machines (GBM)_Undersampling...
18 Gradient Boosting Machines (GBM)_Undersampling...
19 Gradient Boosting Machines (GBM)_Undersampling...
20 Gradient Boosting Machines (GBM)_Undersampling...
21 Gradient Boosting Machines (GBM)_Undersampling...
22 Gradient Boosting Machines (GBM)_Undersampling...
23 Gradient Boosting Machines (GBM)_Undersampling...
24 Gradient Boosting Machines (GBM)_Undersampling...
25 Gradient Boosting Machines (GBM)_Undersampling...
26 Gradient Boosting Machines (GBM)_Undersampling...
27 Gradient Boosting Machines (GBM)_Undersampling...
28 Gradient Boosting Machines (GBM)_Undersampling...
29 Gradient Boosting Machines (GBM)_Undersampling...
30 Gradient Boosting Machines (GBM)_Undersampling...
31 Gradient Boosting Machines (GBM)_Undersampling...
32 Gradient Boosting Machines (GBM)_Undersampling...
33 Gradient Boosting Machines (GBM)_Undersampling...
34 Gradient Boosting Machines (GBM)_Undersampling...
35 Gradient Boosting Machines (GBM)_Undersampling...
36 Gradient Boosting Machines (GBM)_Undersampling...
37 Gradient Boosting Machines (GBM)_Undersampling...
38 Gradient Boosting Machines (GBM)_Undersampling...
39 Gradient Boosting Machines (GBM)_Undersampling...
40 Gradient Boosting Machines (GBM)_Undersampling...
41 Gradient Boosting Machines (GBM)_Undersampling...
42 Gradient Boosting Machines (GBM)_Undersampling...
43 Gradient Boosting Machines (GBM)_Undersampling...
44 Gradient Boosting Machines (GBM)_Undersampling...
45 Gradient Boosting Machines (GBM)_Undersampling...
46 Gradient Boosting Machines (GBM)_Undersampling...
47 Gradient Boosting Machines (GBM)_Undersampling...
48 Gradient Boosting Machines (GBM)_Undersampling...
49 Gradient Boosting Machines (GBM)_Undersampling...
50 Gradient Boosting Machines (GBM)_Undersampling...
51 Gradient Boosting Machines (GBM)_Undersampling...
52 Gradient Boosting Machines (GBM)_Undersampling...
53 Gradient Boosting Machines (GBM)_Undersampling...
54 Gradient Boosting Machines (GBM)_Undersampling...
55 Gradient Boosting Machines (GBM)_Undersampling...
56 Gradient Boosting Machines (GBM)_Undersampling...
57 Gradient Boosting Machines (GBM)_Undersampling...
58 Gradient Boosting Machines (GBM)_Undersampling...
59 Gradient Boosting Machines (GBM)_Undersampling...
60 Gradient Boosting Machines (GBM)_Undersampling...
61 Gradient Boosting Machines (GBM)_Undersampling...
62 Gradient Boosting Machines (GBM)_Undersampling...
63 Gradient Boosting Machines (GBM)_Undersampling...
64 Gradient Boosting Machines (GBM)_Undersampling...
65 Gradient Boosting Machines (GBM)_Undersampling...
66 Gradient Boosting Machines (GBM)_Undersampling...
67 Gradient Boosting Machines (GBM)_Undersampling...
68 Gradient Boosting Machines (GBM)_Undersampling...
69 Gradient Boosting Machines (GBM)_Undersampling...
70 Gradient Boosting Machines (GBM)_Undersampling...
71 Gradient Boosting Machines (GBM)_Undersampling...
72 Gradient Boosting Machines (GBM)_Undersampling...
73 Gradient Boosting Machines (GBM)_Undersampling...
74 Gradient Boosting Machines (GBM)_Undersampling...
75 Gradient Boosting Machines (GBM)_Undersampling...
76 Gradient Boosting Machines (GBM)_Undersampling...
77 Gradient Boosting Machines (GBM)_Undersampling...
78 Gradient Boosting Machines (GBM)_Undersampling...
79 Gradient Boosting Machines (GBM)_Undersampling...
80 Gradient Boosting Machines (GBM)_Undersampling...
81 Gradient Boosting Machines (GBM)_Undersampling...
82 Gradient Boosting Machines (GBM)_Undersampling...
83 Gradient Boosting Machines (GBM)_Undersampling...
84 Gradient Boosting Machines (GBM)_Undersampling...
85 Gradient Boosting Machines (GBM)_Undersampling...
86 Gradient Boosting Machines (GBM)_Undersampling...
87 Gradient Boosting Machines (GBM)_Undersampling...
88 Gradient Boosting Machines (GBM)_Undersampling...
89 Gradient Boosting Machines (GBM)_Undersampling...
90 Gradient Boosting Machines (GBM)_Undersampling...
91 Gradient Boosting Machines (GBM)_Undersampling...
92 Gradient Boosting Machines (GBM)_Undersampling...
93 Gradient Boosting Machines (GBM)_Undersampling...
94 Gradient Boosting Machines (GBM)_Undersampling...
95 Gradient Boosting Machines (GBM)_Undersampling...
96 Gradient Boosting Machines (GBM)_Undersampling...
97 Gradient Boosting Machines (GBM)_Undersampling...
98 Gradient Boosting Machines (GBM)_Undersampling...
99 Gradient Boosting Machines (GBM)_Undersampling...
100 Gradient Boosting Machines (GBM)_Undersampling...
101 Gradient Boosting Machines (GBM)_Undersampling...
102 Gradient Boosting Machines (GBM)_Undersampling...
103 Gradient Boosting Machines (GBM)_Undersampling...
104 Gradient Boosting Machines (GBM)_Undersampling...
105 Gradient Boosting Machines (GBM)_Undersampling...
106 Gradient Boosting Machines (GBM)_Undersampling...
107 Gradient Boosting Machines (GBM)_Undersampling...
108 Gradient Boosting Machines (GBM)_Undersampling...
109 Gradient Boosting Machines (GBM)_Undersampling...
110 Gradient Boosting Machines (GBM)_Undersampling...
111 Gradient Boosting Machines (GBM)_Undersampling...
112 Gradient Boosting Machines (GBM)_Undersampling...
113 Gradient Boosting Machines (GBM)_Undersampling...
114 Gradient Boosting Machines (GBM)_Undersampling...
115 Gradient Boosting Machines (GBM)_Undersampling...
116 Gradient Boosting Machines (GBM)_Undersampling...
117 Gradient Boosting Machines (GBM)_Undersampling...
118 Gradient Boosting Machines (GBM)_Undersampling...
119 Gradient Boosting Machines (GBM)_Undersampling...
120 Gradient Boosting Machines (GBM)_Undersampling...
121 Gradient Boosting Machines (GBM)_Undersampling...
122 Gradient Boosting Machines (GBM)_Undersampling...
123 Gradient Boosting Machines (GBM)_Undersampling...
124 Gradient Boosting Machines (GBM)_Undersampling...
125 Gradient Boosting Machines (GBM)_Undersampling...
126 Gradient Boosting Machines (GBM)_Undersampling...
127 Gradient Boosting Machines (GBM)_Undersampling...
128 Gradient Boosting Machines (GBM)_Undersampling...
129 Gradient Boosting Machines (GBM)_Undersampling...
130 Gradient Boosting Machines (GBM)_Undersampling...
131 Gradient Boosting Machines (GBM)_Undersampling...
132 Gradient Boosting Machines (GBM)_Undersampling...
133 Gradient Boosting Machines (GBM)_Undersampling...
134 Gradient Boosting Machines (GBM)_Undersampling... ,
Algorithm Metrics Hyperparameters \
0 Gradient Boosting Machines (GBM) 0.629181 NaN
1 Gradient Boosting Machines (GBM) 0.104124 NaN
2 Gradient Boosting Machines (GBM) 0.649789 NaN
3 Gradient Boosting Machines (GBM) 0.179487 NaN
4 Gradient Boosting Machines (GBM) 0.691467 NaN
5 Gradient Boosting Machines (GBM) 0.292169 NaN
6 Gradient Boosting Machines (GBM) 0.382934 NaN
7 Gradient Boosting Machines (GBM) NaN 0.0
8 Gradient Boosting Machines (GBM) NaN friedman_mse
9 Gradient Boosting Machines (GBM) NaN None
10 Gradient Boosting Machines (GBM) NaN 0.1
11 Gradient Boosting Machines (GBM) NaN log_loss
12 Gradient Boosting Machines (GBM) NaN 3
13 Gradient Boosting Machines (GBM) NaN None
14 Gradient Boosting Machines (GBM) NaN None
15 Gradient Boosting Machines (GBM) NaN 0.0
16 Gradient Boosting Machines (GBM) NaN 1
17 Gradient Boosting Machines (GBM) NaN 2
18 Gradient Boosting Machines (GBM) NaN 0.0
19 Gradient Boosting Machines (GBM) NaN 100
20 Gradient Boosting Machines (GBM) NaN None
21 Gradient Boosting Machines (GBM) NaN None
22 Gradient Boosting Machines (GBM) NaN 1.0
23 Gradient Boosting Machines (GBM) NaN 0.0001
24 Gradient Boosting Machines (GBM) NaN 0.1
25 Gradient Boosting Machines (GBM) NaN 0
26 Gradient Boosting Machines (GBM) NaN False
27 Gradient Boosting Machines (GBM) 0.597050 NaN
28 Gradient Boosting Machines (GBM) 0.071518 NaN
29 Gradient Boosting Machines (GBM) 0.695122 NaN
30 Gradient Boosting Machines (GBM) 0.129693 NaN
31 Gradient Boosting Machines (GBM) 0.695282 NaN
32 Gradient Boosting Machines (GBM) 0.314067 NaN
33 Gradient Boosting Machines (GBM) 0.390564 NaN
34 Gradient Boosting Machines (GBM) NaN 0.0
35 Gradient Boosting Machines (GBM) NaN friedman_mse
36 Gradient Boosting Machines (GBM) NaN None
37 Gradient Boosting Machines (GBM) NaN 0.1
38 Gradient Boosting Machines (GBM) NaN log_loss
39 Gradient Boosting Machines (GBM) NaN 3
40 Gradient Boosting Machines (GBM) NaN None
41 Gradient Boosting Machines (GBM) NaN None
42 Gradient Boosting Machines (GBM) NaN 0.0
43 Gradient Boosting Machines (GBM) NaN 1
44 Gradient Boosting Machines (GBM) NaN 2
45 Gradient Boosting Machines (GBM) NaN 0.0
46 Gradient Boosting Machines (GBM) NaN 100
47 Gradient Boosting Machines (GBM) NaN None
48 Gradient Boosting Machines (GBM) NaN None
49 Gradient Boosting Machines (GBM) NaN 1.0
50 Gradient Boosting Machines (GBM) NaN 0.0001
51 Gradient Boosting Machines (GBM) NaN 0.1
52 Gradient Boosting Machines (GBM) NaN 0
53 Gradient Boosting Machines (GBM) NaN False
54 Gradient Boosting Machines (GBM) 0.584145 NaN
55 Gradient Boosting Machines (GBM) 0.080723 NaN
56 Gradient Boosting Machines (GBM) 0.716578 NaN
57 Gradient Boosting Machines (GBM) 0.145100 NaN
58 Gradient Boosting Machines (GBM) 0.694298 NaN
59 Gradient Boosting Machines (GBM) 0.306750 NaN
60 Gradient Boosting Machines (GBM) 0.388597 NaN
61 Gradient Boosting Machines (GBM) NaN 0.0
62 Gradient Boosting Machines (GBM) NaN friedman_mse
63 Gradient Boosting Machines (GBM) NaN None
64 Gradient Boosting Machines (GBM) NaN 0.1
65 Gradient Boosting Machines (GBM) NaN log_loss
66 Gradient Boosting Machines (GBM) NaN 3
67 Gradient Boosting Machines (GBM) NaN None
68 Gradient Boosting Machines (GBM) NaN None
69 Gradient Boosting Machines (GBM) NaN 0.0
70 Gradient Boosting Machines (GBM) NaN 1
71 Gradient Boosting Machines (GBM) NaN 2
72 Gradient Boosting Machines (GBM) NaN 0.0
73 Gradient Boosting Machines (GBM) NaN 100
74 Gradient Boosting Machines (GBM) NaN None
75 Gradient Boosting Machines (GBM) NaN None
76 Gradient Boosting Machines (GBM) NaN 1.0
77 Gradient Boosting Machines (GBM) NaN 0.0001
78 Gradient Boosting Machines (GBM) NaN 0.1
79 Gradient Boosting Machines (GBM) NaN 0
80 Gradient Boosting Machines (GBM) NaN False
81 Gradient Boosting Machines (GBM) 0.613277 NaN
82 Gradient Boosting Machines (GBM) 0.087549 NaN
83 Gradient Boosting Machines (GBM) 0.688776 NaN
84 Gradient Boosting Machines (GBM) 0.155351 NaN
85 Gradient Boosting Machines (GBM) 0.703506 NaN
86 Gradient Boosting Machines (GBM) 0.329615 NaN
87 Gradient Boosting Machines (GBM) 0.407011 NaN
88 Gradient Boosting Machines (GBM) NaN 0.0
89 Gradient Boosting Machines (GBM) NaN friedman_mse
90 Gradient Boosting Machines (GBM) NaN None
91 Gradient Boosting Machines (GBM) NaN 0.1
92 Gradient Boosting Machines (GBM) NaN log_loss
93 Gradient Boosting Machines (GBM) NaN 3
94 Gradient Boosting Machines (GBM) NaN None
95 Gradient Boosting Machines (GBM) NaN None
96 Gradient Boosting Machines (GBM) NaN 0.0
97 Gradient Boosting Machines (GBM) NaN 1
98 Gradient Boosting Machines (GBM) NaN 2
99 Gradient Boosting Machines (GBM) NaN 0.0
100 Gradient Boosting Machines (GBM) NaN 100
101 Gradient Boosting Machines (GBM) NaN None
102 Gradient Boosting Machines (GBM) NaN None
103 Gradient Boosting Machines (GBM) NaN 1.0
104 Gradient Boosting Machines (GBM) NaN 0.0001
105 Gradient Boosting Machines (GBM) NaN 0.1
106 Gradient Boosting Machines (GBM) NaN 0
107 Gradient Boosting Machines (GBM) NaN False
108 Gradient Boosting Machines (GBM) 0.639621 NaN
109 Gradient Boosting Machines (GBM) 0.101108 NaN
110 Gradient Boosting Machines (GBM) 0.675926 NaN
111 Gradient Boosting Machines (GBM) 0.175904 NaN
112 Gradient Boosting Machines (GBM) 0.711200 NaN
113 Gradient Boosting Machines (GBM) 0.328171 NaN
114 Gradient Boosting Machines (GBM) 0.422399 NaN
115 Gradient Boosting Machines (GBM) NaN 0.0
116 Gradient Boosting Machines (GBM) NaN friedman_mse
117 Gradient Boosting Machines (GBM) NaN None
118 Gradient Boosting Machines (GBM) NaN 0.1
119 Gradient Boosting Machines (GBM) NaN log_loss
120 Gradient Boosting Machines (GBM) NaN 3
121 Gradient Boosting Machines (GBM) NaN None
122 Gradient Boosting Machines (GBM) NaN None
123 Gradient Boosting Machines (GBM) NaN 0.0
124 Gradient Boosting Machines (GBM) NaN 1
125 Gradient Boosting Machines (GBM) NaN 2
126 Gradient Boosting Machines (GBM) NaN 0.0
127 Gradient Boosting Machines (GBM) NaN 100
128 Gradient Boosting Machines (GBM) NaN None
129 Gradient Boosting Machines (GBM) NaN None
130 Gradient Boosting Machines (GBM) NaN 1.0
131 Gradient Boosting Machines (GBM) NaN 0.0001
132 Gradient Boosting Machines (GBM) NaN 0.1
133 Gradient Boosting Machines (GBM) NaN 0
134 Gradient Boosting Machines (GBM) NaN False
Imputation Technique Imbalance Class Technique \
0 Median Imputation Undersampling
1 Median Imputation Undersampling
2 Median Imputation Undersampling
3 Median Imputation Undersampling
4 Median Imputation Undersampling
5 Median Imputation Undersampling
6 Median Imputation Undersampling
7 Median Imputation Undersampling
8 Median Imputation Undersampling
9 Median Imputation Undersampling
10 Median Imputation Undersampling
11 Median Imputation Undersampling
12 Median Imputation Undersampling
13 Median Imputation Undersampling
14 Median Imputation Undersampling
15 Median Imputation Undersampling
16 Median Imputation Undersampling
17 Median Imputation Undersampling
18 Median Imputation Undersampling
19 Median Imputation Undersampling
20 Median Imputation Undersampling
21 Median Imputation Undersampling
22 Median Imputation Undersampling
23 Median Imputation Undersampling
24 Median Imputation Undersampling
25 Median Imputation Undersampling
26 Median Imputation Undersampling
27 Median Imputation Undersampling
28 Median Imputation Undersampling
29 Median Imputation Undersampling
30 Median Imputation Undersampling
31 Median Imputation Undersampling
32 Median Imputation Undersampling
33 Median Imputation Undersampling
34 Median Imputation Undersampling
35 Median Imputation Undersampling
36 Median Imputation Undersampling
37 Median Imputation Undersampling
38 Median Imputation Undersampling
39 Median Imputation Undersampling
40 Median Imputation Undersampling
41 Median Imputation Undersampling
42 Median Imputation Undersampling
43 Median Imputation Undersampling
44 Median Imputation Undersampling
45 Median Imputation Undersampling
46 Median Imputation Undersampling
47 Median Imputation Undersampling
48 Median Imputation Undersampling
49 Median Imputation Undersampling
50 Median Imputation Undersampling
51 Median Imputation Undersampling
52 Median Imputation Undersampling
53 Median Imputation Undersampling
54 Median Imputation Undersampling
55 Median Imputation Undersampling
56 Median Imputation Undersampling
57 Median Imputation Undersampling
58 Median Imputation Undersampling
59 Median Imputation Undersampling
60 Median Imputation Undersampling
61 Median Imputation Undersampling
62 Median Imputation Undersampling
63 Median Imputation Undersampling
64 Median Imputation Undersampling
65 Median Imputation Undersampling
66 Median Imputation Undersampling
67 Median Imputation Undersampling
68 Median Imputation Undersampling
69 Median Imputation Undersampling
70 Median Imputation Undersampling
71 Median Imputation Undersampling
72 Median Imputation Undersampling
73 Median Imputation Undersampling
74 Median Imputation Undersampling
75 Median Imputation Undersampling
76 Median Imputation Undersampling
77 Median Imputation Undersampling
78 Median Imputation Undersampling
79 Median Imputation Undersampling
80 Median Imputation Undersampling
81 Median Imputation Undersampling
82 Median Imputation Undersampling
83 Median Imputation Undersampling
84 Median Imputation Undersampling
85 Median Imputation Undersampling
86 Median Imputation Undersampling
87 Median Imputation Undersampling
88 Median Imputation Undersampling
89 Median Imputation Undersampling
90 Median Imputation Undersampling
91 Median Imputation Undersampling
92 Median Imputation Undersampling
93 Median Imputation Undersampling
94 Median Imputation Undersampling
95 Median Imputation Undersampling
96 Median Imputation Undersampling
97 Median Imputation Undersampling
98 Median Imputation Undersampling
99 Median Imputation Undersampling
100 Median Imputation Undersampling
101 Median Imputation Undersampling
102 Median Imputation Undersampling
103 Median Imputation Undersampling
104 Median Imputation Undersampling
105 Median Imputation Undersampling
106 Median Imputation Undersampling
107 Median Imputation Undersampling
108 Median Imputation Undersampling
109 Median Imputation Undersampling
110 Median Imputation Undersampling
111 Median Imputation Undersampling
112 Median Imputation Undersampling
113 Median Imputation Undersampling
114 Median Imputation Undersampling
115 Median Imputation Undersampling
116 Median Imputation Undersampling
117 Median Imputation Undersampling
118 Median Imputation Undersampling
119 Median Imputation Undersampling
120 Median Imputation Undersampling
121 Median Imputation Undersampling
122 Median Imputation Undersampling
123 Median Imputation Undersampling
124 Median Imputation Undersampling
125 Median Imputation Undersampling
126 Median Imputation Undersampling
127 Median Imputation Undersampling
128 Median Imputation Undersampling
129 Median Imputation Undersampling
130 Median Imputation Undersampling
131 Median Imputation Undersampling
132 Median Imputation Undersampling
133 Median Imputation Undersampling
134 Median Imputation Undersampling
Model Unique Code
0 Gradient Boosting Machines (GBM)_Undersampling...
1 Gradient Boosting Machines (GBM)_Undersampling...
2 Gradient Boosting Machines (GBM)_Undersampling...
3 Gradient Boosting Machines (GBM)_Undersampling...
4 Gradient Boosting Machines (GBM)_Undersampling...
5 Gradient Boosting Machines (GBM)_Undersampling...
6 Gradient Boosting Machines (GBM)_Undersampling...
7 Gradient Boosting Machines (GBM)_Undersampling...
8 Gradient Boosting Machines (GBM)_Undersampling...
9 Gradient Boosting Machines (GBM)_Undersampling...
10 Gradient Boosting Machines (GBM)_Undersampling...
11 Gradient Boosting Machines (GBM)_Undersampling...
12 Gradient Boosting Machines (GBM)_Undersampling...
13 Gradient Boosting Machines (GBM)_Undersampling...
14 Gradient Boosting Machines (GBM)_Undersampling...
15 Gradient Boosting Machines (GBM)_Undersampling...
16 Gradient Boosting Machines (GBM)_Undersampling...
17 Gradient Boosting Machines (GBM)_Undersampling...
18 Gradient Boosting Machines (GBM)_Undersampling...
19 Gradient Boosting Machines (GBM)_Undersampling...
20 Gradient Boosting Machines (GBM)_Undersampling...
21 Gradient Boosting Machines (GBM)_Undersampling...
22 Gradient Boosting Machines (GBM)_Undersampling...
23 Gradient Boosting Machines (GBM)_Undersampling...
24 Gradient Boosting Machines (GBM)_Undersampling...
25 Gradient Boosting Machines (GBM)_Undersampling...
26 Gradient Boosting Machines (GBM)_Undersampling...
27 Gradient Boosting Machines (GBM)_Undersampling...
28 Gradient Boosting Machines (GBM)_Undersampling...
29 Gradient Boosting Machines (GBM)_Undersampling...
30 Gradient Boosting Machines (GBM)_Undersampling...
31 Gradient Boosting Machines (GBM)_Undersampling...
32 Gradient Boosting Machines (GBM)_Undersampling...
33 Gradient Boosting Machines (GBM)_Undersampling...
34 Gradient Boosting Machines (GBM)_Undersampling...
35 Gradient Boosting Machines (GBM)_Undersampling...
36 Gradient Boosting Machines (GBM)_Undersampling...
37 Gradient Boosting Machines (GBM)_Undersampling...
38 Gradient Boosting Machines (GBM)_Undersampling...
39 Gradient Boosting Machines (GBM)_Undersampling...
40 Gradient Boosting Machines (GBM)_Undersampling...
41 Gradient Boosting Machines (GBM)_Undersampling...
42 Gradient Boosting Machines (GBM)_Undersampling...
43 Gradient Boosting Machines (GBM)_Undersampling...
44 Gradient Boosting Machines (GBM)_Undersampling...
45 Gradient Boosting Machines (GBM)_Undersampling...
46 Gradient Boosting Machines (GBM)_Undersampling...
47 Gradient Boosting Machines (GBM)_Undersampling...
48 Gradient Boosting Machines (GBM)_Undersampling...
49 Gradient Boosting Machines (GBM)_Undersampling...
50 Gradient Boosting Machines (GBM)_Undersampling...
51 Gradient Boosting Machines (GBM)_Undersampling...
52 Gradient Boosting Machines (GBM)_Undersampling...
53 Gradient Boosting Machines (GBM)_Undersampling...
54 Gradient Boosting Machines (GBM)_Undersampling...
55 Gradient Boosting Machines (GBM)_Undersampling...
56 Gradient Boosting Machines (GBM)_Undersampling...
57 Gradient Boosting Machines (GBM)_Undersampling...
58 Gradient Boosting Machines (GBM)_Undersampling...
59 Gradient Boosting Machines (GBM)_Undersampling...
60 Gradient Boosting Machines (GBM)_Undersampling...
61 Gradient Boosting Machines (GBM)_Undersampling...
62 Gradient Boosting Machines (GBM)_Undersampling...
63 Gradient Boosting Machines (GBM)_Undersampling...
64 Gradient Boosting Machines (GBM)_Undersampling...
65 Gradient Boosting Machines (GBM)_Undersampling...
66 Gradient Boosting Machines (GBM)_Undersampling...
67 Gradient Boosting Machines (GBM)_Undersampling...
68 Gradient Boosting Machines (GBM)_Undersampling...
69 Gradient Boosting Machines (GBM)_Undersampling...
70 Gradient Boosting Machines (GBM)_Undersampling...
71 Gradient Boosting Machines (GBM)_Undersampling...
72 Gradient Boosting Machines (GBM)_Undersampling...
73 Gradient Boosting Machines (GBM)_Undersampling...
74 Gradient Boosting Machines (GBM)_Undersampling...
75 Gradient Boosting Machines (GBM)_Undersampling...
76 Gradient Boosting Machines (GBM)_Undersampling...
77 Gradient Boosting Machines (GBM)_Undersampling...
78 Gradient Boosting Machines (GBM)_Undersampling...
79 Gradient Boosting Machines (GBM)_Undersampling...
80 Gradient Boosting Machines (GBM)_Undersampling...
81 Gradient Boosting Machines (GBM)_Undersampling...
82 Gradient Boosting Machines (GBM)_Undersampling...
83 Gradient Boosting Machines (GBM)_Undersampling...
84 Gradient Boosting Machines (GBM)_Undersampling...
85 Gradient Boosting Machines (GBM)_Undersampling...
86 Gradient Boosting Machines (GBM)_Undersampling...
87 Gradient Boosting Machines (GBM)_Undersampling...
88 Gradient Boosting Machines (GBM)_Undersampling...
89 Gradient Boosting Machines (GBM)_Undersampling...
90 Gradient Boosting Machines (GBM)_Undersampling...
91 Gradient Boosting Machines (GBM)_Undersampling...
92 Gradient Boosting Machines (GBM)_Undersampling...
93 Gradient Boosting Machines (GBM)_Undersampling...
94 Gradient Boosting Machines (GBM)_Undersampling...
95 Gradient Boosting Machines (GBM)_Undersampling...
96 Gradient Boosting Machines (GBM)_Undersampling...
97 Gradient Boosting Machines (GBM)_Undersampling...
98 Gradient Boosting Machines (GBM)_Undersampling...
99 Gradient Boosting Machines (GBM)_Undersampling...
100 Gradient Boosting Machines (GBM)_Undersampling...
101 Gradient Boosting Machines (GBM)_Undersampling...
102 Gradient Boosting Machines (GBM)_Undersampling...
103 Gradient Boosting Machines (GBM)_Undersampling...
104 Gradient Boosting Machines (GBM)_Undersampling...
105 Gradient Boosting Machines (GBM)_Undersampling...
106 Gradient Boosting Machines (GBM)_Undersampling...
107 Gradient Boosting Machines (GBM)_Undersampling...
108 Gradient Boosting Machines (GBM)_Undersampling...
109 Gradient Boosting Machines (GBM)_Undersampling...
110 Gradient Boosting Machines (GBM)_Undersampling...
111 Gradient Boosting Machines (GBM)_Undersampling...
112 Gradient Boosting Machines (GBM)_Undersampling...
113 Gradient Boosting Machines (GBM)_Undersampling...
114 Gradient Boosting Machines (GBM)_Undersampling...
115 Gradient Boosting Machines (GBM)_Undersampling...
116 Gradient Boosting Machines (GBM)_Undersampling...
117 Gradient Boosting Machines (GBM)_Undersampling...
118 Gradient Boosting Machines (GBM)_Undersampling...
119 Gradient Boosting Machines (GBM)_Undersampling...
120 Gradient Boosting Machines (GBM)_Undersampling...
121 Gradient Boosting Machines (GBM)_Undersampling...
122 Gradient Boosting Machines (GBM)_Undersampling...
123 Gradient Boosting Machines (GBM)_Undersampling...
124 Gradient Boosting Machines (GBM)_Undersampling...
125 Gradient Boosting Machines (GBM)_Undersampling...
126 Gradient Boosting Machines (GBM)_Undersampling...
127 Gradient Boosting Machines (GBM)_Undersampling...
128 Gradient Boosting Machines (GBM)_Undersampling...
129 Gradient Boosting Machines (GBM)_Undersampling...
130 Gradient Boosting Machines (GBM)_Undersampling...
131 Gradient Boosting Machines (GBM)_Undersampling...
132 Gradient Boosting Machines (GBM)_Undersampling...
133 Gradient Boosting Machines (GBM)_Undersampling...
134 Gradient Boosting Machines (GBM)_Undersampling... ,
Algorithm Metrics Hyperparameters \
0 Gradient Boosting Machines (GBM) 0.811957 NaN
1 Gradient Boosting Machines (GBM) 0.118400 NaN
2 Gradient Boosting Machines (GBM) 0.312236 NaN
3 Gradient Boosting Machines (GBM) 0.171694 NaN
4 Gradient Boosting Machines (GBM) 0.668407 NaN
5 Gradient Boosting Machines (GBM) 0.273396 NaN
6 Gradient Boosting Machines (GBM) 0.336813 NaN
7 Gradient Boosting Machines (GBM) NaN 0.0
8 Gradient Boosting Machines (GBM) NaN friedman_mse
9 Gradient Boosting Machines (GBM) NaN None
10 Gradient Boosting Machines (GBM) NaN 0.1
11 Gradient Boosting Machines (GBM) NaN log_loss
12 Gradient Boosting Machines (GBM) NaN 3
13 Gradient Boosting Machines (GBM) NaN None
14 Gradient Boosting Machines (GBM) NaN None
15 Gradient Boosting Machines (GBM) NaN 0.0
16 Gradient Boosting Machines (GBM) NaN 1
17 Gradient Boosting Machines (GBM) NaN 2
18 Gradient Boosting Machines (GBM) NaN 0.0
19 Gradient Boosting Machines (GBM) NaN 100
20 Gradient Boosting Machines (GBM) NaN None
21 Gradient Boosting Machines (GBM) NaN None
22 Gradient Boosting Machines (GBM) NaN 1.0
23 Gradient Boosting Machines (GBM) NaN 0.0001
24 Gradient Boosting Machines (GBM) NaN 0.1
25 Gradient Boosting Machines (GBM) NaN 0
26 Gradient Boosting Machines (GBM) NaN False
27 Gradient Boosting Machines (GBM) 0.830919 NaN
28 Gradient Boosting Machines (GBM) 0.114516 NaN
29 Gradient Boosting Machines (GBM) 0.432927 NaN
30 Gradient Boosting Machines (GBM) 0.181122 NaN
31 Gradient Boosting Machines (GBM) 0.704383 NaN
32 Gradient Boosting Machines (GBM) 0.315976 NaN
33 Gradient Boosting Machines (GBM) 0.408767 NaN
34 Gradient Boosting Machines (GBM) NaN 0.0
35 Gradient Boosting Machines (GBM) NaN friedman_mse
36 Gradient Boosting Machines (GBM) NaN None
37 Gradient Boosting Machines (GBM) NaN 0.1
38 Gradient Boosting Machines (GBM) NaN log_loss
39 Gradient Boosting Machines (GBM) NaN 3
40 Gradient Boosting Machines (GBM) NaN None
41 Gradient Boosting Machines (GBM) NaN None
42 Gradient Boosting Machines (GBM) NaN 0.0
43 Gradient Boosting Machines (GBM) NaN 1
44 Gradient Boosting Machines (GBM) NaN 2
45 Gradient Boosting Machines (GBM) NaN 0.0
46 Gradient Boosting Machines (GBM) NaN 100
47 Gradient Boosting Machines (GBM) NaN None
48 Gradient Boosting Machines (GBM) NaN None
49 Gradient Boosting Machines (GBM) NaN 1.0
50 Gradient Boosting Machines (GBM) NaN 0.0001
51 Gradient Boosting Machines (GBM) NaN 0.1
52 Gradient Boosting Machines (GBM) NaN 0
53 Gradient Boosting Machines (GBM) NaN False
54 Gradient Boosting Machines (GBM) 0.798789 NaN
55 Gradient Boosting Machines (GBM) 0.095372 NaN
56 Gradient Boosting Machines (GBM) 0.363636 NaN
57 Gradient Boosting Machines (GBM) 0.151111 NaN
58 Gradient Boosting Machines (GBM) 0.659361 NaN
59 Gradient Boosting Machines (GBM) 0.267713 NaN
60 Gradient Boosting Machines (GBM) 0.318723 NaN
61 Gradient Boosting Machines (GBM) NaN 0.0
62 Gradient Boosting Machines (GBM) NaN friedman_mse
63 Gradient Boosting Machines (GBM) NaN None
64 Gradient Boosting Machines (GBM) NaN 0.1
65 Gradient Boosting Machines (GBM) NaN log_loss
66 Gradient Boosting Machines (GBM) NaN 3
67 Gradient Boosting Machines (GBM) NaN None
68 Gradient Boosting Machines (GBM) NaN None
69 Gradient Boosting Machines (GBM) NaN 0.0
70 Gradient Boosting Machines (GBM) NaN 1
71 Gradient Boosting Machines (GBM) NaN 2
72 Gradient Boosting Machines (GBM) NaN 0.0
73 Gradient Boosting Machines (GBM) NaN 100
74 Gradient Boosting Machines (GBM) NaN None
75 Gradient Boosting Machines (GBM) NaN None
76 Gradient Boosting Machines (GBM) NaN 1.0
77 Gradient Boosting Machines (GBM) NaN 0.0001
78 Gradient Boosting Machines (GBM) NaN 0.1
79 Gradient Boosting Machines (GBM) NaN 0
80 Gradient Boosting Machines (GBM) NaN False
81 Gradient Boosting Machines (GBM) 0.812698 NaN
82 Gradient Boosting Machines (GBM) 0.109256 NaN
83 Gradient Boosting Machines (GBM) 0.367347 NaN
84 Gradient Boosting Machines (GBM) 0.168421 NaN
85 Gradient Boosting Machines (GBM) 0.670480 NaN
86 Gradient Boosting Machines (GBM) 0.273503 NaN
87 Gradient Boosting Machines (GBM) 0.340961 NaN
88 Gradient Boosting Machines (GBM) NaN 0.0
89 Gradient Boosting Machines (GBM) NaN friedman_mse
90 Gradient Boosting Machines (GBM) NaN None
91 Gradient Boosting Machines (GBM) NaN 0.1
92 Gradient Boosting Machines (GBM) NaN log_loss
93 Gradient Boosting Machines (GBM) NaN 3
94 Gradient Boosting Machines (GBM) NaN None
95 Gradient Boosting Machines (GBM) NaN None
96 Gradient Boosting Machines (GBM) NaN 0.0
97 Gradient Boosting Machines (GBM) NaN 1
98 Gradient Boosting Machines (GBM) NaN 2
99 Gradient Boosting Machines (GBM) NaN 0.0
100 Gradient Boosting Machines (GBM) NaN 100
101 Gradient Boosting Machines (GBM) NaN None
102 Gradient Boosting Machines (GBM) NaN None
103 Gradient Boosting Machines (GBM) NaN 1.0
104 Gradient Boosting Machines (GBM) NaN 0.0001
105 Gradient Boosting Machines (GBM) NaN 0.1
106 Gradient Boosting Machines (GBM) NaN 0
107 Gradient Boosting Machines (GBM) NaN False
108 Gradient Boosting Machines (GBM) 0.814015 NaN
109 Gradient Boosting Machines (GBM) 0.124233 NaN
110 Gradient Boosting Machines (GBM) 0.375000 NaN
111 Gradient Boosting Machines (GBM) 0.186636 NaN
112 Gradient Boosting Machines (GBM) 0.688363 NaN
113 Gradient Boosting Machines (GBM) 0.292965 NaN
114 Gradient Boosting Machines (GBM) 0.376725 NaN
115 Gradient Boosting Machines (GBM) NaN 0.0
116 Gradient Boosting Machines (GBM) NaN friedman_mse
117 Gradient Boosting Machines (GBM) NaN None
118 Gradient Boosting Machines (GBM) NaN 0.1
119 Gradient Boosting Machines (GBM) NaN log_loss
120 Gradient Boosting Machines (GBM) NaN 3
121 Gradient Boosting Machines (GBM) NaN None
122 Gradient Boosting Machines (GBM) NaN None
123 Gradient Boosting Machines (GBM) NaN 0.0
124 Gradient Boosting Machines (GBM) NaN 1
125 Gradient Boosting Machines (GBM) NaN 2
126 Gradient Boosting Machines (GBM) NaN 0.0
127 Gradient Boosting Machines (GBM) NaN 100
128 Gradient Boosting Machines (GBM) NaN None
129 Gradient Boosting Machines (GBM) NaN None
130 Gradient Boosting Machines (GBM) NaN 1.0
131 Gradient Boosting Machines (GBM) NaN 0.0001
132 Gradient Boosting Machines (GBM) NaN 0.1
133 Gradient Boosting Machines (GBM) NaN 0
134 Gradient Boosting Machines (GBM) NaN False
Imputation Technique Imbalance Class Technique \
0 Zero Imputation Hybrid Sampling (SMOTEENN)
1 Zero Imputation Hybrid Sampling (SMOTEENN)
2 Zero Imputation Hybrid Sampling (SMOTEENN)
3 Zero Imputation Hybrid Sampling (SMOTEENN)
4 Zero Imputation Hybrid Sampling (SMOTEENN)
5 Zero Imputation Hybrid Sampling (SMOTEENN)
6 Zero Imputation Hybrid Sampling (SMOTEENN)
7 Zero Imputation Hybrid Sampling (SMOTEENN)
8 Zero Imputation Hybrid Sampling (SMOTEENN)
9 Zero Imputation Hybrid Sampling (SMOTEENN)
10 Zero Imputation Hybrid Sampling (SMOTEENN)
11 Zero Imputation Hybrid Sampling (SMOTEENN)
12 Zero Imputation Hybrid Sampling (SMOTEENN)
13 Zero Imputation Hybrid Sampling (SMOTEENN)
14 Zero Imputation Hybrid Sampling (SMOTEENN)
15 Zero Imputation Hybrid Sampling (SMOTEENN)
16 Zero Imputation Hybrid Sampling (SMOTEENN)
17 Zero Imputation Hybrid Sampling (SMOTEENN)
18 Zero Imputation Hybrid Sampling (SMOTEENN)
19 Zero Imputation Hybrid Sampling (SMOTEENN)
20 Zero Imputation Hybrid Sampling (SMOTEENN)
21 Zero Imputation Hybrid Sampling (SMOTEENN)
22 Zero Imputation Hybrid Sampling (SMOTEENN)
23 Zero Imputation Hybrid Sampling (SMOTEENN)
24 Zero Imputation Hybrid Sampling (SMOTEENN)
25 Zero Imputation Hybrid Sampling (SMOTEENN)
26 Zero Imputation Hybrid Sampling (SMOTEENN)
27 Zero Imputation Hybrid Sampling (SMOTEENN)
28 Zero Imputation Hybrid Sampling (SMOTEENN)
29 Zero Imputation Hybrid Sampling (SMOTEENN)
30 Zero Imputation Hybrid Sampling (SMOTEENN)
31 Zero Imputation Hybrid Sampling (SMOTEENN)
32 Zero Imputation Hybrid Sampling (SMOTEENN)
33 Zero Imputation Hybrid Sampling (SMOTEENN)
34 Zero Imputation Hybrid Sampling (SMOTEENN)
35 Zero Imputation Hybrid Sampling (SMOTEENN)
36 Zero Imputation Hybrid Sampling (SMOTEENN)
37 Zero Imputation Hybrid Sampling (SMOTEENN)
38 Zero Imputation Hybrid Sampling (SMOTEENN)
39 Zero Imputation Hybrid Sampling (SMOTEENN)
40 Zero Imputation Hybrid Sampling (SMOTEENN)
41 Zero Imputation Hybrid Sampling (SMOTEENN)
42 Zero Imputation Hybrid Sampling (SMOTEENN)
43 Zero Imputation Hybrid Sampling (SMOTEENN)
44 Zero Imputation Hybrid Sampling (SMOTEENN)
45 Zero Imputation Hybrid Sampling (SMOTEENN)
46 Zero Imputation Hybrid Sampling (SMOTEENN)
47 Zero Imputation Hybrid Sampling (SMOTEENN)
48 Zero Imputation Hybrid Sampling (SMOTEENN)
49 Zero Imputation Hybrid Sampling (SMOTEENN)
50 Zero Imputation Hybrid Sampling (SMOTEENN)
51 Zero Imputation Hybrid Sampling (SMOTEENN)
52 Zero Imputation Hybrid Sampling (SMOTEENN)
53 Zero Imputation Hybrid Sampling (SMOTEENN)
54 Zero Imputation Hybrid Sampling (SMOTEENN)
55 Zero Imputation Hybrid Sampling (SMOTEENN)
56 Zero Imputation Hybrid Sampling (SMOTEENN)
57 Zero Imputation Hybrid Sampling (SMOTEENN)
58 Zero Imputation Hybrid Sampling (SMOTEENN)
59 Zero Imputation Hybrid Sampling (SMOTEENN)
60 Zero Imputation Hybrid Sampling (SMOTEENN)
61 Zero Imputation Hybrid Sampling (SMOTEENN)
62 Zero Imputation Hybrid Sampling (SMOTEENN)
63 Zero Imputation Hybrid Sampling (SMOTEENN)
64 Zero Imputation Hybrid Sampling (SMOTEENN)
65 Zero Imputation Hybrid Sampling (SMOTEENN)
66 Zero Imputation Hybrid Sampling (SMOTEENN)
67 Zero Imputation Hybrid Sampling (SMOTEENN)
68 Zero Imputation Hybrid Sampling (SMOTEENN)
69 Zero Imputation Hybrid Sampling (SMOTEENN)
70 Zero Imputation Hybrid Sampling (SMOTEENN)
71 Zero Imputation Hybrid Sampling (SMOTEENN)
72 Zero Imputation Hybrid Sampling (SMOTEENN)
73 Zero Imputation Hybrid Sampling (SMOTEENN)
74 Zero Imputation Hybrid Sampling (SMOTEENN)
75 Zero Imputation Hybrid Sampling (SMOTEENN)
76 Zero Imputation Hybrid Sampling (SMOTEENN)
77 Zero Imputation Hybrid Sampling (SMOTEENN)
78 Zero Imputation Hybrid Sampling (SMOTEENN)
79 Zero Imputation Hybrid Sampling (SMOTEENN)
80 Zero Imputation Hybrid Sampling (SMOTEENN)
81 Zero Imputation Hybrid Sampling (SMOTEENN)
82 Zero Imputation Hybrid Sampling (SMOTEENN)
83 Zero Imputation Hybrid Sampling (SMOTEENN)
84 Zero Imputation Hybrid Sampling (SMOTEENN)
85 Zero Imputation Hybrid Sampling (SMOTEENN)
86 Zero Imputation Hybrid Sampling (SMOTEENN)
87 Zero Imputation Hybrid Sampling (SMOTEENN)
88 Zero Imputation Hybrid Sampling (SMOTEENN)
89 Zero Imputation Hybrid Sampling (SMOTEENN)
90 Zero Imputation Hybrid Sampling (SMOTEENN)
91 Zero Imputation Hybrid Sampling (SMOTEENN)
92 Zero Imputation Hybrid Sampling (SMOTEENN)
93 Zero Imputation Hybrid Sampling (SMOTEENN)
94 Zero Imputation Hybrid Sampling (SMOTEENN)
95 Zero Imputation Hybrid Sampling (SMOTEENN)
96 Zero Imputation Hybrid Sampling (SMOTEENN)
97 Zero Imputation Hybrid Sampling (SMOTEENN)
98 Zero Imputation Hybrid Sampling (SMOTEENN)
99 Zero Imputation Hybrid Sampling (SMOTEENN)
100 Zero Imputation Hybrid Sampling (SMOTEENN)
101 Zero Imputation Hybrid Sampling (SMOTEENN)
102 Zero Imputation Hybrid Sampling (SMOTEENN)
103 Zero Imputation Hybrid Sampling (SMOTEENN)
104 Zero Imputation Hybrid Sampling (SMOTEENN)
105 Zero Imputation Hybrid Sampling (SMOTEENN)
106 Zero Imputation Hybrid Sampling (SMOTEENN)
107 Zero Imputation Hybrid Sampling (SMOTEENN)
108 Zero Imputation Hybrid Sampling (SMOTEENN)
109 Zero Imputation Hybrid Sampling (SMOTEENN)
110 Zero Imputation Hybrid Sampling (SMOTEENN)
111 Zero Imputation Hybrid Sampling (SMOTEENN)
112 Zero Imputation Hybrid Sampling (SMOTEENN)
113 Zero Imputation Hybrid Sampling (SMOTEENN)
114 Zero Imputation Hybrid Sampling (SMOTEENN)
115 Zero Imputation Hybrid Sampling (SMOTEENN)
116 Zero Imputation Hybrid Sampling (SMOTEENN)
117 Zero Imputation Hybrid Sampling (SMOTEENN)
118 Zero Imputation Hybrid Sampling (SMOTEENN)
119 Zero Imputation Hybrid Sampling (SMOTEENN)
120 Zero Imputation Hybrid Sampling (SMOTEENN)
121 Zero Imputation Hybrid Sampling (SMOTEENN)
122 Zero Imputation Hybrid Sampling (SMOTEENN)
123 Zero Imputation Hybrid Sampling (SMOTEENN)
124 Zero Imputation Hybrid Sampling (SMOTEENN)
125 Zero Imputation Hybrid Sampling (SMOTEENN)
126 Zero Imputation Hybrid Sampling (SMOTEENN)
127 Zero Imputation Hybrid Sampling (SMOTEENN)
128 Zero Imputation Hybrid Sampling (SMOTEENN)
129 Zero Imputation Hybrid Sampling (SMOTEENN)
130 Zero Imputation Hybrid Sampling (SMOTEENN)
131 Zero Imputation Hybrid Sampling (SMOTEENN)
132 Zero Imputation Hybrid Sampling (SMOTEENN)
133 Zero Imputation Hybrid Sampling (SMOTEENN)
134 Zero Imputation Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Gradient Boosting Machines (GBM)_Hybrid Sampli...
1 Gradient Boosting Machines (GBM)_Hybrid Sampli...
2 Gradient Boosting Machines (GBM)_Hybrid Sampli...
3 Gradient Boosting Machines (GBM)_Hybrid Sampli...
4 Gradient Boosting Machines (GBM)_Hybrid Sampli...
5 Gradient Boosting Machines (GBM)_Hybrid Sampli...
6 Gradient Boosting Machines (GBM)_Hybrid Sampli...
7 Gradient Boosting Machines (GBM)_Hybrid Sampli...
8 Gradient Boosting Machines (GBM)_Hybrid Sampli...
9 Gradient Boosting Machines (GBM)_Hybrid Sampli...
10 Gradient Boosting Machines (GBM)_Hybrid Sampli...
11 Gradient Boosting Machines (GBM)_Hybrid Sampli...
12 Gradient Boosting Machines (GBM)_Hybrid Sampli...
13 Gradient Boosting Machines (GBM)_Hybrid Sampli...
14 Gradient Boosting Machines (GBM)_Hybrid Sampli...
15 Gradient Boosting Machines (GBM)_Hybrid Sampli...
16 Gradient Boosting Machines (GBM)_Hybrid Sampli...
17 Gradient Boosting Machines (GBM)_Hybrid Sampli...
18 Gradient Boosting Machines (GBM)_Hybrid Sampli...
19 Gradient Boosting Machines (GBM)_Hybrid Sampli...
20 Gradient Boosting Machines (GBM)_Hybrid Sampli...
21 Gradient Boosting Machines (GBM)_Hybrid Sampli...
22 Gradient Boosting Machines (GBM)_Hybrid Sampli...
23 Gradient Boosting Machines (GBM)_Hybrid Sampli...
24 Gradient Boosting Machines (GBM)_Hybrid Sampli...
25 Gradient Boosting Machines (GBM)_Hybrid Sampli...
26 Gradient Boosting Machines (GBM)_Hybrid Sampli...
27 Gradient Boosting Machines (GBM)_Hybrid Sampli...
28 Gradient Boosting Machines (GBM)_Hybrid Sampli...
29 Gradient Boosting Machines (GBM)_Hybrid Sampli...
30 Gradient Boosting Machines (GBM)_Hybrid Sampli...
31 Gradient Boosting Machines (GBM)_Hybrid Sampli...
32 Gradient Boosting Machines (GBM)_Hybrid Sampli...
33 Gradient Boosting Machines (GBM)_Hybrid Sampli...
34 Gradient Boosting Machines (GBM)_Hybrid Sampli...
35 Gradient Boosting Machines (GBM)_Hybrid Sampli...
36 Gradient Boosting Machines (GBM)_Hybrid Sampli...
37 Gradient Boosting Machines (GBM)_Hybrid Sampli...
38 Gradient Boosting Machines (GBM)_Hybrid Sampli...
39 Gradient Boosting Machines (GBM)_Hybrid Sampli...
40 Gradient Boosting Machines (GBM)_Hybrid Sampli...
41 Gradient Boosting Machines (GBM)_Hybrid Sampli...
42 Gradient Boosting Machines (GBM)_Hybrid Sampli...
43 Gradient Boosting Machines (GBM)_Hybrid Sampli...
44 Gradient Boosting Machines (GBM)_Hybrid Sampli...
45 Gradient Boosting Machines (GBM)_Hybrid Sampli...
46 Gradient Boosting Machines (GBM)_Hybrid Sampli...
47 Gradient Boosting Machines (GBM)_Hybrid Sampli...
48 Gradient Boosting Machines (GBM)_Hybrid Sampli...
49 Gradient Boosting Machines (GBM)_Hybrid Sampli...
50 Gradient Boosting Machines (GBM)_Hybrid Sampli...
51 Gradient Boosting Machines (GBM)_Hybrid Sampli...
52 Gradient Boosting Machines (GBM)_Hybrid Sampli...
53 Gradient Boosting Machines (GBM)_Hybrid Sampli...
54 Gradient Boosting Machines (GBM)_Hybrid Sampli...
55 Gradient Boosting Machines (GBM)_Hybrid Sampli...
56 Gradient Boosting Machines (GBM)_Hybrid Sampli...
57 Gradient Boosting Machines (GBM)_Hybrid Sampli...
58 Gradient Boosting Machines (GBM)_Hybrid Sampli...
59 Gradient Boosting Machines (GBM)_Hybrid Sampli...
60 Gradient Boosting Machines (GBM)_Hybrid Sampli...
61 Gradient Boosting Machines (GBM)_Hybrid Sampli...
62 Gradient Boosting Machines (GBM)_Hybrid Sampli...
63 Gradient Boosting Machines (GBM)_Hybrid Sampli...
64 Gradient Boosting Machines (GBM)_Hybrid Sampli...
65 Gradient Boosting Machines (GBM)_Hybrid Sampli...
66 Gradient Boosting Machines (GBM)_Hybrid Sampli...
67 Gradient Boosting Machines (GBM)_Hybrid Sampli...
68 Gradient Boosting Machines (GBM)_Hybrid Sampli...
69 Gradient Boosting Machines (GBM)_Hybrid Sampli...
70 Gradient Boosting Machines (GBM)_Hybrid Sampli...
71 Gradient Boosting Machines (GBM)_Hybrid Sampli...
72 Gradient Boosting Machines (GBM)_Hybrid Sampli...
73 Gradient Boosting Machines (GBM)_Hybrid Sampli...
74 Gradient Boosting Machines (GBM)_Hybrid Sampli...
75 Gradient Boosting Machines (GBM)_Hybrid Sampli...
76 Gradient Boosting Machines (GBM)_Hybrid Sampli...
77 Gradient Boosting Machines (GBM)_Hybrid Sampli...
78 Gradient Boosting Machines (GBM)_Hybrid Sampli...
79 Gradient Boosting Machines (GBM)_Hybrid Sampli...
80 Gradient Boosting Machines (GBM)_Hybrid Sampli...
81 Gradient Boosting Machines (GBM)_Hybrid Sampli...
82 Gradient Boosting Machines (GBM)_Hybrid Sampli...
83 Gradient Boosting Machines (GBM)_Hybrid Sampli...
84 Gradient Boosting Machines (GBM)_Hybrid Sampli...
85 Gradient Boosting Machines (GBM)_Hybrid Sampli...
86 Gradient Boosting Machines (GBM)_Hybrid Sampli...
87 Gradient Boosting Machines (GBM)_Hybrid Sampli...
88 Gradient Boosting Machines (GBM)_Hybrid Sampli...
89 Gradient Boosting Machines (GBM)_Hybrid Sampli...
90 Gradient Boosting Machines (GBM)_Hybrid Sampli...
91 Gradient Boosting Machines (GBM)_Hybrid Sampli...
92 Gradient Boosting Machines (GBM)_Hybrid Sampli...
93 Gradient Boosting Machines (GBM)_Hybrid Sampli...
94 Gradient Boosting Machines (GBM)_Hybrid Sampli...
95 Gradient Boosting Machines (GBM)_Hybrid Sampli...
96 Gradient Boosting Machines (GBM)_Hybrid Sampli...
97 Gradient Boosting Machines (GBM)_Hybrid Sampli...
98 Gradient Boosting Machines (GBM)_Hybrid Sampli...
99 Gradient Boosting Machines (GBM)_Hybrid Sampli...
100 Gradient Boosting Machines (GBM)_Hybrid Sampli...
101 Gradient Boosting Machines (GBM)_Hybrid Sampli...
102 Gradient Boosting Machines (GBM)_Hybrid Sampli...
103 Gradient Boosting Machines (GBM)_Hybrid Sampli...
104 Gradient Boosting Machines (GBM)_Hybrid Sampli...
105 Gradient Boosting Machines (GBM)_Hybrid Sampli...
106 Gradient Boosting Machines (GBM)_Hybrid Sampli...
107 Gradient Boosting Machines (GBM)_Hybrid Sampli...
108 Gradient Boosting Machines (GBM)_Hybrid Sampli...
109 Gradient Boosting Machines (GBM)_Hybrid Sampli...
110 Gradient Boosting Machines (GBM)_Hybrid Sampli...
111 Gradient Boosting Machines (GBM)_Hybrid Sampli...
112 Gradient Boosting Machines (GBM)_Hybrid Sampli...
113 Gradient Boosting Machines (GBM)_Hybrid Sampli...
114 Gradient Boosting Machines (GBM)_Hybrid Sampli...
115 Gradient Boosting Machines (GBM)_Hybrid Sampli...
116 Gradient Boosting Machines (GBM)_Hybrid Sampli...
117 Gradient Boosting Machines (GBM)_Hybrid Sampli...
118 Gradient Boosting Machines (GBM)_Hybrid Sampli...
119 Gradient Boosting Machines (GBM)_Hybrid Sampli...
120 Gradient Boosting Machines (GBM)_Hybrid Sampli...
121 Gradient Boosting Machines (GBM)_Hybrid Sampli...
122 Gradient Boosting Machines (GBM)_Hybrid Sampli...
123 Gradient Boosting Machines (GBM)_Hybrid Sampli...
124 Gradient Boosting Machines (GBM)_Hybrid Sampli...
125 Gradient Boosting Machines (GBM)_Hybrid Sampli...
126 Gradient Boosting Machines (GBM)_Hybrid Sampli...
127 Gradient Boosting Machines (GBM)_Hybrid Sampli...
128 Gradient Boosting Machines (GBM)_Hybrid Sampli...
129 Gradient Boosting Machines (GBM)_Hybrid Sampli...
130 Gradient Boosting Machines (GBM)_Hybrid Sampli...
131 Gradient Boosting Machines (GBM)_Hybrid Sampli...
132 Gradient Boosting Machines (GBM)_Hybrid Sampli...
133 Gradient Boosting Machines (GBM)_Hybrid Sampli...
134 Gradient Boosting Machines (GBM)_Hybrid Sampli... ,
Algorithm Metrics Hyperparameters \
0 Gradient Boosting Machines (GBM) 0.826969 NaN
1 Gradient Boosting Machines (GBM) 0.112546 NaN
2 Gradient Boosting Machines (GBM) 0.257384 NaN
3 Gradient Boosting Machines (GBM) 0.156611 NaN
4 Gradient Boosting Machines (GBM) 0.651866 NaN
5 Gradient Boosting Machines (GBM) 0.233869 NaN
6 Gradient Boosting Machines (GBM) 0.303732 NaN
7 Gradient Boosting Machines (GBM) NaN 0.0
8 Gradient Boosting Machines (GBM) NaN friedman_mse
9 Gradient Boosting Machines (GBM) NaN None
10 Gradient Boosting Machines (GBM) NaN 0.1
11 Gradient Boosting Machines (GBM) NaN log_loss
12 Gradient Boosting Machines (GBM) NaN 3
13 Gradient Boosting Machines (GBM) NaN None
14 Gradient Boosting Machines (GBM) NaN None
15 Gradient Boosting Machines (GBM) NaN 0.0
16 Gradient Boosting Machines (GBM) NaN 1
17 Gradient Boosting Machines (GBM) NaN 2
18 Gradient Boosting Machines (GBM) NaN 0.0
19 Gradient Boosting Machines (GBM) NaN 100
20 Gradient Boosting Machines (GBM) NaN None
21 Gradient Boosting Machines (GBM) NaN None
22 Gradient Boosting Machines (GBM) NaN 1.0
23 Gradient Boosting Machines (GBM) NaN 0.0001
24 Gradient Boosting Machines (GBM) NaN 0.1
25 Gradient Boosting Machines (GBM) NaN 0
26 Gradient Boosting Machines (GBM) NaN False
27 Gradient Boosting Machines (GBM) 0.828812 NaN
28 Gradient Boosting Machines (GBM) 0.093645 NaN
29 Gradient Boosting Machines (GBM) 0.341463 NaN
30 Gradient Boosting Machines (GBM) 0.146982 NaN
31 Gradient Boosting Machines (GBM) 0.678118 NaN
32 Gradient Boosting Machines (GBM) 0.298252 NaN
33 Gradient Boosting Machines (GBM) 0.356237 NaN
34 Gradient Boosting Machines (GBM) NaN 0.0
35 Gradient Boosting Machines (GBM) NaN friedman_mse
36 Gradient Boosting Machines (GBM) NaN None
37 Gradient Boosting Machines (GBM) NaN 0.1
38 Gradient Boosting Machines (GBM) NaN log_loss
39 Gradient Boosting Machines (GBM) NaN 3
40 Gradient Boosting Machines (GBM) NaN None
41 Gradient Boosting Machines (GBM) NaN None
42 Gradient Boosting Machines (GBM) NaN 0.0
43 Gradient Boosting Machines (GBM) NaN 1
44 Gradient Boosting Machines (GBM) NaN 2
45 Gradient Boosting Machines (GBM) NaN 0.0
46 Gradient Boosting Machines (GBM) NaN 100
47 Gradient Boosting Machines (GBM) NaN None
48 Gradient Boosting Machines (GBM) NaN None
49 Gradient Boosting Machines (GBM) NaN 1.0
50 Gradient Boosting Machines (GBM) NaN 0.0001
51 Gradient Boosting Machines (GBM) NaN 0.1
52 Gradient Boosting Machines (GBM) NaN 0
53 Gradient Boosting Machines (GBM) NaN False
54 Gradient Boosting Machines (GBM) 0.821701 NaN
55 Gradient Boosting Machines (GBM) 0.103560 NaN
56 Gradient Boosting Machines (GBM) 0.342246 NaN
57 Gradient Boosting Machines (GBM) 0.159006 NaN
58 Gradient Boosting Machines (GBM) 0.674822 NaN
59 Gradient Boosting Machines (GBM) 0.268507 NaN
60 Gradient Boosting Machines (GBM) 0.349644 NaN
61 Gradient Boosting Machines (GBM) NaN 0.0
62 Gradient Boosting Machines (GBM) NaN friedman_mse
63 Gradient Boosting Machines (GBM) NaN None
64 Gradient Boosting Machines (GBM) NaN 0.1
65 Gradient Boosting Machines (GBM) NaN log_loss
66 Gradient Boosting Machines (GBM) NaN 3
67 Gradient Boosting Machines (GBM) NaN None
68 Gradient Boosting Machines (GBM) NaN None
69 Gradient Boosting Machines (GBM) NaN 0.0
70 Gradient Boosting Machines (GBM) NaN 1
71 Gradient Boosting Machines (GBM) NaN 2
72 Gradient Boosting Machines (GBM) NaN 0.0
73 Gradient Boosting Machines (GBM) NaN 100
74 Gradient Boosting Machines (GBM) NaN None
75 Gradient Boosting Machines (GBM) NaN None
76 Gradient Boosting Machines (GBM) NaN 1.0
77 Gradient Boosting Machines (GBM) NaN 0.0001
78 Gradient Boosting Machines (GBM) NaN 0.1
79 Gradient Boosting Machines (GBM) NaN 0
80 Gradient Boosting Machines (GBM) NaN False
81 Gradient Boosting Machines (GBM) 0.838514 NaN
82 Gradient Boosting Machines (GBM) 0.113173 NaN
83 Gradient Boosting Machines (GBM) 0.311224 NaN
84 Gradient Boosting Machines (GBM) 0.165986 NaN
85 Gradient Boosting Machines (GBM) 0.674381 NaN
86 Gradient Boosting Machines (GBM) 0.276701 NaN
87 Gradient Boosting Machines (GBM) 0.348761 NaN
88 Gradient Boosting Machines (GBM) NaN 0.0
89 Gradient Boosting Machines (GBM) NaN friedman_mse
90 Gradient Boosting Machines (GBM) NaN None
91 Gradient Boosting Machines (GBM) NaN 0.1
92 Gradient Boosting Machines (GBM) NaN log_loss
93 Gradient Boosting Machines (GBM) NaN 3
94 Gradient Boosting Machines (GBM) NaN None
95 Gradient Boosting Machines (GBM) NaN None
96 Gradient Boosting Machines (GBM) NaN 0.0
97 Gradient Boosting Machines (GBM) NaN 1
98 Gradient Boosting Machines (GBM) NaN 2
99 Gradient Boosting Machines (GBM) NaN 0.0
100 Gradient Boosting Machines (GBM) NaN 100
101 Gradient Boosting Machines (GBM) NaN None
102 Gradient Boosting Machines (GBM) NaN None
103 Gradient Boosting Machines (GBM) NaN 1.0
104 Gradient Boosting Machines (GBM) NaN 0.0001
105 Gradient Boosting Machines (GBM) NaN 0.1
106 Gradient Boosting Machines (GBM) NaN 0
107 Gradient Boosting Machines (GBM) NaN False
108 Gradient Boosting Machines (GBM) 0.835090 NaN
109 Gradient Boosting Machines (GBM) 0.123162 NaN
110 Gradient Boosting Machines (GBM) 0.310185 NaN
111 Gradient Boosting Machines (GBM) 0.176316 NaN
112 Gradient Boosting Machines (GBM) 0.676027 NaN
113 Gradient Boosting Machines (GBM) 0.270665 NaN
114 Gradient Boosting Machines (GBM) 0.352054 NaN
115 Gradient Boosting Machines (GBM) NaN 0.0
116 Gradient Boosting Machines (GBM) NaN friedman_mse
117 Gradient Boosting Machines (GBM) NaN None
118 Gradient Boosting Machines (GBM) NaN 0.1
119 Gradient Boosting Machines (GBM) NaN log_loss
120 Gradient Boosting Machines (GBM) NaN 3
121 Gradient Boosting Machines (GBM) NaN None
122 Gradient Boosting Machines (GBM) NaN None
123 Gradient Boosting Machines (GBM) NaN 0.0
124 Gradient Boosting Machines (GBM) NaN 1
125 Gradient Boosting Machines (GBM) NaN 2
126 Gradient Boosting Machines (GBM) NaN 0.0
127 Gradient Boosting Machines (GBM) NaN 100
128 Gradient Boosting Machines (GBM) NaN None
129 Gradient Boosting Machines (GBM) NaN None
130 Gradient Boosting Machines (GBM) NaN 1.0
131 Gradient Boosting Machines (GBM) NaN 0.0001
132 Gradient Boosting Machines (GBM) NaN 0.1
133 Gradient Boosting Machines (GBM) NaN 0
134 Gradient Boosting Machines (GBM) NaN False
Imputation Technique Imbalance Class Technique \
0 Mean Imputation Hybrid Sampling (SMOTEENN)
1 Mean Imputation Hybrid Sampling (SMOTEENN)
2 Mean Imputation Hybrid Sampling (SMOTEENN)
3 Mean Imputation Hybrid Sampling (SMOTEENN)
4 Mean Imputation Hybrid Sampling (SMOTEENN)
5 Mean Imputation Hybrid Sampling (SMOTEENN)
6 Mean Imputation Hybrid Sampling (SMOTEENN)
7 Mean Imputation Hybrid Sampling (SMOTEENN)
8 Mean Imputation Hybrid Sampling (SMOTEENN)
9 Mean Imputation Hybrid Sampling (SMOTEENN)
10 Mean Imputation Hybrid Sampling (SMOTEENN)
11 Mean Imputation Hybrid Sampling (SMOTEENN)
12 Mean Imputation Hybrid Sampling (SMOTEENN)
13 Mean Imputation Hybrid Sampling (SMOTEENN)
14 Mean Imputation Hybrid Sampling (SMOTEENN)
15 Mean Imputation Hybrid Sampling (SMOTEENN)
16 Mean Imputation Hybrid Sampling (SMOTEENN)
17 Mean Imputation Hybrid Sampling (SMOTEENN)
18 Mean Imputation Hybrid Sampling (SMOTEENN)
19 Mean Imputation Hybrid Sampling (SMOTEENN)
20 Mean Imputation Hybrid Sampling (SMOTEENN)
21 Mean Imputation Hybrid Sampling (SMOTEENN)
22 Mean Imputation Hybrid Sampling (SMOTEENN)
23 Mean Imputation Hybrid Sampling (SMOTEENN)
24 Mean Imputation Hybrid Sampling (SMOTEENN)
25 Mean Imputation Hybrid Sampling (SMOTEENN)
26 Mean Imputation Hybrid Sampling (SMOTEENN)
27 Mean Imputation Hybrid Sampling (SMOTEENN)
28 Mean Imputation Hybrid Sampling (SMOTEENN)
29 Mean Imputation Hybrid Sampling (SMOTEENN)
30 Mean Imputation Hybrid Sampling (SMOTEENN)
31 Mean Imputation Hybrid Sampling (SMOTEENN)
32 Mean Imputation Hybrid Sampling (SMOTEENN)
33 Mean Imputation Hybrid Sampling (SMOTEENN)
34 Mean Imputation Hybrid Sampling (SMOTEENN)
35 Mean Imputation Hybrid Sampling (SMOTEENN)
36 Mean Imputation Hybrid Sampling (SMOTEENN)
37 Mean Imputation Hybrid Sampling (SMOTEENN)
38 Mean Imputation Hybrid Sampling (SMOTEENN)
39 Mean Imputation Hybrid Sampling (SMOTEENN)
40 Mean Imputation Hybrid Sampling (SMOTEENN)
41 Mean Imputation Hybrid Sampling (SMOTEENN)
42 Mean Imputation Hybrid Sampling (SMOTEENN)
43 Mean Imputation Hybrid Sampling (SMOTEENN)
44 Mean Imputation Hybrid Sampling (SMOTEENN)
45 Mean Imputation Hybrid Sampling (SMOTEENN)
46 Mean Imputation Hybrid Sampling (SMOTEENN)
47 Mean Imputation Hybrid Sampling (SMOTEENN)
48 Mean Imputation Hybrid Sampling (SMOTEENN)
49 Mean Imputation Hybrid Sampling (SMOTEENN)
50 Mean Imputation Hybrid Sampling (SMOTEENN)
51 Mean Imputation Hybrid Sampling (SMOTEENN)
52 Mean Imputation Hybrid Sampling (SMOTEENN)
53 Mean Imputation Hybrid Sampling (SMOTEENN)
54 Mean Imputation Hybrid Sampling (SMOTEENN)
55 Mean Imputation Hybrid Sampling (SMOTEENN)
56 Mean Imputation Hybrid Sampling (SMOTEENN)
57 Mean Imputation Hybrid Sampling (SMOTEENN)
58 Mean Imputation Hybrid Sampling (SMOTEENN)
59 Mean Imputation Hybrid Sampling (SMOTEENN)
60 Mean Imputation Hybrid Sampling (SMOTEENN)
61 Mean Imputation Hybrid Sampling (SMOTEENN)
62 Mean Imputation Hybrid Sampling (SMOTEENN)
63 Mean Imputation Hybrid Sampling (SMOTEENN)
64 Mean Imputation Hybrid Sampling (SMOTEENN)
65 Mean Imputation Hybrid Sampling (SMOTEENN)
66 Mean Imputation Hybrid Sampling (SMOTEENN)
67 Mean Imputation Hybrid Sampling (SMOTEENN)
68 Mean Imputation Hybrid Sampling (SMOTEENN)
69 Mean Imputation Hybrid Sampling (SMOTEENN)
70 Mean Imputation Hybrid Sampling (SMOTEENN)
71 Mean Imputation Hybrid Sampling (SMOTEENN)
72 Mean Imputation Hybrid Sampling (SMOTEENN)
73 Mean Imputation Hybrid Sampling (SMOTEENN)
74 Mean Imputation Hybrid Sampling (SMOTEENN)
75 Mean Imputation Hybrid Sampling (SMOTEENN)
76 Mean Imputation Hybrid Sampling (SMOTEENN)
77 Mean Imputation Hybrid Sampling (SMOTEENN)
78 Mean Imputation Hybrid Sampling (SMOTEENN)
79 Mean Imputation Hybrid Sampling (SMOTEENN)
80 Mean Imputation Hybrid Sampling (SMOTEENN)
81 Mean Imputation Hybrid Sampling (SMOTEENN)
82 Mean Imputation Hybrid Sampling (SMOTEENN)
83 Mean Imputation Hybrid Sampling (SMOTEENN)
84 Mean Imputation Hybrid Sampling (SMOTEENN)
85 Mean Imputation Hybrid Sampling (SMOTEENN)
86 Mean Imputation Hybrid Sampling (SMOTEENN)
87 Mean Imputation Hybrid Sampling (SMOTEENN)
88 Mean Imputation Hybrid Sampling (SMOTEENN)
89 Mean Imputation Hybrid Sampling (SMOTEENN)
90 Mean Imputation Hybrid Sampling (SMOTEENN)
91 Mean Imputation Hybrid Sampling (SMOTEENN)
92 Mean Imputation Hybrid Sampling (SMOTEENN)
93 Mean Imputation Hybrid Sampling (SMOTEENN)
94 Mean Imputation Hybrid Sampling (SMOTEENN)
95 Mean Imputation Hybrid Sampling (SMOTEENN)
96 Mean Imputation Hybrid Sampling (SMOTEENN)
97 Mean Imputation Hybrid Sampling (SMOTEENN)
98 Mean Imputation Hybrid Sampling (SMOTEENN)
99 Mean Imputation Hybrid Sampling (SMOTEENN)
100 Mean Imputation Hybrid Sampling (SMOTEENN)
101 Mean Imputation Hybrid Sampling (SMOTEENN)
102 Mean Imputation Hybrid Sampling (SMOTEENN)
103 Mean Imputation Hybrid Sampling (SMOTEENN)
104 Mean Imputation Hybrid Sampling (SMOTEENN)
105 Mean Imputation Hybrid Sampling (SMOTEENN)
106 Mean Imputation Hybrid Sampling (SMOTEENN)
107 Mean Imputation Hybrid Sampling (SMOTEENN)
108 Mean Imputation Hybrid Sampling (SMOTEENN)
109 Mean Imputation Hybrid Sampling (SMOTEENN)
110 Mean Imputation Hybrid Sampling (SMOTEENN)
111 Mean Imputation Hybrid Sampling (SMOTEENN)
112 Mean Imputation Hybrid Sampling (SMOTEENN)
113 Mean Imputation Hybrid Sampling (SMOTEENN)
114 Mean Imputation Hybrid Sampling (SMOTEENN)
115 Mean Imputation Hybrid Sampling (SMOTEENN)
116 Mean Imputation Hybrid Sampling (SMOTEENN)
117 Mean Imputation Hybrid Sampling (SMOTEENN)
118 Mean Imputation Hybrid Sampling (SMOTEENN)
119 Mean Imputation Hybrid Sampling (SMOTEENN)
120 Mean Imputation Hybrid Sampling (SMOTEENN)
121 Mean Imputation Hybrid Sampling (SMOTEENN)
122 Mean Imputation Hybrid Sampling (SMOTEENN)
123 Mean Imputation Hybrid Sampling (SMOTEENN)
124 Mean Imputation Hybrid Sampling (SMOTEENN)
125 Mean Imputation Hybrid Sampling (SMOTEENN)
126 Mean Imputation Hybrid Sampling (SMOTEENN)
127 Mean Imputation Hybrid Sampling (SMOTEENN)
128 Mean Imputation Hybrid Sampling (SMOTEENN)
129 Mean Imputation Hybrid Sampling (SMOTEENN)
130 Mean Imputation Hybrid Sampling (SMOTEENN)
131 Mean Imputation Hybrid Sampling (SMOTEENN)
132 Mean Imputation Hybrid Sampling (SMOTEENN)
133 Mean Imputation Hybrid Sampling (SMOTEENN)
134 Mean Imputation Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Gradient Boosting Machines (GBM)_Hybrid Sampli...
1 Gradient Boosting Machines (GBM)_Hybrid Sampli...
2 Gradient Boosting Machines (GBM)_Hybrid Sampli...
3 Gradient Boosting Machines (GBM)_Hybrid Sampli...
4 Gradient Boosting Machines (GBM)_Hybrid Sampli...
5 Gradient Boosting Machines (GBM)_Hybrid Sampli...
6 Gradient Boosting Machines (GBM)_Hybrid Sampli...
7 Gradient Boosting Machines (GBM)_Hybrid Sampli...
8 Gradient Boosting Machines (GBM)_Hybrid Sampli...
9 Gradient Boosting Machines (GBM)_Hybrid Sampli...
10 Gradient Boosting Machines (GBM)_Hybrid Sampli...
11 Gradient Boosting Machines (GBM)_Hybrid Sampli...
12 Gradient Boosting Machines (GBM)_Hybrid Sampli...
13 Gradient Boosting Machines (GBM)_Hybrid Sampli...
14 Gradient Boosting Machines (GBM)_Hybrid Sampli...
15 Gradient Boosting Machines (GBM)_Hybrid Sampli...
16 Gradient Boosting Machines (GBM)_Hybrid Sampli...
17 Gradient Boosting Machines (GBM)_Hybrid Sampli...
18 Gradient Boosting Machines (GBM)_Hybrid Sampli...
19 Gradient Boosting Machines (GBM)_Hybrid Sampli...
20 Gradient Boosting Machines (GBM)_Hybrid Sampli...
21 Gradient Boosting Machines (GBM)_Hybrid Sampli...
22 Gradient Boosting Machines (GBM)_Hybrid Sampli...
23 Gradient Boosting Machines (GBM)_Hybrid Sampli...
24 Gradient Boosting Machines (GBM)_Hybrid Sampli...
25 Gradient Boosting Machines (GBM)_Hybrid Sampli...
26 Gradient Boosting Machines (GBM)_Hybrid Sampli...
27 Gradient Boosting Machines (GBM)_Hybrid Sampli...
28 Gradient Boosting Machines (GBM)_Hybrid Sampli...
29 Gradient Boosting Machines (GBM)_Hybrid Sampli...
30 Gradient Boosting Machines (GBM)_Hybrid Sampli...
31 Gradient Boosting Machines (GBM)_Hybrid Sampli...
32 Gradient Boosting Machines (GBM)_Hybrid Sampli...
33 Gradient Boosting Machines (GBM)_Hybrid Sampli...
34 Gradient Boosting Machines (GBM)_Hybrid Sampli...
35 Gradient Boosting Machines (GBM)_Hybrid Sampli...
36 Gradient Boosting Machines (GBM)_Hybrid Sampli...
37 Gradient Boosting Machines (GBM)_Hybrid Sampli...
38 Gradient Boosting Machines (GBM)_Hybrid Sampli...
39 Gradient Boosting Machines (GBM)_Hybrid Sampli...
40 Gradient Boosting Machines (GBM)_Hybrid Sampli...
41 Gradient Boosting Machines (GBM)_Hybrid Sampli...
42 Gradient Boosting Machines (GBM)_Hybrid Sampli...
43 Gradient Boosting Machines (GBM)_Hybrid Sampli...
44 Gradient Boosting Machines (GBM)_Hybrid Sampli...
45 Gradient Boosting Machines (GBM)_Hybrid Sampli...
46 Gradient Boosting Machines (GBM)_Hybrid Sampli...
47 Gradient Boosting Machines (GBM)_Hybrid Sampli...
48 Gradient Boosting Machines (GBM)_Hybrid Sampli...
49 Gradient Boosting Machines (GBM)_Hybrid Sampli...
50 Gradient Boosting Machines (GBM)_Hybrid Sampli...
51 Gradient Boosting Machines (GBM)_Hybrid Sampli...
52 Gradient Boosting Machines (GBM)_Hybrid Sampli...
53 Gradient Boosting Machines (GBM)_Hybrid Sampli...
54 Gradient Boosting Machines (GBM)_Hybrid Sampli...
55 Gradient Boosting Machines (GBM)_Hybrid Sampli...
56 Gradient Boosting Machines (GBM)_Hybrid Sampli...
57 Gradient Boosting Machines (GBM)_Hybrid Sampli...
58 Gradient Boosting Machines (GBM)_Hybrid Sampli...
59 Gradient Boosting Machines (GBM)_Hybrid Sampli...
60 Gradient Boosting Machines (GBM)_Hybrid Sampli...
61 Gradient Boosting Machines (GBM)_Hybrid Sampli...
62 Gradient Boosting Machines (GBM)_Hybrid Sampli...
63 Gradient Boosting Machines (GBM)_Hybrid Sampli...
64 Gradient Boosting Machines (GBM)_Hybrid Sampli...
65 Gradient Boosting Machines (GBM)_Hybrid Sampli...
66 Gradient Boosting Machines (GBM)_Hybrid Sampli...
67 Gradient Boosting Machines (GBM)_Hybrid Sampli...
68 Gradient Boosting Machines (GBM)_Hybrid Sampli...
69 Gradient Boosting Machines (GBM)_Hybrid Sampli...
70 Gradient Boosting Machines (GBM)_Hybrid Sampli...
71 Gradient Boosting Machines (GBM)_Hybrid Sampli...
72 Gradient Boosting Machines (GBM)_Hybrid Sampli...
73 Gradient Boosting Machines (GBM)_Hybrid Sampli...
74 Gradient Boosting Machines (GBM)_Hybrid Sampli...
75 Gradient Boosting Machines (GBM)_Hybrid Sampli...
76 Gradient Boosting Machines (GBM)_Hybrid Sampli...
77 Gradient Boosting Machines (GBM)_Hybrid Sampli...
78 Gradient Boosting Machines (GBM)_Hybrid Sampli...
79 Gradient Boosting Machines (GBM)_Hybrid Sampli...
80 Gradient Boosting Machines (GBM)_Hybrid Sampli...
81 Gradient Boosting Machines (GBM)_Hybrid Sampli...
82 Gradient Boosting Machines (GBM)_Hybrid Sampli...
83 Gradient Boosting Machines (GBM)_Hybrid Sampli...
84 Gradient Boosting Machines (GBM)_Hybrid Sampli...
85 Gradient Boosting Machines (GBM)_Hybrid Sampli...
86 Gradient Boosting Machines (GBM)_Hybrid Sampli...
87 Gradient Boosting Machines (GBM)_Hybrid Sampli...
88 Gradient Boosting Machines (GBM)_Hybrid Sampli...
89 Gradient Boosting Machines (GBM)_Hybrid Sampli...
90 Gradient Boosting Machines (GBM)_Hybrid Sampli...
91 Gradient Boosting Machines (GBM)_Hybrid Sampli...
92 Gradient Boosting Machines (GBM)_Hybrid Sampli...
93 Gradient Boosting Machines (GBM)_Hybrid Sampli...
94 Gradient Boosting Machines (GBM)_Hybrid Sampli...
95 Gradient Boosting Machines (GBM)_Hybrid Sampli...
96 Gradient Boosting Machines (GBM)_Hybrid Sampli...
97 Gradient Boosting Machines (GBM)_Hybrid Sampli...
98 Gradient Boosting Machines (GBM)_Hybrid Sampli...
99 Gradient Boosting Machines (GBM)_Hybrid Sampli...
100 Gradient Boosting Machines (GBM)_Hybrid Sampli...
101 Gradient Boosting Machines (GBM)_Hybrid Sampli...
102 Gradient Boosting Machines (GBM)_Hybrid Sampli...
103 Gradient Boosting Machines (GBM)_Hybrid Sampli...
104 Gradient Boosting Machines (GBM)_Hybrid Sampli...
105 Gradient Boosting Machines (GBM)_Hybrid Sampli...
106 Gradient Boosting Machines (GBM)_Hybrid Sampli...
107 Gradient Boosting Machines (GBM)_Hybrid Sampli...
108 Gradient Boosting Machines (GBM)_Hybrid Sampli...
109 Gradient Boosting Machines (GBM)_Hybrid Sampli...
110 Gradient Boosting Machines (GBM)_Hybrid Sampli...
111 Gradient Boosting Machines (GBM)_Hybrid Sampli...
112 Gradient Boosting Machines (GBM)_Hybrid Sampli...
113 Gradient Boosting Machines (GBM)_Hybrid Sampli...
114 Gradient Boosting Machines (GBM)_Hybrid Sampli...
115 Gradient Boosting Machines (GBM)_Hybrid Sampli...
116 Gradient Boosting Machines (GBM)_Hybrid Sampli...
117 Gradient Boosting Machines (GBM)_Hybrid Sampli...
118 Gradient Boosting Machines (GBM)_Hybrid Sampli...
119 Gradient Boosting Machines (GBM)_Hybrid Sampli...
120 Gradient Boosting Machines (GBM)_Hybrid Sampli...
121 Gradient Boosting Machines (GBM)_Hybrid Sampli...
122 Gradient Boosting Machines (GBM)_Hybrid Sampli...
123 Gradient Boosting Machines (GBM)_Hybrid Sampli...
124 Gradient Boosting Machines (GBM)_Hybrid Sampli...
125 Gradient Boosting Machines (GBM)_Hybrid Sampli...
126 Gradient Boosting Machines (GBM)_Hybrid Sampli...
127 Gradient Boosting Machines (GBM)_Hybrid Sampli...
128 Gradient Boosting Machines (GBM)_Hybrid Sampli...
129 Gradient Boosting Machines (GBM)_Hybrid Sampli...
130 Gradient Boosting Machines (GBM)_Hybrid Sampli...
131 Gradient Boosting Machines (GBM)_Hybrid Sampli...
132 Gradient Boosting Machines (GBM)_Hybrid Sampli...
133 Gradient Boosting Machines (GBM)_Hybrid Sampli...
134 Gradient Boosting Machines (GBM)_Hybrid Sampli... ,
Algorithm Metrics Hyperparameters \
0 Gradient Boosting Machines (GBM) 0.822228 NaN
1 Gradient Boosting Machines (GBM) 0.118467 NaN
2 Gradient Boosting Machines (GBM) 0.286920 NaN
3 Gradient Boosting Machines (GBM) 0.167694 NaN
4 Gradient Boosting Machines (GBM) 0.640819 NaN
5 Gradient Boosting Machines (GBM) 0.218757 NaN
6 Gradient Boosting Machines (GBM) 0.281637 NaN
7 Gradient Boosting Machines (GBM) NaN 0.0
8 Gradient Boosting Machines (GBM) NaN friedman_mse
9 Gradient Boosting Machines (GBM) NaN None
10 Gradient Boosting Machines (GBM) NaN 0.1
11 Gradient Boosting Machines (GBM) NaN log_loss
12 Gradient Boosting Machines (GBM) NaN 3
13 Gradient Boosting Machines (GBM) NaN None
14 Gradient Boosting Machines (GBM) NaN None
15 Gradient Boosting Machines (GBM) NaN 0.0
16 Gradient Boosting Machines (GBM) NaN 1
17 Gradient Boosting Machines (GBM) NaN 2
18 Gradient Boosting Machines (GBM) NaN 0.0
19 Gradient Boosting Machines (GBM) NaN 100
20 Gradient Boosting Machines (GBM) NaN None
21 Gradient Boosting Machines (GBM) NaN None
22 Gradient Boosting Machines (GBM) NaN 1.0
23 Gradient Boosting Machines (GBM) NaN 0.0001
24 Gradient Boosting Machines (GBM) NaN 0.1
25 Gradient Boosting Machines (GBM) NaN 0
26 Gradient Boosting Machines (GBM) NaN False
27 Gradient Boosting Machines (GBM) 0.837240 NaN
28 Gradient Boosting Machines (GBM) 0.103147 NaN
29 Gradient Boosting Machines (GBM) 0.359756 NaN
30 Gradient Boosting Machines (GBM) 0.160326 NaN
31 Gradient Boosting Machines (GBM) 0.674597 NaN
32 Gradient Boosting Machines (GBM) 0.260738 NaN
33 Gradient Boosting Machines (GBM) 0.349194 NaN
34 Gradient Boosting Machines (GBM) NaN 0.0
35 Gradient Boosting Machines (GBM) NaN friedman_mse
36 Gradient Boosting Machines (GBM) NaN None
37 Gradient Boosting Machines (GBM) NaN 0.1
38 Gradient Boosting Machines (GBM) NaN log_loss
39 Gradient Boosting Machines (GBM) NaN 3
40 Gradient Boosting Machines (GBM) NaN None
41 Gradient Boosting Machines (GBM) NaN None
42 Gradient Boosting Machines (GBM) NaN 0.0
43 Gradient Boosting Machines (GBM) NaN 1
44 Gradient Boosting Machines (GBM) NaN 2
45 Gradient Boosting Machines (GBM) NaN 0.0
46 Gradient Boosting Machines (GBM) NaN 100
47 Gradient Boosting Machines (GBM) NaN None
48 Gradient Boosting Machines (GBM) NaN None
49 Gradient Boosting Machines (GBM) NaN 1.0
50 Gradient Boosting Machines (GBM) NaN 0.0001
51 Gradient Boosting Machines (GBM) NaN 0.1
52 Gradient Boosting Machines (GBM) NaN 0
53 Gradient Boosting Machines (GBM) NaN False
54 Gradient Boosting Machines (GBM) 0.833816 NaN
55 Gradient Boosting Machines (GBM) 0.106383 NaN
56 Gradient Boosting Machines (GBM) 0.320856 NaN
57 Gradient Boosting Machines (GBM) 0.159787 NaN
58 Gradient Boosting Machines (GBM) 0.670028 NaN
59 Gradient Boosting Machines (GBM) 0.253799 NaN
60 Gradient Boosting Machines (GBM) 0.340055 NaN
61 Gradient Boosting Machines (GBM) NaN 0.0
62 Gradient Boosting Machines (GBM) NaN friedman_mse
63 Gradient Boosting Machines (GBM) NaN None
64 Gradient Boosting Machines (GBM) NaN 0.1
65 Gradient Boosting Machines (GBM) NaN log_loss
66 Gradient Boosting Machines (GBM) NaN 3
67 Gradient Boosting Machines (GBM) NaN None
68 Gradient Boosting Machines (GBM) NaN None
69 Gradient Boosting Machines (GBM) NaN 0.0
70 Gradient Boosting Machines (GBM) NaN 1
71 Gradient Boosting Machines (GBM) NaN 2
72 Gradient Boosting Machines (GBM) NaN 0.0
73 Gradient Boosting Machines (GBM) NaN 100
74 Gradient Boosting Machines (GBM) NaN None
75 Gradient Boosting Machines (GBM) NaN None
76 Gradient Boosting Machines (GBM) NaN 1.0
77 Gradient Boosting Machines (GBM) NaN 0.0001
78 Gradient Boosting Machines (GBM) NaN 0.1
79 Gradient Boosting Machines (GBM) NaN 0
80 Gradient Boosting Machines (GBM) NaN False
81 Gradient Boosting Machines (GBM) 0.827713 NaN
82 Gradient Boosting Machines (GBM) 0.107877 NaN
83 Gradient Boosting Machines (GBM) 0.321429 NaN
84 Gradient Boosting Machines (GBM) 0.161538 NaN
85 Gradient Boosting Machines (GBM) 0.662846 NaN
86 Gradient Boosting Machines (GBM) 0.250867 NaN
87 Gradient Boosting Machines (GBM) 0.325692 NaN
88 Gradient Boosting Machines (GBM) NaN 0.0
89 Gradient Boosting Machines (GBM) NaN friedman_mse
90 Gradient Boosting Machines (GBM) NaN None
91 Gradient Boosting Machines (GBM) NaN 0.1
92 Gradient Boosting Machines (GBM) NaN log_loss
93 Gradient Boosting Machines (GBM) NaN 3
94 Gradient Boosting Machines (GBM) NaN None
95 Gradient Boosting Machines (GBM) NaN None
96 Gradient Boosting Machines (GBM) NaN 0.0
97 Gradient Boosting Machines (GBM) NaN 1
98 Gradient Boosting Machines (GBM) NaN 2
99 Gradient Boosting Machines (GBM) NaN 0.0
100 Gradient Boosting Machines (GBM) NaN 100
101 Gradient Boosting Machines (GBM) NaN None
102 Gradient Boosting Machines (GBM) NaN None
103 Gradient Boosting Machines (GBM) NaN 1.0
104 Gradient Boosting Machines (GBM) NaN 0.0001
105 Gradient Boosting Machines (GBM) NaN 0.1
106 Gradient Boosting Machines (GBM) NaN 0
107 Gradient Boosting Machines (GBM) NaN False
108 Gradient Boosting Machines (GBM) 0.825869 NaN
109 Gradient Boosting Machines (GBM) 0.111693 NaN
110 Gradient Boosting Machines (GBM) 0.296296 NaN
111 Gradient Boosting Machines (GBM) 0.162231 NaN
112 Gradient Boosting Machines (GBM) 0.670936 NaN
113 Gradient Boosting Machines (GBM) 0.285216 NaN
114 Gradient Boosting Machines (GBM) 0.341872 NaN
115 Gradient Boosting Machines (GBM) NaN 0.0
116 Gradient Boosting Machines (GBM) NaN friedman_mse
117 Gradient Boosting Machines (GBM) NaN None
118 Gradient Boosting Machines (GBM) NaN 0.1
119 Gradient Boosting Machines (GBM) NaN log_loss
120 Gradient Boosting Machines (GBM) NaN 3
121 Gradient Boosting Machines (GBM) NaN None
122 Gradient Boosting Machines (GBM) NaN None
123 Gradient Boosting Machines (GBM) NaN 0.0
124 Gradient Boosting Machines (GBM) NaN 1
125 Gradient Boosting Machines (GBM) NaN 2
126 Gradient Boosting Machines (GBM) NaN 0.0
127 Gradient Boosting Machines (GBM) NaN 100
128 Gradient Boosting Machines (GBM) NaN None
129 Gradient Boosting Machines (GBM) NaN None
130 Gradient Boosting Machines (GBM) NaN 1.0
131 Gradient Boosting Machines (GBM) NaN 0.0001
132 Gradient Boosting Machines (GBM) NaN 0.1
133 Gradient Boosting Machines (GBM) NaN 0
134 Gradient Boosting Machines (GBM) NaN False
Imputation Technique Imbalance Class Technique \
0 Median Imputation Hybrid Sampling (SMOTEENN)
1 Median Imputation Hybrid Sampling (SMOTEENN)
2 Median Imputation Hybrid Sampling (SMOTEENN)
3 Median Imputation Hybrid Sampling (SMOTEENN)
4 Median Imputation Hybrid Sampling (SMOTEENN)
5 Median Imputation Hybrid Sampling (SMOTEENN)
6 Median Imputation Hybrid Sampling (SMOTEENN)
7 Median Imputation Hybrid Sampling (SMOTEENN)
8 Median Imputation Hybrid Sampling (SMOTEENN)
9 Median Imputation Hybrid Sampling (SMOTEENN)
10 Median Imputation Hybrid Sampling (SMOTEENN)
11 Median Imputation Hybrid Sampling (SMOTEENN)
12 Median Imputation Hybrid Sampling (SMOTEENN)
13 Median Imputation Hybrid Sampling (SMOTEENN)
14 Median Imputation Hybrid Sampling (SMOTEENN)
15 Median Imputation Hybrid Sampling (SMOTEENN)
16 Median Imputation Hybrid Sampling (SMOTEENN)
17 Median Imputation Hybrid Sampling (SMOTEENN)
18 Median Imputation Hybrid Sampling (SMOTEENN)
19 Median Imputation Hybrid Sampling (SMOTEENN)
20 Median Imputation Hybrid Sampling (SMOTEENN)
21 Median Imputation Hybrid Sampling (SMOTEENN)
22 Median Imputation Hybrid Sampling (SMOTEENN)
23 Median Imputation Hybrid Sampling (SMOTEENN)
24 Median Imputation Hybrid Sampling (SMOTEENN)
25 Median Imputation Hybrid Sampling (SMOTEENN)
26 Median Imputation Hybrid Sampling (SMOTEENN)
27 Median Imputation Hybrid Sampling (SMOTEENN)
28 Median Imputation Hybrid Sampling (SMOTEENN)
29 Median Imputation Hybrid Sampling (SMOTEENN)
30 Median Imputation Hybrid Sampling (SMOTEENN)
31 Median Imputation Hybrid Sampling (SMOTEENN)
32 Median Imputation Hybrid Sampling (SMOTEENN)
33 Median Imputation Hybrid Sampling (SMOTEENN)
34 Median Imputation Hybrid Sampling (SMOTEENN)
35 Median Imputation Hybrid Sampling (SMOTEENN)
36 Median Imputation Hybrid Sampling (SMOTEENN)
37 Median Imputation Hybrid Sampling (SMOTEENN)
38 Median Imputation Hybrid Sampling (SMOTEENN)
39 Median Imputation Hybrid Sampling (SMOTEENN)
40 Median Imputation Hybrid Sampling (SMOTEENN)
41 Median Imputation Hybrid Sampling (SMOTEENN)
42 Median Imputation Hybrid Sampling (SMOTEENN)
43 Median Imputation Hybrid Sampling (SMOTEENN)
44 Median Imputation Hybrid Sampling (SMOTEENN)
45 Median Imputation Hybrid Sampling (SMOTEENN)
46 Median Imputation Hybrid Sampling (SMOTEENN)
47 Median Imputation Hybrid Sampling (SMOTEENN)
48 Median Imputation Hybrid Sampling (SMOTEENN)
49 Median Imputation Hybrid Sampling (SMOTEENN)
50 Median Imputation Hybrid Sampling (SMOTEENN)
51 Median Imputation Hybrid Sampling (SMOTEENN)
52 Median Imputation Hybrid Sampling (SMOTEENN)
53 Median Imputation Hybrid Sampling (SMOTEENN)
54 Median Imputation Hybrid Sampling (SMOTEENN)
55 Median Imputation Hybrid Sampling (SMOTEENN)
56 Median Imputation Hybrid Sampling (SMOTEENN)
57 Median Imputation Hybrid Sampling (SMOTEENN)
58 Median Imputation Hybrid Sampling (SMOTEENN)
59 Median Imputation Hybrid Sampling (SMOTEENN)
60 Median Imputation Hybrid Sampling (SMOTEENN)
61 Median Imputation Hybrid Sampling (SMOTEENN)
62 Median Imputation Hybrid Sampling (SMOTEENN)
63 Median Imputation Hybrid Sampling (SMOTEENN)
64 Median Imputation Hybrid Sampling (SMOTEENN)
65 Median Imputation Hybrid Sampling (SMOTEENN)
66 Median Imputation Hybrid Sampling (SMOTEENN)
67 Median Imputation Hybrid Sampling (SMOTEENN)
68 Median Imputation Hybrid Sampling (SMOTEENN)
69 Median Imputation Hybrid Sampling (SMOTEENN)
70 Median Imputation Hybrid Sampling (SMOTEENN)
71 Median Imputation Hybrid Sampling (SMOTEENN)
72 Median Imputation Hybrid Sampling (SMOTEENN)
73 Median Imputation Hybrid Sampling (SMOTEENN)
74 Median Imputation Hybrid Sampling (SMOTEENN)
75 Median Imputation Hybrid Sampling (SMOTEENN)
76 Median Imputation Hybrid Sampling (SMOTEENN)
77 Median Imputation Hybrid Sampling (SMOTEENN)
78 Median Imputation Hybrid Sampling (SMOTEENN)
79 Median Imputation Hybrid Sampling (SMOTEENN)
80 Median Imputation Hybrid Sampling (SMOTEENN)
81 Median Imputation Hybrid Sampling (SMOTEENN)
82 Median Imputation Hybrid Sampling (SMOTEENN)
83 Median Imputation Hybrid Sampling (SMOTEENN)
84 Median Imputation Hybrid Sampling (SMOTEENN)
85 Median Imputation Hybrid Sampling (SMOTEENN)
86 Median Imputation Hybrid Sampling (SMOTEENN)
87 Median Imputation Hybrid Sampling (SMOTEENN)
88 Median Imputation Hybrid Sampling (SMOTEENN)
89 Median Imputation Hybrid Sampling (SMOTEENN)
90 Median Imputation Hybrid Sampling (SMOTEENN)
91 Median Imputation Hybrid Sampling (SMOTEENN)
92 Median Imputation Hybrid Sampling (SMOTEENN)
93 Median Imputation Hybrid Sampling (SMOTEENN)
94 Median Imputation Hybrid Sampling (SMOTEENN)
95 Median Imputation Hybrid Sampling (SMOTEENN)
96 Median Imputation Hybrid Sampling (SMOTEENN)
97 Median Imputation Hybrid Sampling (SMOTEENN)
98 Median Imputation Hybrid Sampling (SMOTEENN)
99 Median Imputation Hybrid Sampling (SMOTEENN)
100 Median Imputation Hybrid Sampling (SMOTEENN)
101 Median Imputation Hybrid Sampling (SMOTEENN)
102 Median Imputation Hybrid Sampling (SMOTEENN)
103 Median Imputation Hybrid Sampling (SMOTEENN)
104 Median Imputation Hybrid Sampling (SMOTEENN)
105 Median Imputation Hybrid Sampling (SMOTEENN)
106 Median Imputation Hybrid Sampling (SMOTEENN)
107 Median Imputation Hybrid Sampling (SMOTEENN)
108 Median Imputation Hybrid Sampling (SMOTEENN)
109 Median Imputation Hybrid Sampling (SMOTEENN)
110 Median Imputation Hybrid Sampling (SMOTEENN)
111 Median Imputation Hybrid Sampling (SMOTEENN)
112 Median Imputation Hybrid Sampling (SMOTEENN)
113 Median Imputation Hybrid Sampling (SMOTEENN)
114 Median Imputation Hybrid Sampling (SMOTEENN)
115 Median Imputation Hybrid Sampling (SMOTEENN)
116 Median Imputation Hybrid Sampling (SMOTEENN)
117 Median Imputation Hybrid Sampling (SMOTEENN)
118 Median Imputation Hybrid Sampling (SMOTEENN)
119 Median Imputation Hybrid Sampling (SMOTEENN)
120 Median Imputation Hybrid Sampling (SMOTEENN)
121 Median Imputation Hybrid Sampling (SMOTEENN)
122 Median Imputation Hybrid Sampling (SMOTEENN)
123 Median Imputation Hybrid Sampling (SMOTEENN)
124 Median Imputation Hybrid Sampling (SMOTEENN)
125 Median Imputation Hybrid Sampling (SMOTEENN)
126 Median Imputation Hybrid Sampling (SMOTEENN)
127 Median Imputation Hybrid Sampling (SMOTEENN)
128 Median Imputation Hybrid Sampling (SMOTEENN)
129 Median Imputation Hybrid Sampling (SMOTEENN)
130 Median Imputation Hybrid Sampling (SMOTEENN)
131 Median Imputation Hybrid Sampling (SMOTEENN)
132 Median Imputation Hybrid Sampling (SMOTEENN)
133 Median Imputation Hybrid Sampling (SMOTEENN)
134 Median Imputation Hybrid Sampling (SMOTEENN)
Model Unique Code
0 Gradient Boosting Machines (GBM)_Hybrid Sampli...
1 Gradient Boosting Machines (GBM)_Hybrid Sampli...
2 Gradient Boosting Machines (GBM)_Hybrid Sampli...
3 Gradient Boosting Machines (GBM)_Hybrid Sampli...
4 Gradient Boosting Machines (GBM)_Hybrid Sampli...
5 Gradient Boosting Machines (GBM)_Hybrid Sampli...
6 Gradient Boosting Machines (GBM)_Hybrid Sampli...
7 Gradient Boosting Machines (GBM)_Hybrid Sampli...
8 Gradient Boosting Machines (GBM)_Hybrid Sampli...
9 Gradient Boosting Machines (GBM)_Hybrid Sampli...
10 Gradient Boosting Machines (GBM)_Hybrid Sampli...
11 Gradient Boosting Machines (GBM)_Hybrid Sampli...
12 Gradient Boosting Machines (GBM)_Hybrid Sampli...
13 Gradient Boosting Machines (GBM)_Hybrid Sampli...
14 Gradient Boosting Machines (GBM)_Hybrid Sampli...
15 Gradient Boosting Machines (GBM)_Hybrid Sampli...
16 Gradient Boosting Machines (GBM)_Hybrid Sampli...
17 Gradient Boosting Machines (GBM)_Hybrid Sampli...
18 Gradient Boosting Machines (GBM)_Hybrid Sampli...
19 Gradient Boosting Machines (GBM)_Hybrid Sampli...
20 Gradient Boosting Machines (GBM)_Hybrid Sampli...
21 Gradient Boosting Machines (GBM)_Hybrid Sampli...
22 Gradient Boosting Machines (GBM)_Hybrid Sampli...
23 Gradient Boosting Machines (GBM)_Hybrid Sampli...
24 Gradient Boosting Machines (GBM)_Hybrid Sampli...
25 Gradient Boosting Machines (GBM)_Hybrid Sampli...
26 Gradient Boosting Machines (GBM)_Hybrid Sampli...
27 Gradient Boosting Machines (GBM)_Hybrid Sampli...
28 Gradient Boosting Machines (GBM)_Hybrid Sampli...
29 Gradient Boosting Machines (GBM)_Hybrid Sampli...
30 Gradient Boosting Machines (GBM)_Hybrid Sampli...
31 Gradient Boosting Machines (GBM)_Hybrid Sampli...
32 Gradient Boosting Machines (GBM)_Hybrid Sampli...
33 Gradient Boosting Machines (GBM)_Hybrid Sampli...
34 Gradient Boosting Machines (GBM)_Hybrid Sampli...
35 Gradient Boosting Machines (GBM)_Hybrid Sampli...
36 Gradient Boosting Machines (GBM)_Hybrid Sampli...
37 Gradient Boosting Machines (GBM)_Hybrid Sampli...
38 Gradient Boosting Machines (GBM)_Hybrid Sampli...
39 Gradient Boosting Machines (GBM)_Hybrid Sampli...
40 Gradient Boosting Machines (GBM)_Hybrid Sampli...
41 Gradient Boosting Machines (GBM)_Hybrid Sampli...
42 Gradient Boosting Machines (GBM)_Hybrid Sampli...
43 Gradient Boosting Machines (GBM)_Hybrid Sampli...
44 Gradient Boosting Machines (GBM)_Hybrid Sampli...
45 Gradient Boosting Machines (GBM)_Hybrid Sampli...
46 Gradient Boosting Machines (GBM)_Hybrid Sampli...
47 Gradient Boosting Machines (GBM)_Hybrid Sampli...
48 Gradient Boosting Machines (GBM)_Hybrid Sampli...
49 Gradient Boosting Machines (GBM)_Hybrid Sampli...
50 Gradient Boosting Machines (GBM)_Hybrid Sampli...
51 Gradient Boosting Machines (GBM)_Hybrid Sampli...
52 Gradient Boosting Machines (GBM)_Hybrid Sampli...
53 Gradient Boosting Machines (GBM)_Hybrid Sampli...
54 Gradient Boosting Machines (GBM)_Hybrid Sampli...
55 Gradient Boosting Machines (GBM)_Hybrid Sampli...
56 Gradient Boosting Machines (GBM)_Hybrid Sampli...
57 Gradient Boosting Machines (GBM)_Hybrid Sampli...
58 Gradient Boosting Machines (GBM)_Hybrid Sampli...
59 Gradient Boosting Machines (GBM)_Hybrid Sampli...
60 Gradient Boosting Machines (GBM)_Hybrid Sampli...
61 Gradient Boosting Machines (GBM)_Hybrid Sampli...
62 Gradient Boosting Machines (GBM)_Hybrid Sampli...
63 Gradient Boosting Machines (GBM)_Hybrid Sampli...
64 Gradient Boosting Machines (GBM)_Hybrid Sampli...
65 Gradient Boosting Machines (GBM)_Hybrid Sampli...
66 Gradient Boosting Machines (GBM)_Hybrid Sampli...
67 Gradient Boosting Machines (GBM)_Hybrid Sampli...
68 Gradient Boosting Machines (GBM)_Hybrid Sampli...
69 Gradient Boosting Machines (GBM)_Hybrid Sampli...
70 Gradient Boosting Machines (GBM)_Hybrid Sampli...
71 Gradient Boosting Machines (GBM)_Hybrid Sampli...
72 Gradient Boosting Machines (GBM)_Hybrid Sampli...
73 Gradient Boosting Machines (GBM)_Hybrid Sampli...
74 Gradient Boosting Machines (GBM)_Hybrid Sampli...
75 Gradient Boosting Machines (GBM)_Hybrid Sampli...
76 Gradient Boosting Machines (GBM)_Hybrid Sampli...
77 Gradient Boosting Machines (GBM)_Hybrid Sampli...
78 Gradient Boosting Machines (GBM)_Hybrid Sampli...
79 Gradient Boosting Machines (GBM)_Hybrid Sampli...
80 Gradient Boosting Machines (GBM)_Hybrid Sampli...
81 Gradient Boosting Machines (GBM)_Hybrid Sampli...
82 Gradient Boosting Machines (GBM)_Hybrid Sampli...
83 Gradient Boosting Machines (GBM)_Hybrid Sampli...
84 Gradient Boosting Machines (GBM)_Hybrid Sampli...
85 Gradient Boosting Machines (GBM)_Hybrid Sampli...
86 Gradient Boosting Machines (GBM)_Hybrid Sampli...
87 Gradient Boosting Machines (GBM)_Hybrid Sampli...
88 Gradient Boosting Machines (GBM)_Hybrid Sampli...
89 Gradient Boosting Machines (GBM)_Hybrid Sampli...
90 Gradient Boosting Machines (GBM)_Hybrid Sampli...
91 Gradient Boosting Machines (GBM)_Hybrid Sampli...
92 Gradient Boosting Machines (GBM)_Hybrid Sampli...
93 Gradient Boosting Machines (GBM)_Hybrid Sampli...
94 Gradient Boosting Machines (GBM)_Hybrid Sampli...
95 Gradient Boosting Machines (GBM)_Hybrid Sampli...
96 Gradient Boosting Machines (GBM)_Hybrid Sampli...
97 Gradient Boosting Machines (GBM)_Hybrid Sampli...
98 Gradient Boosting Machines (GBM)_Hybrid Sampli...
99 Gradient Boosting Machines (GBM)_Hybrid Sampli...
100 Gradient Boosting Machines (GBM)_Hybrid Sampli...
101 Gradient Boosting Machines (GBM)_Hybrid Sampli...
102 Gradient Boosting Machines (GBM)_Hybrid Sampli...
103 Gradient Boosting Machines (GBM)_Hybrid Sampli...
104 Gradient Boosting Machines (GBM)_Hybrid Sampli...
105 Gradient Boosting Machines (GBM)_Hybrid Sampli...
106 Gradient Boosting Machines (GBM)_Hybrid Sampli...
107 Gradient Boosting Machines (GBM)_Hybrid Sampli...
108 Gradient Boosting Machines (GBM)_Hybrid Sampli...
109 Gradient Boosting Machines (GBM)_Hybrid Sampli...
110 Gradient Boosting Machines (GBM)_Hybrid Sampli...
111 Gradient Boosting Machines (GBM)_Hybrid Sampli...
112 Gradient Boosting Machines (GBM)_Hybrid Sampli...
113 Gradient Boosting Machines (GBM)_Hybrid Sampli...
114 Gradient Boosting Machines (GBM)_Hybrid Sampli...
115 Gradient Boosting Machines (GBM)_Hybrid Sampli...
116 Gradient Boosting Machines (GBM)_Hybrid Sampli...
117 Gradient Boosting Machines (GBM)_Hybrid Sampli...
118 Gradient Boosting Machines (GBM)_Hybrid Sampli...
119 Gradient Boosting Machines (GBM)_Hybrid Sampli...
120 Gradient Boosting Machines (GBM)_Hybrid Sampli...
121 Gradient Boosting Machines (GBM)_Hybrid Sampli...
122 Gradient Boosting Machines (GBM)_Hybrid Sampli...
123 Gradient Boosting Machines (GBM)_Hybrid Sampli...
124 Gradient Boosting Machines (GBM)_Hybrid Sampli...
125 Gradient Boosting Machines (GBM)_Hybrid Sampli...
126 Gradient Boosting Machines (GBM)_Hybrid Sampli...
127 Gradient Boosting Machines (GBM)_Hybrid Sampli...
128 Gradient Boosting Machines (GBM)_Hybrid Sampli...
129 Gradient Boosting Machines (GBM)_Hybrid Sampli...
130 Gradient Boosting Machines (GBM)_Hybrid Sampli...
131 Gradient Boosting Machines (GBM)_Hybrid Sampli...
132 Gradient Boosting Machines (GBM)_Hybrid Sampli...
133 Gradient Boosting Machines (GBM)_Hybrid Sampli...
134 Gradient Boosting Machines (GBM)_Hybrid Sampli... ,
Algorithm Metrics Hyperparameters \
0 Gradient Boosting Machines (GBM) 0.838030 NaN
1 Gradient Boosting Machines (GBM) 0.111111 NaN
2 Gradient Boosting Machines (GBM) 0.227848 NaN
3 Gradient Boosting Machines (GBM) 0.149378 NaN
4 Gradient Boosting Machines (GBM) 0.654424 NaN
5 Gradient Boosting Machines (GBM) 0.253470 NaN
6 Gradient Boosting Machines (GBM) 0.308849 NaN
7 Gradient Boosting Machines (GBM) NaN 0.0
8 Gradient Boosting Machines (GBM) NaN friedman_mse
9 Gradient Boosting Machines (GBM) NaN None
10 Gradient Boosting Machines (GBM) NaN 0.1
11 Gradient Boosting Machines (GBM) NaN log_loss
12 Gradient Boosting Machines (GBM) NaN 3
13 Gradient Boosting Machines (GBM) NaN None
14 Gradient Boosting Machines (GBM) NaN None
15 Gradient Boosting Machines (GBM) NaN 0.0
16 Gradient Boosting Machines (GBM) NaN 1
17 Gradient Boosting Machines (GBM) NaN 2
18 Gradient Boosting Machines (GBM) NaN 0.0
19 Gradient Boosting Machines (GBM) NaN 100
20 Gradient Boosting Machines (GBM) NaN None
21 Gradient Boosting Machines (GBM) NaN None
22 Gradient Boosting Machines (GBM) NaN 1.0
23 Gradient Boosting Machines (GBM) NaN 0.0001
24 Gradient Boosting Machines (GBM) NaN 0.1
25 Gradient Boosting Machines (GBM) NaN 0
26 Gradient Boosting Machines (GBM) NaN False
27 Gradient Boosting Machines (GBM) 0.852515 NaN
28 Gradient Boosting Machines (GBM) 0.108696 NaN
29 Gradient Boosting Machines (GBM) 0.335366 NaN
30 Gradient Boosting Machines (GBM) 0.164179 NaN
31 Gradient Boosting Machines (GBM) 0.698704 NaN
32 Gradient Boosting Machines (GBM) 0.306696 NaN
33 Gradient Boosting Machines (GBM) 0.397409 NaN
34 Gradient Boosting Machines (GBM) NaN 0.0
35 Gradient Boosting Machines (GBM) NaN friedman_mse
36 Gradient Boosting Machines (GBM) NaN None
37 Gradient Boosting Machines (GBM) NaN 0.1
38 Gradient Boosting Machines (GBM) NaN log_loss
39 Gradient Boosting Machines (GBM) NaN 3
40 Gradient Boosting Machines (GBM) NaN None
41 Gradient Boosting Machines (GBM) NaN None
42 Gradient Boosting Machines (GBM) NaN 0.0
43 Gradient Boosting Machines (GBM) NaN 1
44 Gradient Boosting Machines (GBM) NaN 2
45 Gradient Boosting Machines (GBM) NaN 0.0
46 Gradient Boosting Machines (GBM) NaN 100
47 Gradient Boosting Machines (GBM) NaN None
48 Gradient Boosting Machines (GBM) NaN None
49 Gradient Boosting Machines (GBM) NaN 1.0
50 Gradient Boosting Machines (GBM) NaN 0.0001
51 Gradient Boosting Machines (GBM) NaN 0.1
52 Gradient Boosting Machines (GBM) NaN 0
53 Gradient Boosting Machines (GBM) NaN False
54 Gradient Boosting Machines (GBM) 0.842244 NaN
55 Gradient Boosting Machines (GBM) 0.103846 NaN
56 Gradient Boosting Machines (GBM) 0.288770 NaN
57 Gradient Boosting Machines (GBM) 0.152758 NaN
58 Gradient Boosting Machines (GBM) 0.669725 NaN
59 Gradient Boosting Machines (GBM) 0.285719 NaN
60 Gradient Boosting Machines (GBM) 0.339451 NaN
61 Gradient Boosting Machines (GBM) NaN 0.0
62 Gradient Boosting Machines (GBM) NaN friedman_mse
63 Gradient Boosting Machines (GBM) NaN None
64 Gradient Boosting Machines (GBM) NaN 0.1
65 Gradient Boosting Machines (GBM) NaN log_loss
66 Gradient Boosting Machines (GBM) NaN 3
67 Gradient Boosting Machines (GBM) NaN None
68 Gradient Boosting Machines (GBM) NaN None
69 Gradient Boosting Machines (GBM) NaN 0.0
70 Gradient Boosting Machines (GBM) NaN 1
71 Gradient Boosting Machines (GBM) NaN 2
72 Gradient Boosting Machines (GBM) NaN 0.0
73 Gradient Boosting Machines (GBM) NaN 100
74 Gradient Boosting Machines (GBM) NaN None
75 Gradient Boosting Machines (GBM) NaN None
76 Gradient Boosting Machines (GBM) NaN 1.0
77 Gradient Boosting Machines (GBM) NaN 0.0001
78 Gradient Boosting Machines (GBM) NaN 0.1
79 Gradient Boosting Machines (GBM) NaN 0
80 Gradient Boosting Machines (GBM) NaN False
81 Gradient Boosting Machines (GBM) 0.851159 NaN
82 Gradient Boosting Machines (GBM) 0.125761 NaN
83 Gradient Boosting Machines (GBM) 0.316327 NaN
84 Gradient Boosting Machines (GBM) 0.179971 NaN
85 Gradient Boosting Machines (GBM) 0.679990 NaN
86 Gradient Boosting Machines (GBM) 0.297358 NaN
87 Gradient Boosting Machines (GBM) 0.359980 NaN
88 Gradient Boosting Machines (GBM) NaN 0.0
89 Gradient Boosting Machines (GBM) NaN friedman_mse
90 Gradient Boosting Machines (GBM) NaN None
91 Gradient Boosting Machines (GBM) NaN 0.1
92 Gradient Boosting Machines (GBM) NaN log_loss
93 Gradient Boosting Machines (GBM) NaN 3
94 Gradient Boosting Machines (GBM) NaN None
95 Gradient Boosting Machines (GBM) NaN None
96 Gradient Boosting Machines (GBM) NaN 0.0
97 Gradient Boosting Machines (GBM) NaN 1
98 Gradient Boosting Machines (GBM) NaN 2
99 Gradient Boosting Machines (GBM) NaN 0.0
100 Gradient Boosting Machines (GBM) NaN 100
101 Gradient Boosting Machines (GBM) NaN None
102 Gradient Boosting Machines (GBM) NaN None
103 Gradient Boosting Machines (GBM) NaN 1.0
104 Gradient Boosting Machines (GBM) NaN 0.0001
105 Gradient Boosting Machines (GBM) NaN 0.1
106 Gradient Boosting Machines (GBM) NaN 0
107 Gradient Boosting Machines (GBM) NaN False
108 Gradient Boosting Machines (GBM) 0.855901 NaN
109 Gradient Boosting Machines (GBM) 0.139434 NaN
110 Gradient Boosting Machines (GBM) 0.296296 NaN
111 Gradient Boosting Machines (GBM) 0.189630 NaN
112 Gradient Boosting Machines (GBM) 0.683147 NaN
113 Gradient Boosting Machines (GBM) 0.291118 NaN
114 Gradient Boosting Machines (GBM) 0.366294 NaN
115 Gradient Boosting Machines (GBM) NaN 0.0
116 Gradient Boosting Machines (GBM) NaN friedman_mse
117 Gradient Boosting Machines (GBM) NaN None
118 Gradient Boosting Machines (GBM) NaN 0.1
119 Gradient Boosting Machines (GBM) NaN log_loss
120 Gradient Boosting Machines (GBM) NaN 3
121 Gradient Boosting Machines (GBM) NaN None
122 Gradient Boosting Machines (GBM) NaN None
123 Gradient Boosting Machines (GBM) NaN 0.0
124 Gradient Boosting Machines (GBM) NaN 1
125 Gradient Boosting Machines (GBM) NaN 2
126 Gradient Boosting Machines (GBM) NaN 0.0
127 Gradient Boosting Machines (GBM) NaN 100
128 Gradient Boosting Machines (GBM) NaN None
129 Gradient Boosting Machines (GBM) NaN None
130 Gradient Boosting Machines (GBM) NaN 1.0
131 Gradient Boosting Machines (GBM) NaN 0.0001
132 Gradient Boosting Machines (GBM) NaN 0.1
133 Gradient Boosting Machines (GBM) NaN 0
134 Gradient Boosting Machines (GBM) NaN False
Imputation Technique Imbalance Class Technique \
0 Zero Imputation Hybrid Sampling (SMOTETomek)
1 Zero Imputation Hybrid Sampling (SMOTETomek)
2 Zero Imputation Hybrid Sampling (SMOTETomek)
3 Zero Imputation Hybrid Sampling (SMOTETomek)
4 Zero Imputation Hybrid Sampling (SMOTETomek)
5 Zero Imputation Hybrid Sampling (SMOTETomek)
6 Zero Imputation Hybrid Sampling (SMOTETomek)
7 Zero Imputation Hybrid Sampling (SMOTETomek)
8 Zero Imputation Hybrid Sampling (SMOTETomek)
9 Zero Imputation Hybrid Sampling (SMOTETomek)
10 Zero Imputation Hybrid Sampling (SMOTETomek)
11 Zero Imputation Hybrid Sampling (SMOTETomek)
12 Zero Imputation Hybrid Sampling (SMOTETomek)
13 Zero Imputation Hybrid Sampling (SMOTETomek)
14 Zero Imputation Hybrid Sampling (SMOTETomek)
15 Zero Imputation Hybrid Sampling (SMOTETomek)
16 Zero Imputation Hybrid Sampling (SMOTETomek)
17 Zero Imputation Hybrid Sampling (SMOTETomek)
18 Zero Imputation Hybrid Sampling (SMOTETomek)
19 Zero Imputation Hybrid Sampling (SMOTETomek)
20 Zero Imputation Hybrid Sampling (SMOTETomek)
21 Zero Imputation Hybrid Sampling (SMOTETomek)
22 Zero Imputation Hybrid Sampling (SMOTETomek)
23 Zero Imputation Hybrid Sampling (SMOTETomek)
24 Zero Imputation Hybrid Sampling (SMOTETomek)
25 Zero Imputation Hybrid Sampling (SMOTETomek)
26 Zero Imputation Hybrid Sampling (SMOTETomek)
27 Zero Imputation Hybrid Sampling (SMOTETomek)
28 Zero Imputation Hybrid Sampling (SMOTETomek)
29 Zero Imputation Hybrid Sampling (SMOTETomek)
30 Zero Imputation Hybrid Sampling (SMOTETomek)
31 Zero Imputation Hybrid Sampling (SMOTETomek)
32 Zero Imputation Hybrid Sampling (SMOTETomek)
33 Zero Imputation Hybrid Sampling (SMOTETomek)
34 Zero Imputation Hybrid Sampling (SMOTETomek)
35 Zero Imputation Hybrid Sampling (SMOTETomek)
36 Zero Imputation Hybrid Sampling (SMOTETomek)
37 Zero Imputation Hybrid Sampling (SMOTETomek)
38 Zero Imputation Hybrid Sampling (SMOTETomek)
39 Zero Imputation Hybrid Sampling (SMOTETomek)
40 Zero Imputation Hybrid Sampling (SMOTETomek)
41 Zero Imputation Hybrid Sampling (SMOTETomek)
42 Zero Imputation Hybrid Sampling (SMOTETomek)
43 Zero Imputation Hybrid Sampling (SMOTETomek)
44 Zero Imputation Hybrid Sampling (SMOTETomek)
45 Zero Imputation Hybrid Sampling (SMOTETomek)
46 Zero Imputation Hybrid Sampling (SMOTETomek)
47 Zero Imputation Hybrid Sampling (SMOTETomek)
48 Zero Imputation Hybrid Sampling (SMOTETomek)
49 Zero Imputation Hybrid Sampling (SMOTETomek)
50 Zero Imputation Hybrid Sampling (SMOTETomek)
51 Zero Imputation Hybrid Sampling (SMOTETomek)
52 Zero Imputation Hybrid Sampling (SMOTETomek)
53 Zero Imputation Hybrid Sampling (SMOTETomek)
54 Zero Imputation Hybrid Sampling (SMOTETomek)
55 Zero Imputation Hybrid Sampling (SMOTETomek)
56 Zero Imputation Hybrid Sampling (SMOTETomek)
57 Zero Imputation Hybrid Sampling (SMOTETomek)
58 Zero Imputation Hybrid Sampling (SMOTETomek)
59 Zero Imputation Hybrid Sampling (SMOTETomek)
60 Zero Imputation Hybrid Sampling (SMOTETomek)
61 Zero Imputation Hybrid Sampling (SMOTETomek)
62 Zero Imputation Hybrid Sampling (SMOTETomek)
63 Zero Imputation Hybrid Sampling (SMOTETomek)
64 Zero Imputation Hybrid Sampling (SMOTETomek)
65 Zero Imputation Hybrid Sampling (SMOTETomek)
66 Zero Imputation Hybrid Sampling (SMOTETomek)
67 Zero Imputation Hybrid Sampling (SMOTETomek)
68 Zero Imputation Hybrid Sampling (SMOTETomek)
69 Zero Imputation Hybrid Sampling (SMOTETomek)
70 Zero Imputation Hybrid Sampling (SMOTETomek)
71 Zero Imputation Hybrid Sampling (SMOTETomek)
72 Zero Imputation Hybrid Sampling (SMOTETomek)
73 Zero Imputation Hybrid Sampling (SMOTETomek)
74 Zero Imputation Hybrid Sampling (SMOTETomek)
75 Zero Imputation Hybrid Sampling (SMOTETomek)
76 Zero Imputation Hybrid Sampling (SMOTETomek)
77 Zero Imputation Hybrid Sampling (SMOTETomek)
78 Zero Imputation Hybrid Sampling (SMOTETomek)
79 Zero Imputation Hybrid Sampling (SMOTETomek)
80 Zero Imputation Hybrid Sampling (SMOTETomek)
81 Zero Imputation Hybrid Sampling (SMOTETomek)
82 Zero Imputation Hybrid Sampling (SMOTETomek)
83 Zero Imputation Hybrid Sampling (SMOTETomek)
84 Zero Imputation Hybrid Sampling (SMOTETomek)
85 Zero Imputation Hybrid Sampling (SMOTETomek)
86 Zero Imputation Hybrid Sampling (SMOTETomek)
87 Zero Imputation Hybrid Sampling (SMOTETomek)
88 Zero Imputation Hybrid Sampling (SMOTETomek)
89 Zero Imputation Hybrid Sampling (SMOTETomek)
90 Zero Imputation Hybrid Sampling (SMOTETomek)
91 Zero Imputation Hybrid Sampling (SMOTETomek)
92 Zero Imputation Hybrid Sampling (SMOTETomek)
93 Zero Imputation Hybrid Sampling (SMOTETomek)
94 Zero Imputation Hybrid Sampling (SMOTETomek)
95 Zero Imputation Hybrid Sampling (SMOTETomek)
96 Zero Imputation Hybrid Sampling (SMOTETomek)
97 Zero Imputation Hybrid Sampling (SMOTETomek)
98 Zero Imputation Hybrid Sampling (SMOTETomek)
99 Zero Imputation Hybrid Sampling (SMOTETomek)
100 Zero Imputation Hybrid Sampling (SMOTETomek)
101 Zero Imputation Hybrid Sampling (SMOTETomek)
102 Zero Imputation Hybrid Sampling (SMOTETomek)
103 Zero Imputation Hybrid Sampling (SMOTETomek)
104 Zero Imputation Hybrid Sampling (SMOTETomek)
105 Zero Imputation Hybrid Sampling (SMOTETomek)
106 Zero Imputation Hybrid Sampling (SMOTETomek)
107 Zero Imputation Hybrid Sampling (SMOTETomek)
108 Zero Imputation Hybrid Sampling (SMOTETomek)
109 Zero Imputation Hybrid Sampling (SMOTETomek)
110 Zero Imputation Hybrid Sampling (SMOTETomek)
111 Zero Imputation Hybrid Sampling (SMOTETomek)
112 Zero Imputation Hybrid Sampling (SMOTETomek)
113 Zero Imputation Hybrid Sampling (SMOTETomek)
114 Zero Imputation Hybrid Sampling (SMOTETomek)
115 Zero Imputation Hybrid Sampling (SMOTETomek)
116 Zero Imputation Hybrid Sampling (SMOTETomek)
117 Zero Imputation Hybrid Sampling (SMOTETomek)
118 Zero Imputation Hybrid Sampling (SMOTETomek)
119 Zero Imputation Hybrid Sampling (SMOTETomek)
120 Zero Imputation Hybrid Sampling (SMOTETomek)
121 Zero Imputation Hybrid Sampling (SMOTETomek)
122 Zero Imputation Hybrid Sampling (SMOTETomek)
123 Zero Imputation Hybrid Sampling (SMOTETomek)
124 Zero Imputation Hybrid Sampling (SMOTETomek)
125 Zero Imputation Hybrid Sampling (SMOTETomek)
126 Zero Imputation Hybrid Sampling (SMOTETomek)
127 Zero Imputation Hybrid Sampling (SMOTETomek)
128 Zero Imputation Hybrid Sampling (SMOTETomek)
129 Zero Imputation Hybrid Sampling (SMOTETomek)
130 Zero Imputation Hybrid Sampling (SMOTETomek)
131 Zero Imputation Hybrid Sampling (SMOTETomek)
132 Zero Imputation Hybrid Sampling (SMOTETomek)
133 Zero Imputation Hybrid Sampling (SMOTETomek)
134 Zero Imputation Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Gradient Boosting Machines (GBM)_Hybrid Sampli...
1 Gradient Boosting Machines (GBM)_Hybrid Sampli...
2 Gradient Boosting Machines (GBM)_Hybrid Sampli...
3 Gradient Boosting Machines (GBM)_Hybrid Sampli...
4 Gradient Boosting Machines (GBM)_Hybrid Sampli...
5 Gradient Boosting Machines (GBM)_Hybrid Sampli...
6 Gradient Boosting Machines (GBM)_Hybrid Sampli...
7 Gradient Boosting Machines (GBM)_Hybrid Sampli...
8 Gradient Boosting Machines (GBM)_Hybrid Sampli...
9 Gradient Boosting Machines (GBM)_Hybrid Sampli...
10 Gradient Boosting Machines (GBM)_Hybrid Sampli...
11 Gradient Boosting Machines (GBM)_Hybrid Sampli...
12 Gradient Boosting Machines (GBM)_Hybrid Sampli...
13 Gradient Boosting Machines (GBM)_Hybrid Sampli...
14 Gradient Boosting Machines (GBM)_Hybrid Sampli...
15 Gradient Boosting Machines (GBM)_Hybrid Sampli...
16 Gradient Boosting Machines (GBM)_Hybrid Sampli...
17 Gradient Boosting Machines (GBM)_Hybrid Sampli...
18 Gradient Boosting Machines (GBM)_Hybrid Sampli...
19 Gradient Boosting Machines (GBM)_Hybrid Sampli...
20 Gradient Boosting Machines (GBM)_Hybrid Sampli...
21 Gradient Boosting Machines (GBM)_Hybrid Sampli...
22 Gradient Boosting Machines (GBM)_Hybrid Sampli...
23 Gradient Boosting Machines (GBM)_Hybrid Sampli...
24 Gradient Boosting Machines (GBM)_Hybrid Sampli...
25 Gradient Boosting Machines (GBM)_Hybrid Sampli...
26 Gradient Boosting Machines (GBM)_Hybrid Sampli...
27 Gradient Boosting Machines (GBM)_Hybrid Sampli...
28 Gradient Boosting Machines (GBM)_Hybrid Sampli...
29 Gradient Boosting Machines (GBM)_Hybrid Sampli...
30 Gradient Boosting Machines (GBM)_Hybrid Sampli...
31 Gradient Boosting Machines (GBM)_Hybrid Sampli...
32 Gradient Boosting Machines (GBM)_Hybrid Sampli...
33 Gradient Boosting Machines (GBM)_Hybrid Sampli...
34 Gradient Boosting Machines (GBM)_Hybrid Sampli...
35 Gradient Boosting Machines (GBM)_Hybrid Sampli...
36 Gradient Boosting Machines (GBM)_Hybrid Sampli...
37 Gradient Boosting Machines (GBM)_Hybrid Sampli...
38 Gradient Boosting Machines (GBM)_Hybrid Sampli...
39 Gradient Boosting Machines (GBM)_Hybrid Sampli...
40 Gradient Boosting Machines (GBM)_Hybrid Sampli...
41 Gradient Boosting Machines (GBM)_Hybrid Sampli...
42 Gradient Boosting Machines (GBM)_Hybrid Sampli...
43 Gradient Boosting Machines (GBM)_Hybrid Sampli...
44 Gradient Boosting Machines (GBM)_Hybrid Sampli...
45 Gradient Boosting Machines (GBM)_Hybrid Sampli...
46 Gradient Boosting Machines (GBM)_Hybrid Sampli...
47 Gradient Boosting Machines (GBM)_Hybrid Sampli...
48 Gradient Boosting Machines (GBM)_Hybrid Sampli...
49 Gradient Boosting Machines (GBM)_Hybrid Sampli...
50 Gradient Boosting Machines (GBM)_Hybrid Sampli...
51 Gradient Boosting Machines (GBM)_Hybrid Sampli...
52 Gradient Boosting Machines (GBM)_Hybrid Sampli...
53 Gradient Boosting Machines (GBM)_Hybrid Sampli...
54 Gradient Boosting Machines (GBM)_Hybrid Sampli...
55 Gradient Boosting Machines (GBM)_Hybrid Sampli...
56 Gradient Boosting Machines (GBM)_Hybrid Sampli...
57 Gradient Boosting Machines (GBM)_Hybrid Sampli...
58 Gradient Boosting Machines (GBM)_Hybrid Sampli...
59 Gradient Boosting Machines (GBM)_Hybrid Sampli...
60 Gradient Boosting Machines (GBM)_Hybrid Sampli...
61 Gradient Boosting Machines (GBM)_Hybrid Sampli...
62 Gradient Boosting Machines (GBM)_Hybrid Sampli...
63 Gradient Boosting Machines (GBM)_Hybrid Sampli...
64 Gradient Boosting Machines (GBM)_Hybrid Sampli...
65 Gradient Boosting Machines (GBM)_Hybrid Sampli...
66 Gradient Boosting Machines (GBM)_Hybrid Sampli...
67 Gradient Boosting Machines (GBM)_Hybrid Sampli...
68 Gradient Boosting Machines (GBM)_Hybrid Sampli...
69 Gradient Boosting Machines (GBM)_Hybrid Sampli...
70 Gradient Boosting Machines (GBM)_Hybrid Sampli...
71 Gradient Boosting Machines (GBM)_Hybrid Sampli...
72 Gradient Boosting Machines (GBM)_Hybrid Sampli...
73 Gradient Boosting Machines (GBM)_Hybrid Sampli...
74 Gradient Boosting Machines (GBM)_Hybrid Sampli...
75 Gradient Boosting Machines (GBM)_Hybrid Sampli...
76 Gradient Boosting Machines (GBM)_Hybrid Sampli...
77 Gradient Boosting Machines (GBM)_Hybrid Sampli...
78 Gradient Boosting Machines (GBM)_Hybrid Sampli...
79 Gradient Boosting Machines (GBM)_Hybrid Sampli...
80 Gradient Boosting Machines (GBM)_Hybrid Sampli...
81 Gradient Boosting Machines (GBM)_Hybrid Sampli...
82 Gradient Boosting Machines (GBM)_Hybrid Sampli...
83 Gradient Boosting Machines (GBM)_Hybrid Sampli...
84 Gradient Boosting Machines (GBM)_Hybrid Sampli...
85 Gradient Boosting Machines (GBM)_Hybrid Sampli...
86 Gradient Boosting Machines (GBM)_Hybrid Sampli...
87 Gradient Boosting Machines (GBM)_Hybrid Sampli...
88 Gradient Boosting Machines (GBM)_Hybrid Sampli...
89 Gradient Boosting Machines (GBM)_Hybrid Sampli...
90 Gradient Boosting Machines (GBM)_Hybrid Sampli...
91 Gradient Boosting Machines (GBM)_Hybrid Sampli...
92 Gradient Boosting Machines (GBM)_Hybrid Sampli...
93 Gradient Boosting Machines (GBM)_Hybrid Sampli...
94 Gradient Boosting Machines (GBM)_Hybrid Sampli...
95 Gradient Boosting Machines (GBM)_Hybrid Sampli...
96 Gradient Boosting Machines (GBM)_Hybrid Sampli...
97 Gradient Boosting Machines (GBM)_Hybrid Sampli...
98 Gradient Boosting Machines (GBM)_Hybrid Sampli...
99 Gradient Boosting Machines (GBM)_Hybrid Sampli...
100 Gradient Boosting Machines (GBM)_Hybrid Sampli...
101 Gradient Boosting Machines (GBM)_Hybrid Sampli...
102 Gradient Boosting Machines (GBM)_Hybrid Sampli...
103 Gradient Boosting Machines (GBM)_Hybrid Sampli...
104 Gradient Boosting Machines (GBM)_Hybrid Sampli...
105 Gradient Boosting Machines (GBM)_Hybrid Sampli...
106 Gradient Boosting Machines (GBM)_Hybrid Sampli...
107 Gradient Boosting Machines (GBM)_Hybrid Sampli...
108 Gradient Boosting Machines (GBM)_Hybrid Sampli...
109 Gradient Boosting Machines (GBM)_Hybrid Sampli...
110 Gradient Boosting Machines (GBM)_Hybrid Sampli...
111 Gradient Boosting Machines (GBM)_Hybrid Sampli...
112 Gradient Boosting Machines (GBM)_Hybrid Sampli...
113 Gradient Boosting Machines (GBM)_Hybrid Sampli...
114 Gradient Boosting Machines (GBM)_Hybrid Sampli...
115 Gradient Boosting Machines (GBM)_Hybrid Sampli...
116 Gradient Boosting Machines (GBM)_Hybrid Sampli...
117 Gradient Boosting Machines (GBM)_Hybrid Sampli...
118 Gradient Boosting Machines (GBM)_Hybrid Sampli...
119 Gradient Boosting Machines (GBM)_Hybrid Sampli...
120 Gradient Boosting Machines (GBM)_Hybrid Sampli...
121 Gradient Boosting Machines (GBM)_Hybrid Sampli...
122 Gradient Boosting Machines (GBM)_Hybrid Sampli...
123 Gradient Boosting Machines (GBM)_Hybrid Sampli...
124 Gradient Boosting Machines (GBM)_Hybrid Sampli...
125 Gradient Boosting Machines (GBM)_Hybrid Sampli...
126 Gradient Boosting Machines (GBM)_Hybrid Sampli...
127 Gradient Boosting Machines (GBM)_Hybrid Sampli...
128 Gradient Boosting Machines (GBM)_Hybrid Sampli...
129 Gradient Boosting Machines (GBM)_Hybrid Sampli...
130 Gradient Boosting Machines (GBM)_Hybrid Sampli...
131 Gradient Boosting Machines (GBM)_Hybrid Sampli...
132 Gradient Boosting Machines (GBM)_Hybrid Sampli...
133 Gradient Boosting Machines (GBM)_Hybrid Sampli...
134 Gradient Boosting Machines (GBM)_Hybrid Sampli... ,
Algorithm Metrics Hyperparameters \
0 Gradient Boosting Machines (GBM) 0.840664 NaN
1 Gradient Boosting Machines (GBM) 0.119835 NaN
2 Gradient Boosting Machines (GBM) 0.244726 NaN
3 Gradient Boosting Machines (GBM) 0.160888 NaN
4 Gradient Boosting Machines (GBM) 0.655089 NaN
5 Gradient Boosting Machines (GBM) 0.241046 NaN
6 Gradient Boosting Machines (GBM) 0.310178 NaN
7 Gradient Boosting Machines (GBM) NaN 0.0
8 Gradient Boosting Machines (GBM) NaN friedman_mse
9 Gradient Boosting Machines (GBM) NaN None
10 Gradient Boosting Machines (GBM) NaN 0.1
11 Gradient Boosting Machines (GBM) NaN log_loss
12 Gradient Boosting Machines (GBM) NaN 3
13 Gradient Boosting Machines (GBM) NaN None
14 Gradient Boosting Machines (GBM) NaN None
15 Gradient Boosting Machines (GBM) NaN 0.0
16 Gradient Boosting Machines (GBM) NaN 1
17 Gradient Boosting Machines (GBM) NaN 2
18 Gradient Boosting Machines (GBM) NaN 0.0
19 Gradient Boosting Machines (GBM) NaN 100
20 Gradient Boosting Machines (GBM) NaN None
21 Gradient Boosting Machines (GBM) NaN None
22 Gradient Boosting Machines (GBM) NaN 1.0
23 Gradient Boosting Machines (GBM) NaN 0.0001
24 Gradient Boosting Machines (GBM) NaN 0.1
25 Gradient Boosting Machines (GBM) NaN 0
26 Gradient Boosting Machines (GBM) NaN False
27 Gradient Boosting Machines (GBM) 0.846721 NaN
28 Gradient Boosting Machines (GBM) 0.108614 NaN
29 Gradient Boosting Machines (GBM) 0.353659 NaN
30 Gradient Boosting Machines (GBM) 0.166189 NaN
31 Gradient Boosting Machines (GBM) 0.670539 NaN
32 Gradient Boosting Machines (GBM) 0.271314 NaN
33 Gradient Boosting Machines (GBM) 0.341077 NaN
34 Gradient Boosting Machines (GBM) NaN 0.0
35 Gradient Boosting Machines (GBM) NaN friedman_mse
36 Gradient Boosting Machines (GBM) NaN None
37 Gradient Boosting Machines (GBM) NaN 0.1
38 Gradient Boosting Machines (GBM) NaN log_loss
39 Gradient Boosting Machines (GBM) NaN 3
40 Gradient Boosting Machines (GBM) NaN None
41 Gradient Boosting Machines (GBM) NaN None
42 Gradient Boosting Machines (GBM) NaN 0.0
43 Gradient Boosting Machines (GBM) NaN 1
44 Gradient Boosting Machines (GBM) NaN 2
45 Gradient Boosting Machines (GBM) NaN 0.0
46 Gradient Boosting Machines (GBM) NaN 100
47 Gradient Boosting Machines (GBM) NaN None
48 Gradient Boosting Machines (GBM) NaN None
49 Gradient Boosting Machines (GBM) NaN 1.0
50 Gradient Boosting Machines (GBM) NaN 0.0001
51 Gradient Boosting Machines (GBM) NaN 0.1
52 Gradient Boosting Machines (GBM) NaN 0
53 Gradient Boosting Machines (GBM) NaN False
54 Gradient Boosting Machines (GBM) 0.845931 NaN
55 Gradient Boosting Machines (GBM) 0.123106 NaN
56 Gradient Boosting Machines (GBM) 0.347594 NaN
57 Gradient Boosting Machines (GBM) 0.181818 NaN
58 Gradient Boosting Machines (GBM) 0.673389 NaN
59 Gradient Boosting Machines (GBM) 0.262072 NaN
60 Gradient Boosting Machines (GBM) 0.346779 NaN
61 Gradient Boosting Machines (GBM) NaN 0.0
62 Gradient Boosting Machines (GBM) NaN friedman_mse
63 Gradient Boosting Machines (GBM) NaN None
64 Gradient Boosting Machines (GBM) NaN 0.1
65 Gradient Boosting Machines (GBM) NaN log_loss
66 Gradient Boosting Machines (GBM) NaN 3
67 Gradient Boosting Machines (GBM) NaN None
68 Gradient Boosting Machines (GBM) NaN None
69 Gradient Boosting Machines (GBM) NaN 0.0
70 Gradient Boosting Machines (GBM) NaN 1
71 Gradient Boosting Machines (GBM) NaN 2
72 Gradient Boosting Machines (GBM) NaN 0.0
73 Gradient Boosting Machines (GBM) NaN 100
74 Gradient Boosting Machines (GBM) NaN None
75 Gradient Boosting Machines (GBM) NaN None
76 Gradient Boosting Machines (GBM) NaN 1.0
77 Gradient Boosting Machines (GBM) NaN 0.0001
78 Gradient Boosting Machines (GBM) NaN 0.1
79 Gradient Boosting Machines (GBM) NaN 0
80 Gradient Boosting Machines (GBM) NaN False
81 Gradient Boosting Machines (GBM) 0.842466 NaN
82 Gradient Boosting Machines (GBM) 0.119318 NaN
83 Gradient Boosting Machines (GBM) 0.321429 NaN
84 Gradient Boosting Machines (GBM) 0.174033 NaN
85 Gradient Boosting Machines (GBM) 0.675806 NaN
86 Gradient Boosting Machines (GBM) 0.304104 NaN
87 Gradient Boosting Machines (GBM) 0.351613 NaN
88 Gradient Boosting Machines (GBM) NaN 0.0
89 Gradient Boosting Machines (GBM) NaN friedman_mse
90 Gradient Boosting Machines (GBM) NaN None
91 Gradient Boosting Machines (GBM) NaN 0.1
92 Gradient Boosting Machines (GBM) NaN log_loss
93 Gradient Boosting Machines (GBM) NaN 3
94 Gradient Boosting Machines (GBM) NaN None
95 Gradient Boosting Machines (GBM) NaN None
96 Gradient Boosting Machines (GBM) NaN 0.0
97 Gradient Boosting Machines (GBM) NaN 1
98 Gradient Boosting Machines (GBM) NaN 2
99 Gradient Boosting Machines (GBM) NaN 0.0
100 Gradient Boosting Machines (GBM) NaN 100
101 Gradient Boosting Machines (GBM) NaN None
102 Gradient Boosting Machines (GBM) NaN None
103 Gradient Boosting Machines (GBM) NaN 1.0
104 Gradient Boosting Machines (GBM) NaN 0.0001
105 Gradient Boosting Machines (GBM) NaN 0.1
106 Gradient Boosting Machines (GBM) NaN 0
107 Gradient Boosting Machines (GBM) NaN False
108 Gradient Boosting Machines (GBM) 0.843783 NaN
109 Gradient Boosting Machines (GBM) 0.126733 NaN
110 Gradient Boosting Machines (GBM) 0.296296 NaN
111 Gradient Boosting Machines (GBM) 0.177531 NaN
112 Gradient Boosting Machines (GBM) 0.684996 NaN
113 Gradient Boosting Machines (GBM) 0.306145 NaN
114 Gradient Boosting Machines (GBM) 0.369991 NaN
115 Gradient Boosting Machines (GBM) NaN 0.0
116 Gradient Boosting Machines (GBM) NaN friedman_mse
117 Gradient Boosting Machines (GBM) NaN None
118 Gradient Boosting Machines (GBM) NaN 0.1
119 Gradient Boosting Machines (GBM) NaN log_loss
120 Gradient Boosting Machines (GBM) NaN 3
121 Gradient Boosting Machines (GBM) NaN None
122 Gradient Boosting Machines (GBM) NaN None
123 Gradient Boosting Machines (GBM) NaN 0.0
124 Gradient Boosting Machines (GBM) NaN 1
125 Gradient Boosting Machines (GBM) NaN 2
126 Gradient Boosting Machines (GBM) NaN 0.0
127 Gradient Boosting Machines (GBM) NaN 100
128 Gradient Boosting Machines (GBM) NaN None
129 Gradient Boosting Machines (GBM) NaN None
130 Gradient Boosting Machines (GBM) NaN 1.0
131 Gradient Boosting Machines (GBM) NaN 0.0001
132 Gradient Boosting Machines (GBM) NaN 0.1
133 Gradient Boosting Machines (GBM) NaN 0
134 Gradient Boosting Machines (GBM) NaN False
Imputation Technique Imbalance Class Technique \
0 Mean Imputation Hybrid Sampling (SMOTETomek)
1 Mean Imputation Hybrid Sampling (SMOTETomek)
2 Mean Imputation Hybrid Sampling (SMOTETomek)
3 Mean Imputation Hybrid Sampling (SMOTETomek)
4 Mean Imputation Hybrid Sampling (SMOTETomek)
5 Mean Imputation Hybrid Sampling (SMOTETomek)
6 Mean Imputation Hybrid Sampling (SMOTETomek)
7 Mean Imputation Hybrid Sampling (SMOTETomek)
8 Mean Imputation Hybrid Sampling (SMOTETomek)
9 Mean Imputation Hybrid Sampling (SMOTETomek)
10 Mean Imputation Hybrid Sampling (SMOTETomek)
11 Mean Imputation Hybrid Sampling (SMOTETomek)
12 Mean Imputation Hybrid Sampling (SMOTETomek)
13 Mean Imputation Hybrid Sampling (SMOTETomek)
14 Mean Imputation Hybrid Sampling (SMOTETomek)
15 Mean Imputation Hybrid Sampling (SMOTETomek)
16 Mean Imputation Hybrid Sampling (SMOTETomek)
17 Mean Imputation Hybrid Sampling (SMOTETomek)
18 Mean Imputation Hybrid Sampling (SMOTETomek)
19 Mean Imputation Hybrid Sampling (SMOTETomek)
20 Mean Imputation Hybrid Sampling (SMOTETomek)
21 Mean Imputation Hybrid Sampling (SMOTETomek)
22 Mean Imputation Hybrid Sampling (SMOTETomek)
23 Mean Imputation Hybrid Sampling (SMOTETomek)
24 Mean Imputation Hybrid Sampling (SMOTETomek)
25 Mean Imputation Hybrid Sampling (SMOTETomek)
26 Mean Imputation Hybrid Sampling (SMOTETomek)
27 Mean Imputation Hybrid Sampling (SMOTETomek)
28 Mean Imputation Hybrid Sampling (SMOTETomek)
29 Mean Imputation Hybrid Sampling (SMOTETomek)
30 Mean Imputation Hybrid Sampling (SMOTETomek)
31 Mean Imputation Hybrid Sampling (SMOTETomek)
32 Mean Imputation Hybrid Sampling (SMOTETomek)
33 Mean Imputation Hybrid Sampling (SMOTETomek)
34 Mean Imputation Hybrid Sampling (SMOTETomek)
35 Mean Imputation Hybrid Sampling (SMOTETomek)
36 Mean Imputation Hybrid Sampling (SMOTETomek)
37 Mean Imputation Hybrid Sampling (SMOTETomek)
38 Mean Imputation Hybrid Sampling (SMOTETomek)
39 Mean Imputation Hybrid Sampling (SMOTETomek)
40 Mean Imputation Hybrid Sampling (SMOTETomek)
41 Mean Imputation Hybrid Sampling (SMOTETomek)
42 Mean Imputation Hybrid Sampling (SMOTETomek)
43 Mean Imputation Hybrid Sampling (SMOTETomek)
44 Mean Imputation Hybrid Sampling (SMOTETomek)
45 Mean Imputation Hybrid Sampling (SMOTETomek)
46 Mean Imputation Hybrid Sampling (SMOTETomek)
47 Mean Imputation Hybrid Sampling (SMOTETomek)
48 Mean Imputation Hybrid Sampling (SMOTETomek)
49 Mean Imputation Hybrid Sampling (SMOTETomek)
50 Mean Imputation Hybrid Sampling (SMOTETomek)
51 Mean Imputation Hybrid Sampling (SMOTETomek)
52 Mean Imputation Hybrid Sampling (SMOTETomek)
53 Mean Imputation Hybrid Sampling (SMOTETomek)
54 Mean Imputation Hybrid Sampling (SMOTETomek)
55 Mean Imputation Hybrid Sampling (SMOTETomek)
56 Mean Imputation Hybrid Sampling (SMOTETomek)
57 Mean Imputation Hybrid Sampling (SMOTETomek)
58 Mean Imputation Hybrid Sampling (SMOTETomek)
59 Mean Imputation Hybrid Sampling (SMOTETomek)
60 Mean Imputation Hybrid Sampling (SMOTETomek)
61 Mean Imputation Hybrid Sampling (SMOTETomek)
62 Mean Imputation Hybrid Sampling (SMOTETomek)
63 Mean Imputation Hybrid Sampling (SMOTETomek)
64 Mean Imputation Hybrid Sampling (SMOTETomek)
65 Mean Imputation Hybrid Sampling (SMOTETomek)
66 Mean Imputation Hybrid Sampling (SMOTETomek)
67 Mean Imputation Hybrid Sampling (SMOTETomek)
68 Mean Imputation Hybrid Sampling (SMOTETomek)
69 Mean Imputation Hybrid Sampling (SMOTETomek)
70 Mean Imputation Hybrid Sampling (SMOTETomek)
71 Mean Imputation Hybrid Sampling (SMOTETomek)
72 Mean Imputation Hybrid Sampling (SMOTETomek)
73 Mean Imputation Hybrid Sampling (SMOTETomek)
74 Mean Imputation Hybrid Sampling (SMOTETomek)
75 Mean Imputation Hybrid Sampling (SMOTETomek)
76 Mean Imputation Hybrid Sampling (SMOTETomek)
77 Mean Imputation Hybrid Sampling (SMOTETomek)
78 Mean Imputation Hybrid Sampling (SMOTETomek)
79 Mean Imputation Hybrid Sampling (SMOTETomek)
80 Mean Imputation Hybrid Sampling (SMOTETomek)
81 Mean Imputation Hybrid Sampling (SMOTETomek)
82 Mean Imputation Hybrid Sampling (SMOTETomek)
83 Mean Imputation Hybrid Sampling (SMOTETomek)
84 Mean Imputation Hybrid Sampling (SMOTETomek)
85 Mean Imputation Hybrid Sampling (SMOTETomek)
86 Mean Imputation Hybrid Sampling (SMOTETomek)
87 Mean Imputation Hybrid Sampling (SMOTETomek)
88 Mean Imputation Hybrid Sampling (SMOTETomek)
89 Mean Imputation Hybrid Sampling (SMOTETomek)
90 Mean Imputation Hybrid Sampling (SMOTETomek)
91 Mean Imputation Hybrid Sampling (SMOTETomek)
92 Mean Imputation Hybrid Sampling (SMOTETomek)
93 Mean Imputation Hybrid Sampling (SMOTETomek)
94 Mean Imputation Hybrid Sampling (SMOTETomek)
95 Mean Imputation Hybrid Sampling (SMOTETomek)
96 Mean Imputation Hybrid Sampling (SMOTETomek)
97 Mean Imputation Hybrid Sampling (SMOTETomek)
98 Mean Imputation Hybrid Sampling (SMOTETomek)
99 Mean Imputation Hybrid Sampling (SMOTETomek)
100 Mean Imputation Hybrid Sampling (SMOTETomek)
101 Mean Imputation Hybrid Sampling (SMOTETomek)
102 Mean Imputation Hybrid Sampling (SMOTETomek)
103 Mean Imputation Hybrid Sampling (SMOTETomek)
104 Mean Imputation Hybrid Sampling (SMOTETomek)
105 Mean Imputation Hybrid Sampling (SMOTETomek)
106 Mean Imputation Hybrid Sampling (SMOTETomek)
107 Mean Imputation Hybrid Sampling (SMOTETomek)
108 Mean Imputation Hybrid Sampling (SMOTETomek)
109 Mean Imputation Hybrid Sampling (SMOTETomek)
110 Mean Imputation Hybrid Sampling (SMOTETomek)
111 Mean Imputation Hybrid Sampling (SMOTETomek)
112 Mean Imputation Hybrid Sampling (SMOTETomek)
113 Mean Imputation Hybrid Sampling (SMOTETomek)
114 Mean Imputation Hybrid Sampling (SMOTETomek)
115 Mean Imputation Hybrid Sampling (SMOTETomek)
116 Mean Imputation Hybrid Sampling (SMOTETomek)
117 Mean Imputation Hybrid Sampling (SMOTETomek)
118 Mean Imputation Hybrid Sampling (SMOTETomek)
119 Mean Imputation Hybrid Sampling (SMOTETomek)
120 Mean Imputation Hybrid Sampling (SMOTETomek)
121 Mean Imputation Hybrid Sampling (SMOTETomek)
122 Mean Imputation Hybrid Sampling (SMOTETomek)
123 Mean Imputation Hybrid Sampling (SMOTETomek)
124 Mean Imputation Hybrid Sampling (SMOTETomek)
125 Mean Imputation Hybrid Sampling (SMOTETomek)
126 Mean Imputation Hybrid Sampling (SMOTETomek)
127 Mean Imputation Hybrid Sampling (SMOTETomek)
128 Mean Imputation Hybrid Sampling (SMOTETomek)
129 Mean Imputation Hybrid Sampling (SMOTETomek)
130 Mean Imputation Hybrid Sampling (SMOTETomek)
131 Mean Imputation Hybrid Sampling (SMOTETomek)
132 Mean Imputation Hybrid Sampling (SMOTETomek)
133 Mean Imputation Hybrid Sampling (SMOTETomek)
134 Mean Imputation Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Gradient Boosting Machines (GBM)_Hybrid Sampli...
1 Gradient Boosting Machines (GBM)_Hybrid Sampli...
2 Gradient Boosting Machines (GBM)_Hybrid Sampli...
3 Gradient Boosting Machines (GBM)_Hybrid Sampli...
4 Gradient Boosting Machines (GBM)_Hybrid Sampli...
5 Gradient Boosting Machines (GBM)_Hybrid Sampli...
6 Gradient Boosting Machines (GBM)_Hybrid Sampli...
7 Gradient Boosting Machines (GBM)_Hybrid Sampli...
8 Gradient Boosting Machines (GBM)_Hybrid Sampli...
9 Gradient Boosting Machines (GBM)_Hybrid Sampli...
10 Gradient Boosting Machines (GBM)_Hybrid Sampli...
11 Gradient Boosting Machines (GBM)_Hybrid Sampli...
12 Gradient Boosting Machines (GBM)_Hybrid Sampli...
13 Gradient Boosting Machines (GBM)_Hybrid Sampli...
14 Gradient Boosting Machines (GBM)_Hybrid Sampli...
15 Gradient Boosting Machines (GBM)_Hybrid Sampli...
16 Gradient Boosting Machines (GBM)_Hybrid Sampli...
17 Gradient Boosting Machines (GBM)_Hybrid Sampli...
18 Gradient Boosting Machines (GBM)_Hybrid Sampli...
19 Gradient Boosting Machines (GBM)_Hybrid Sampli...
20 Gradient Boosting Machines (GBM)_Hybrid Sampli...
21 Gradient Boosting Machines (GBM)_Hybrid Sampli...
22 Gradient Boosting Machines (GBM)_Hybrid Sampli...
23 Gradient Boosting Machines (GBM)_Hybrid Sampli...
24 Gradient Boosting Machines (GBM)_Hybrid Sampli...
25 Gradient Boosting Machines (GBM)_Hybrid Sampli...
26 Gradient Boosting Machines (GBM)_Hybrid Sampli...
27 Gradient Boosting Machines (GBM)_Hybrid Sampli...
28 Gradient Boosting Machines (GBM)_Hybrid Sampli...
29 Gradient Boosting Machines (GBM)_Hybrid Sampli...
30 Gradient Boosting Machines (GBM)_Hybrid Sampli...
31 Gradient Boosting Machines (GBM)_Hybrid Sampli...
32 Gradient Boosting Machines (GBM)_Hybrid Sampli...
33 Gradient Boosting Machines (GBM)_Hybrid Sampli...
34 Gradient Boosting Machines (GBM)_Hybrid Sampli...
35 Gradient Boosting Machines (GBM)_Hybrid Sampli...
36 Gradient Boosting Machines (GBM)_Hybrid Sampli...
37 Gradient Boosting Machines (GBM)_Hybrid Sampli...
38 Gradient Boosting Machines (GBM)_Hybrid Sampli...
39 Gradient Boosting Machines (GBM)_Hybrid Sampli...
40 Gradient Boosting Machines (GBM)_Hybrid Sampli...
41 Gradient Boosting Machines (GBM)_Hybrid Sampli...
42 Gradient Boosting Machines (GBM)_Hybrid Sampli...
43 Gradient Boosting Machines (GBM)_Hybrid Sampli...
44 Gradient Boosting Machines (GBM)_Hybrid Sampli...
45 Gradient Boosting Machines (GBM)_Hybrid Sampli...
46 Gradient Boosting Machines (GBM)_Hybrid Sampli...
47 Gradient Boosting Machines (GBM)_Hybrid Sampli...
48 Gradient Boosting Machines (GBM)_Hybrid Sampli...
49 Gradient Boosting Machines (GBM)_Hybrid Sampli...
50 Gradient Boosting Machines (GBM)_Hybrid Sampli...
51 Gradient Boosting Machines (GBM)_Hybrid Sampli...
52 Gradient Boosting Machines (GBM)_Hybrid Sampli...
53 Gradient Boosting Machines (GBM)_Hybrid Sampli...
54 Gradient Boosting Machines (GBM)_Hybrid Sampli...
55 Gradient Boosting Machines (GBM)_Hybrid Sampli...
56 Gradient Boosting Machines (GBM)_Hybrid Sampli...
57 Gradient Boosting Machines (GBM)_Hybrid Sampli...
58 Gradient Boosting Machines (GBM)_Hybrid Sampli...
59 Gradient Boosting Machines (GBM)_Hybrid Sampli...
60 Gradient Boosting Machines (GBM)_Hybrid Sampli...
61 Gradient Boosting Machines (GBM)_Hybrid Sampli...
62 Gradient Boosting Machines (GBM)_Hybrid Sampli...
63 Gradient Boosting Machines (GBM)_Hybrid Sampli...
64 Gradient Boosting Machines (GBM)_Hybrid Sampli...
65 Gradient Boosting Machines (GBM)_Hybrid Sampli...
66 Gradient Boosting Machines (GBM)_Hybrid Sampli...
67 Gradient Boosting Machines (GBM)_Hybrid Sampli...
68 Gradient Boosting Machines (GBM)_Hybrid Sampli...
69 Gradient Boosting Machines (GBM)_Hybrid Sampli...
70 Gradient Boosting Machines (GBM)_Hybrid Sampli...
71 Gradient Boosting Machines (GBM)_Hybrid Sampli...
72 Gradient Boosting Machines (GBM)_Hybrid Sampli...
73 Gradient Boosting Machines (GBM)_Hybrid Sampli...
74 Gradient Boosting Machines (GBM)_Hybrid Sampli...
75 Gradient Boosting Machines (GBM)_Hybrid Sampli...
76 Gradient Boosting Machines (GBM)_Hybrid Sampli...
77 Gradient Boosting Machines (GBM)_Hybrid Sampli...
78 Gradient Boosting Machines (GBM)_Hybrid Sampli...
79 Gradient Boosting Machines (GBM)_Hybrid Sampli...
80 Gradient Boosting Machines (GBM)_Hybrid Sampli...
81 Gradient Boosting Machines (GBM)_Hybrid Sampli...
82 Gradient Boosting Machines (GBM)_Hybrid Sampli...
83 Gradient Boosting Machines (GBM)_Hybrid Sampli...
84 Gradient Boosting Machines (GBM)_Hybrid Sampli...
85 Gradient Boosting Machines (GBM)_Hybrid Sampli...
86 Gradient Boosting Machines (GBM)_Hybrid Sampli...
87 Gradient Boosting Machines (GBM)_Hybrid Sampli...
88 Gradient Boosting Machines (GBM)_Hybrid Sampli...
89 Gradient Boosting Machines (GBM)_Hybrid Sampli...
90 Gradient Boosting Machines (GBM)_Hybrid Sampli...
91 Gradient Boosting Machines (GBM)_Hybrid Sampli...
92 Gradient Boosting Machines (GBM)_Hybrid Sampli...
93 Gradient Boosting Machines (GBM)_Hybrid Sampli...
94 Gradient Boosting Machines (GBM)_Hybrid Sampli...
95 Gradient Boosting Machines (GBM)_Hybrid Sampli...
96 Gradient Boosting Machines (GBM)_Hybrid Sampli...
97 Gradient Boosting Machines (GBM)_Hybrid Sampli...
98 Gradient Boosting Machines (GBM)_Hybrid Sampli...
99 Gradient Boosting Machines (GBM)_Hybrid Sampli...
100 Gradient Boosting Machines (GBM)_Hybrid Sampli...
101 Gradient Boosting Machines (GBM)_Hybrid Sampli...
102 Gradient Boosting Machines (GBM)_Hybrid Sampli...
103 Gradient Boosting Machines (GBM)_Hybrid Sampli...
104 Gradient Boosting Machines (GBM)_Hybrid Sampli...
105 Gradient Boosting Machines (GBM)_Hybrid Sampli...
106 Gradient Boosting Machines (GBM)_Hybrid Sampli...
107 Gradient Boosting Machines (GBM)_Hybrid Sampli...
108 Gradient Boosting Machines (GBM)_Hybrid Sampli...
109 Gradient Boosting Machines (GBM)_Hybrid Sampli...
110 Gradient Boosting Machines (GBM)_Hybrid Sampli...
111 Gradient Boosting Machines (GBM)_Hybrid Sampli...
112 Gradient Boosting Machines (GBM)_Hybrid Sampli...
113 Gradient Boosting Machines (GBM)_Hybrid Sampli...
114 Gradient Boosting Machines (GBM)_Hybrid Sampli...
115 Gradient Boosting Machines (GBM)_Hybrid Sampli...
116 Gradient Boosting Machines (GBM)_Hybrid Sampli...
117 Gradient Boosting Machines (GBM)_Hybrid Sampli...
118 Gradient Boosting Machines (GBM)_Hybrid Sampli...
119 Gradient Boosting Machines (GBM)_Hybrid Sampli...
120 Gradient Boosting Machines (GBM)_Hybrid Sampli...
121 Gradient Boosting Machines (GBM)_Hybrid Sampli...
122 Gradient Boosting Machines (GBM)_Hybrid Sampli...
123 Gradient Boosting Machines (GBM)_Hybrid Sampli...
124 Gradient Boosting Machines (GBM)_Hybrid Sampli...
125 Gradient Boosting Machines (GBM)_Hybrid Sampli...
126 Gradient Boosting Machines (GBM)_Hybrid Sampli...
127 Gradient Boosting Machines (GBM)_Hybrid Sampli...
128 Gradient Boosting Machines (GBM)_Hybrid Sampli...
129 Gradient Boosting Machines (GBM)_Hybrid Sampli...
130 Gradient Boosting Machines (GBM)_Hybrid Sampli...
131 Gradient Boosting Machines (GBM)_Hybrid Sampli...
132 Gradient Boosting Machines (GBM)_Hybrid Sampli...
133 Gradient Boosting Machines (GBM)_Hybrid Sampli...
134 Gradient Boosting Machines (GBM)_Hybrid Sampli... ,
Algorithm Metrics Hyperparameters \
0 Gradient Boosting Machines (GBM) 0.836977 NaN
1 Gradient Boosting Machines (GBM) 0.106996 NaN
2 Gradient Boosting Machines (GBM) 0.219409 NaN
3 Gradient Boosting Machines (GBM) 0.143845 NaN
4 Gradient Boosting Machines (GBM) 0.634986 NaN
5 Gradient Boosting Machines (GBM) 0.202133 NaN
6 Gradient Boosting Machines (GBM) 0.269971 NaN
7 Gradient Boosting Machines (GBM) NaN 0.0
8 Gradient Boosting Machines (GBM) NaN friedman_mse
9 Gradient Boosting Machines (GBM) NaN None
10 Gradient Boosting Machines (GBM) NaN 0.1
11 Gradient Boosting Machines (GBM) NaN log_loss
12 Gradient Boosting Machines (GBM) NaN 3
13 Gradient Boosting Machines (GBM) NaN None
14 Gradient Boosting Machines (GBM) NaN None
15 Gradient Boosting Machines (GBM) NaN 0.0
16 Gradient Boosting Machines (GBM) NaN 1
17 Gradient Boosting Machines (GBM) NaN 2
18 Gradient Boosting Machines (GBM) NaN 0.0
19 Gradient Boosting Machines (GBM) NaN 100
20 Gradient Boosting Machines (GBM) NaN None
21 Gradient Boosting Machines (GBM) NaN None
22 Gradient Boosting Machines (GBM) NaN 1.0
23 Gradient Boosting Machines (GBM) NaN 0.0001
24 Gradient Boosting Machines (GBM) NaN 0.1
25 Gradient Boosting Machines (GBM) NaN 0
26 Gradient Boosting Machines (GBM) NaN False
27 Gradient Boosting Machines (GBM) 0.855412 NaN
28 Gradient Boosting Machines (GBM) 0.104723 NaN
29 Gradient Boosting Machines (GBM) 0.310976 NaN
30 Gradient Boosting Machines (GBM) 0.156682 NaN
31 Gradient Boosting Machines (GBM) 0.667502 NaN
32 Gradient Boosting Machines (GBM) 0.257262 NaN
33 Gradient Boosting Machines (GBM) 0.335003 NaN
34 Gradient Boosting Machines (GBM) NaN 0.0
35 Gradient Boosting Machines (GBM) NaN friedman_mse
36 Gradient Boosting Machines (GBM) NaN None
37 Gradient Boosting Machines (GBM) NaN 0.1
38 Gradient Boosting Machines (GBM) NaN log_loss
39 Gradient Boosting Machines (GBM) NaN 3
40 Gradient Boosting Machines (GBM) NaN None
41 Gradient Boosting Machines (GBM) NaN None
42 Gradient Boosting Machines (GBM) NaN 0.0
43 Gradient Boosting Machines (GBM) NaN 1
44 Gradient Boosting Machines (GBM) NaN 2
45 Gradient Boosting Machines (GBM) NaN 0.0
46 Gradient Boosting Machines (GBM) NaN 100
47 Gradient Boosting Machines (GBM) NaN None
48 Gradient Boosting Machines (GBM) NaN None
49 Gradient Boosting Machines (GBM) NaN 1.0
50 Gradient Boosting Machines (GBM) NaN 0.0001
51 Gradient Boosting Machines (GBM) NaN 0.1
52 Gradient Boosting Machines (GBM) NaN 0
53 Gradient Boosting Machines (GBM) NaN False
54 Gradient Boosting Machines (GBM) 0.849881 NaN
55 Gradient Boosting Machines (GBM) 0.116232 NaN
56 Gradient Boosting Machines (GBM) 0.310160 NaN
57 Gradient Boosting Machines (GBM) 0.169096 NaN
58 Gradient Boosting Machines (GBM) 0.673500 NaN
59 Gradient Boosting Machines (GBM) 0.260268 NaN
60 Gradient Boosting Machines (GBM) 0.347000 NaN
61 Gradient Boosting Machines (GBM) NaN 0.0
62 Gradient Boosting Machines (GBM) NaN friedman_mse
63 Gradient Boosting Machines (GBM) NaN None
64 Gradient Boosting Machines (GBM) NaN 0.1
65 Gradient Boosting Machines (GBM) NaN log_loss
66 Gradient Boosting Machines (GBM) NaN 3
67 Gradient Boosting Machines (GBM) NaN None
68 Gradient Boosting Machines (GBM) NaN None
69 Gradient Boosting Machines (GBM) NaN 0.0
70 Gradient Boosting Machines (GBM) NaN 1
71 Gradient Boosting Machines (GBM) NaN 2
72 Gradient Boosting Machines (GBM) NaN 0.0
73 Gradient Boosting Machines (GBM) NaN 100
74 Gradient Boosting Machines (GBM) NaN None
75 Gradient Boosting Machines (GBM) NaN None
76 Gradient Boosting Machines (GBM) NaN 1.0
77 Gradient Boosting Machines (GBM) NaN 0.0001
78 Gradient Boosting Machines (GBM) NaN 0.1
79 Gradient Boosting Machines (GBM) NaN 0
80 Gradient Boosting Machines (GBM) NaN False
81 Gradient Boosting Machines (GBM) 0.844310 NaN
82 Gradient Boosting Machines (GBM) 0.104208 NaN
83 Gradient Boosting Machines (GBM) 0.265306 NaN
84 Gradient Boosting Machines (GBM) 0.149640 NaN
85 Gradient Boosting Machines (GBM) 0.659612 NaN
86 Gradient Boosting Machines (GBM) 0.247545 NaN
87 Gradient Boosting Machines (GBM) 0.319225 NaN
88 Gradient Boosting Machines (GBM) NaN 0.0
89 Gradient Boosting Machines (GBM) NaN friedman_mse
90 Gradient Boosting Machines (GBM) NaN None
91 Gradient Boosting Machines (GBM) NaN 0.1
92 Gradient Boosting Machines (GBM) NaN log_loss
93 Gradient Boosting Machines (GBM) NaN 3
94 Gradient Boosting Machines (GBM) NaN None
95 Gradient Boosting Machines (GBM) NaN None
96 Gradient Boosting Machines (GBM) NaN 0.0
97 Gradient Boosting Machines (GBM) NaN 1
98 Gradient Boosting Machines (GBM) NaN 2
99 Gradient Boosting Machines (GBM) NaN 0.0
100 Gradient Boosting Machines (GBM) NaN 100
101 Gradient Boosting Machines (GBM) NaN None
102 Gradient Boosting Machines (GBM) NaN None
103 Gradient Boosting Machines (GBM) NaN 1.0
104 Gradient Boosting Machines (GBM) NaN 0.0001
105 Gradient Boosting Machines (GBM) NaN 0.1
106 Gradient Boosting Machines (GBM) NaN 0
107 Gradient Boosting Machines (GBM) NaN False
108 Gradient Boosting Machines (GBM) 0.850632 NaN
109 Gradient Boosting Machines (GBM) 0.136646 NaN
110 Gradient Boosting Machines (GBM) 0.305556 NaN
111 Gradient Boosting Machines (GBM) 0.188841 NaN
112 Gradient Boosting Machines (GBM) 0.675818 NaN
113 Gradient Boosting Machines (GBM) 0.283152 NaN
114 Gradient Boosting Machines (GBM) 0.351636 NaN
115 Gradient Boosting Machines (GBM) NaN 0.0
116 Gradient Boosting Machines (GBM) NaN friedman_mse
117 Gradient Boosting Machines (GBM) NaN None
118 Gradient Boosting Machines (GBM) NaN 0.1
119 Gradient Boosting Machines (GBM) NaN log_loss
120 Gradient Boosting Machines (GBM) NaN 3
121 Gradient Boosting Machines (GBM) NaN None
122 Gradient Boosting Machines (GBM) NaN None
123 Gradient Boosting Machines (GBM) NaN 0.0
124 Gradient Boosting Machines (GBM) NaN 1
125 Gradient Boosting Machines (GBM) NaN 2
126 Gradient Boosting Machines (GBM) NaN 0.0
127 Gradient Boosting Machines (GBM) NaN 100
128 Gradient Boosting Machines (GBM) NaN None
129 Gradient Boosting Machines (GBM) NaN None
130 Gradient Boosting Machines (GBM) NaN 1.0
131 Gradient Boosting Machines (GBM) NaN 0.0001
132 Gradient Boosting Machines (GBM) NaN 0.1
133 Gradient Boosting Machines (GBM) NaN 0
134 Gradient Boosting Machines (GBM) NaN False
Imputation Technique Imbalance Class Technique \
0 Median Imputation Hybrid Sampling (SMOTETomek)
1 Median Imputation Hybrid Sampling (SMOTETomek)
2 Median Imputation Hybrid Sampling (SMOTETomek)
3 Median Imputation Hybrid Sampling (SMOTETomek)
4 Median Imputation Hybrid Sampling (SMOTETomek)
5 Median Imputation Hybrid Sampling (SMOTETomek)
6 Median Imputation Hybrid Sampling (SMOTETomek)
7 Median Imputation Hybrid Sampling (SMOTETomek)
8 Median Imputation Hybrid Sampling (SMOTETomek)
9 Median Imputation Hybrid Sampling (SMOTETomek)
10 Median Imputation Hybrid Sampling (SMOTETomek)
11 Median Imputation Hybrid Sampling (SMOTETomek)
12 Median Imputation Hybrid Sampling (SMOTETomek)
13 Median Imputation Hybrid Sampling (SMOTETomek)
14 Median Imputation Hybrid Sampling (SMOTETomek)
15 Median Imputation Hybrid Sampling (SMOTETomek)
16 Median Imputation Hybrid Sampling (SMOTETomek)
17 Median Imputation Hybrid Sampling (SMOTETomek)
18 Median Imputation Hybrid Sampling (SMOTETomek)
19 Median Imputation Hybrid Sampling (SMOTETomek)
20 Median Imputation Hybrid Sampling (SMOTETomek)
21 Median Imputation Hybrid Sampling (SMOTETomek)
22 Median Imputation Hybrid Sampling (SMOTETomek)
23 Median Imputation Hybrid Sampling (SMOTETomek)
24 Median Imputation Hybrid Sampling (SMOTETomek)
25 Median Imputation Hybrid Sampling (SMOTETomek)
26 Median Imputation Hybrid Sampling (SMOTETomek)
27 Median Imputation Hybrid Sampling (SMOTETomek)
28 Median Imputation Hybrid Sampling (SMOTETomek)
29 Median Imputation Hybrid Sampling (SMOTETomek)
30 Median Imputation Hybrid Sampling (SMOTETomek)
31 Median Imputation Hybrid Sampling (SMOTETomek)
32 Median Imputation Hybrid Sampling (SMOTETomek)
33 Median Imputation Hybrid Sampling (SMOTETomek)
34 Median Imputation Hybrid Sampling (SMOTETomek)
35 Median Imputation Hybrid Sampling (SMOTETomek)
36 Median Imputation Hybrid Sampling (SMOTETomek)
37 Median Imputation Hybrid Sampling (SMOTETomek)
38 Median Imputation Hybrid Sampling (SMOTETomek)
39 Median Imputation Hybrid Sampling (SMOTETomek)
40 Median Imputation Hybrid Sampling (SMOTETomek)
41 Median Imputation Hybrid Sampling (SMOTETomek)
42 Median Imputation Hybrid Sampling (SMOTETomek)
43 Median Imputation Hybrid Sampling (SMOTETomek)
44 Median Imputation Hybrid Sampling (SMOTETomek)
45 Median Imputation Hybrid Sampling (SMOTETomek)
46 Median Imputation Hybrid Sampling (SMOTETomek)
47 Median Imputation Hybrid Sampling (SMOTETomek)
48 Median Imputation Hybrid Sampling (SMOTETomek)
49 Median Imputation Hybrid Sampling (SMOTETomek)
50 Median Imputation Hybrid Sampling (SMOTETomek)
51 Median Imputation Hybrid Sampling (SMOTETomek)
52 Median Imputation Hybrid Sampling (SMOTETomek)
53 Median Imputation Hybrid Sampling (SMOTETomek)
54 Median Imputation Hybrid Sampling (SMOTETomek)
55 Median Imputation Hybrid Sampling (SMOTETomek)
56 Median Imputation Hybrid Sampling (SMOTETomek)
57 Median Imputation Hybrid Sampling (SMOTETomek)
58 Median Imputation Hybrid Sampling (SMOTETomek)
59 Median Imputation Hybrid Sampling (SMOTETomek)
60 Median Imputation Hybrid Sampling (SMOTETomek)
61 Median Imputation Hybrid Sampling (SMOTETomek)
62 Median Imputation Hybrid Sampling (SMOTETomek)
63 Median Imputation Hybrid Sampling (SMOTETomek)
64 Median Imputation Hybrid Sampling (SMOTETomek)
65 Median Imputation Hybrid Sampling (SMOTETomek)
66 Median Imputation Hybrid Sampling (SMOTETomek)
67 Median Imputation Hybrid Sampling (SMOTETomek)
68 Median Imputation Hybrid Sampling (SMOTETomek)
69 Median Imputation Hybrid Sampling (SMOTETomek)
70 Median Imputation Hybrid Sampling (SMOTETomek)
71 Median Imputation Hybrid Sampling (SMOTETomek)
72 Median Imputation Hybrid Sampling (SMOTETomek)
73 Median Imputation Hybrid Sampling (SMOTETomek)
74 Median Imputation Hybrid Sampling (SMOTETomek)
75 Median Imputation Hybrid Sampling (SMOTETomek)
76 Median Imputation Hybrid Sampling (SMOTETomek)
77 Median Imputation Hybrid Sampling (SMOTETomek)
78 Median Imputation Hybrid Sampling (SMOTETomek)
79 Median Imputation Hybrid Sampling (SMOTETomek)
80 Median Imputation Hybrid Sampling (SMOTETomek)
81 Median Imputation Hybrid Sampling (SMOTETomek)
82 Median Imputation Hybrid Sampling (SMOTETomek)
83 Median Imputation Hybrid Sampling (SMOTETomek)
84 Median Imputation Hybrid Sampling (SMOTETomek)
85 Median Imputation Hybrid Sampling (SMOTETomek)
86 Median Imputation Hybrid Sampling (SMOTETomek)
87 Median Imputation Hybrid Sampling (SMOTETomek)
88 Median Imputation Hybrid Sampling (SMOTETomek)
89 Median Imputation Hybrid Sampling (SMOTETomek)
90 Median Imputation Hybrid Sampling (SMOTETomek)
91 Median Imputation Hybrid Sampling (SMOTETomek)
92 Median Imputation Hybrid Sampling (SMOTETomek)
93 Median Imputation Hybrid Sampling (SMOTETomek)
94 Median Imputation Hybrid Sampling (SMOTETomek)
95 Median Imputation Hybrid Sampling (SMOTETomek)
96 Median Imputation Hybrid Sampling (SMOTETomek)
97 Median Imputation Hybrid Sampling (SMOTETomek)
98 Median Imputation Hybrid Sampling (SMOTETomek)
99 Median Imputation Hybrid Sampling (SMOTETomek)
100 Median Imputation Hybrid Sampling (SMOTETomek)
101 Median Imputation Hybrid Sampling (SMOTETomek)
102 Median Imputation Hybrid Sampling (SMOTETomek)
103 Median Imputation Hybrid Sampling (SMOTETomek)
104 Median Imputation Hybrid Sampling (SMOTETomek)
105 Median Imputation Hybrid Sampling (SMOTETomek)
106 Median Imputation Hybrid Sampling (SMOTETomek)
107 Median Imputation Hybrid Sampling (SMOTETomek)
108 Median Imputation Hybrid Sampling (SMOTETomek)
109 Median Imputation Hybrid Sampling (SMOTETomek)
110 Median Imputation Hybrid Sampling (SMOTETomek)
111 Median Imputation Hybrid Sampling (SMOTETomek)
112 Median Imputation Hybrid Sampling (SMOTETomek)
113 Median Imputation Hybrid Sampling (SMOTETomek)
114 Median Imputation Hybrid Sampling (SMOTETomek)
115 Median Imputation Hybrid Sampling (SMOTETomek)
116 Median Imputation Hybrid Sampling (SMOTETomek)
117 Median Imputation Hybrid Sampling (SMOTETomek)
118 Median Imputation Hybrid Sampling (SMOTETomek)
119 Median Imputation Hybrid Sampling (SMOTETomek)
120 Median Imputation Hybrid Sampling (SMOTETomek)
121 Median Imputation Hybrid Sampling (SMOTETomek)
122 Median Imputation Hybrid Sampling (SMOTETomek)
123 Median Imputation Hybrid Sampling (SMOTETomek)
124 Median Imputation Hybrid Sampling (SMOTETomek)
125 Median Imputation Hybrid Sampling (SMOTETomek)
126 Median Imputation Hybrid Sampling (SMOTETomek)
127 Median Imputation Hybrid Sampling (SMOTETomek)
128 Median Imputation Hybrid Sampling (SMOTETomek)
129 Median Imputation Hybrid Sampling (SMOTETomek)
130 Median Imputation Hybrid Sampling (SMOTETomek)
131 Median Imputation Hybrid Sampling (SMOTETomek)
132 Median Imputation Hybrid Sampling (SMOTETomek)
133 Median Imputation Hybrid Sampling (SMOTETomek)
134 Median Imputation Hybrid Sampling (SMOTETomek)
Model Unique Code
0 Gradient Boosting Machines (GBM)_Hybrid Sampli...
1 Gradient Boosting Machines (GBM)_Hybrid Sampli...
2 Gradient Boosting Machines (GBM)_Hybrid Sampli...
3 Gradient Boosting Machines (GBM)_Hybrid Sampli...
4 Gradient Boosting Machines (GBM)_Hybrid Sampli...
5 Gradient Boosting Machines (GBM)_Hybrid Sampli...
6 Gradient Boosting Machines (GBM)_Hybrid Sampli...
7 Gradient Boosting Machines (GBM)_Hybrid Sampli...
8 Gradient Boosting Machines (GBM)_Hybrid Sampli...
9 Gradient Boosting Machines (GBM)_Hybrid Sampli...
10 Gradient Boosting Machines (GBM)_Hybrid Sampli...
11 Gradient Boosting Machines (GBM)_Hybrid Sampli...
12 Gradient Boosting Machines (GBM)_Hybrid Sampli...
13 Gradient Boosting Machines (GBM)_Hybrid Sampli...
14 Gradient Boosting Machines (GBM)_Hybrid Sampli...
15 Gradient Boosting Machines (GBM)_Hybrid Sampli...
16 Gradient Boosting Machines (GBM)_Hybrid Sampli...
17 Gradient Boosting Machines (GBM)_Hybrid Sampli...
18 Gradient Boosting Machines (GBM)_Hybrid Sampli...
19 Gradient Boosting Machines (GBM)_Hybrid Sampli...
20 Gradient Boosting Machines (GBM)_Hybrid Sampli...
21 Gradient Boosting Machines (GBM)_Hybrid Sampli...
22 Gradient Boosting Machines (GBM)_Hybrid Sampli...
23 Gradient Boosting Machines (GBM)_Hybrid Sampli...
24 Gradient Boosting Machines (GBM)_Hybrid Sampli...
25 Gradient Boosting Machines (GBM)_Hybrid Sampli...
26 Gradient Boosting Machines (GBM)_Hybrid Sampli...
27 Gradient Boosting Machines (GBM)_Hybrid Sampli...
28 Gradient Boosting Machines (GBM)_Hybrid Sampli...
29 Gradient Boosting Machines (GBM)_Hybrid Sampli...
30 Gradient Boosting Machines (GBM)_Hybrid Sampli...
31 Gradient Boosting Machines (GBM)_Hybrid Sampli...
32 Gradient Boosting Machines (GBM)_Hybrid Sampli...
33 Gradient Boosting Machines (GBM)_Hybrid Sampli...
34 Gradient Boosting Machines (GBM)_Hybrid Sampli...
35 Gradient Boosting Machines (GBM)_Hybrid Sampli...
36 Gradient Boosting Machines (GBM)_Hybrid Sampli...
37 Gradient Boosting Machines (GBM)_Hybrid Sampli...
38 Gradient Boosting Machines (GBM)_Hybrid Sampli...
39 Gradient Boosting Machines (GBM)_Hybrid Sampli...
40 Gradient Boosting Machines (GBM)_Hybrid Sampli...
41 Gradient Boosting Machines (GBM)_Hybrid Sampli...
42 Gradient Boosting Machines (GBM)_Hybrid Sampli...
43 Gradient Boosting Machines (GBM)_Hybrid Sampli...
44 Gradient Boosting Machines (GBM)_Hybrid Sampli...
45 Gradient Boosting Machines (GBM)_Hybrid Sampli...
46 Gradient Boosting Machines (GBM)_Hybrid Sampli...
47 Gradient Boosting Machines (GBM)_Hybrid Sampli...
48 Gradient Boosting Machines (GBM)_Hybrid Sampli...
49 Gradient Boosting Machines (GBM)_Hybrid Sampli...
50 Gradient Boosting Machines (GBM)_Hybrid Sampli...
51 Gradient Boosting Machines (GBM)_Hybrid Sampli...
52 Gradient Boosting Machines (GBM)_Hybrid Sampli...
53 Gradient Boosting Machines (GBM)_Hybrid Sampli...
54 Gradient Boosting Machines (GBM)_Hybrid Sampli...
55 Gradient Boosting Machines (GBM)_Hybrid Sampli...
56 Gradient Boosting Machines (GBM)_Hybrid Sampli...
57 Gradient Boosting Machines (GBM)_Hybrid Sampli...
58 Gradient Boosting Machines (GBM)_Hybrid Sampli...
59 Gradient Boosting Machines (GBM)_Hybrid Sampli...
60 Gradient Boosting Machines (GBM)_Hybrid Sampli...
61 Gradient Boosting Machines (GBM)_Hybrid Sampli...
62 Gradient Boosting Machines (GBM)_Hybrid Sampli...
63 Gradient Boosting Machines (GBM)_Hybrid Sampli...
64 Gradient Boosting Machines (GBM)_Hybrid Sampli...
65 Gradient Boosting Machines (GBM)_Hybrid Sampli...
66 Gradient Boosting Machines (GBM)_Hybrid Sampli...
67 Gradient Boosting Machines (GBM)_Hybrid Sampli...
68 Gradient Boosting Machines (GBM)_Hybrid Sampli...
69 Gradient Boosting Machines (GBM)_Hybrid Sampli...
70 Gradient Boosting Machines (GBM)_Hybrid Sampli...
71 Gradient Boosting Machines (GBM)_Hybrid Sampli...
72 Gradient Boosting Machines (GBM)_Hybrid Sampli...
73 Gradient Boosting Machines (GBM)_Hybrid Sampli...
74 Gradient Boosting Machines (GBM)_Hybrid Sampli...
75 Gradient Boosting Machines (GBM)_Hybrid Sampli...
76 Gradient Boosting Machines (GBM)_Hybrid Sampli...
77 Gradient Boosting Machines (GBM)_Hybrid Sampli...
78 Gradient Boosting Machines (GBM)_Hybrid Sampli...
79 Gradient Boosting Machines (GBM)_Hybrid Sampli...
80 Gradient Boosting Machines (GBM)_Hybrid Sampli...
81 Gradient Boosting Machines (GBM)_Hybrid Sampli...
82 Gradient Boosting Machines (GBM)_Hybrid Sampli...
83 Gradient Boosting Machines (GBM)_Hybrid Sampli...
84 Gradient Boosting Machines (GBM)_Hybrid Sampli...
85 Gradient Boosting Machines (GBM)_Hybrid Sampli...
86 Gradient Boosting Machines (GBM)_Hybrid Sampli...
87 Gradient Boosting Machines (GBM)_Hybrid Sampli...
88 Gradient Boosting Machines (GBM)_Hybrid Sampli...
89 Gradient Boosting Machines (GBM)_Hybrid Sampli...
90 Gradient Boosting Machines (GBM)_Hybrid Sampli...
91 Gradient Boosting Machines (GBM)_Hybrid Sampli...
92 Gradient Boosting Machines (GBM)_Hybrid Sampli...
93 Gradient Boosting Machines (GBM)_Hybrid Sampli...
94 Gradient Boosting Machines (GBM)_Hybrid Sampli...
95 Gradient Boosting Machines (GBM)_Hybrid Sampli...
96 Gradient Boosting Machines (GBM)_Hybrid Sampli...
97 Gradient Boosting Machines (GBM)_Hybrid Sampli...
98 Gradient Boosting Machines (GBM)_Hybrid Sampli...
99 Gradient Boosting Machines (GBM)_Hybrid Sampli...
100 Gradient Boosting Machines (GBM)_Hybrid Sampli...
101 Gradient Boosting Machines (GBM)_Hybrid Sampli...
102 Gradient Boosting Machines (GBM)_Hybrid Sampli...
103 Gradient Boosting Machines (GBM)_Hybrid Sampli...
104 Gradient Boosting Machines (GBM)_Hybrid Sampli...
105 Gradient Boosting Machines (GBM)_Hybrid Sampli...
106 Gradient Boosting Machines (GBM)_Hybrid Sampli...
107 Gradient Boosting Machines (GBM)_Hybrid Sampli...
108 Gradient Boosting Machines (GBM)_Hybrid Sampli...
109 Gradient Boosting Machines (GBM)_Hybrid Sampli...
110 Gradient Boosting Machines (GBM)_Hybrid Sampli...
111 Gradient Boosting Machines (GBM)_Hybrid Sampli...
112 Gradient Boosting Machines (GBM)_Hybrid Sampli...
113 Gradient Boosting Machines (GBM)_Hybrid Sampli...
114 Gradient Boosting Machines (GBM)_Hybrid Sampli...
115 Gradient Boosting Machines (GBM)_Hybrid Sampli...
116 Gradient Boosting Machines (GBM)_Hybrid Sampli...
117 Gradient Boosting Machines (GBM)_Hybrid Sampli...
118 Gradient Boosting Machines (GBM)_Hybrid Sampli...
119 Gradient Boosting Machines (GBM)_Hybrid Sampli...
120 Gradient Boosting Machines (GBM)_Hybrid Sampli...
121 Gradient Boosting Machines (GBM)_Hybrid Sampli...
122 Gradient Boosting Machines (GBM)_Hybrid Sampli...
123 Gradient Boosting Machines (GBM)_Hybrid Sampli...
124 Gradient Boosting Machines (GBM)_Hybrid Sampli...
125 Gradient Boosting Machines (GBM)_Hybrid Sampli...
126 Gradient Boosting Machines (GBM)_Hybrid Sampli...
127 Gradient Boosting Machines (GBM)_Hybrid Sampli...
128 Gradient Boosting Machines (GBM)_Hybrid Sampli...
129 Gradient Boosting Machines (GBM)_Hybrid Sampli...
130 Gradient Boosting Machines (GBM)_Hybrid Sampli...
131 Gradient Boosting Machines (GBM)_Hybrid Sampli...
132 Gradient Boosting Machines (GBM)_Hybrid Sampli...
133 Gradient Boosting Machines (GBM)_Hybrid Sampli...
134 Gradient Boosting Machines (GBM)_Hybrid Sampli... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 XGBoost 0.842771 NaN Zero Imputation
1 XGBoost 0.134146 NaN Zero Imputation
2 XGBoost 0.278481 NaN Zero Imputation
3 XGBoost 0.181070 NaN Zero Imputation
4 XGBoost 0.659895 NaN Zero Imputation
5 XGBoost 0.243817 NaN Zero Imputation
6 XGBoost 0.319791 NaN Zero Imputation
7 XGBoost NaN binary:logistic Zero Imputation
8 XGBoost NaN None Zero Imputation
9 XGBoost NaN None Zero Imputation
10 XGBoost NaN None Zero Imputation
11 XGBoost NaN None Zero Imputation
12 XGBoost NaN None Zero Imputation
13 XGBoost NaN None Zero Imputation
14 XGBoost NaN None Zero Imputation
15 XGBoost NaN None Zero Imputation
16 XGBoost NaN False Zero Imputation
17 XGBoost NaN None Zero Imputation
18 XGBoost NaN None Zero Imputation
19 XGBoost NaN None Zero Imputation
20 XGBoost NaN None Zero Imputation
21 XGBoost NaN None Zero Imputation
22 XGBoost NaN None Zero Imputation
23 XGBoost NaN None Zero Imputation
24 XGBoost NaN None Zero Imputation
25 XGBoost NaN None Zero Imputation
26 XGBoost NaN None Zero Imputation
27 XGBoost NaN None Zero Imputation
28 XGBoost NaN None Zero Imputation
29 XGBoost NaN None Zero Imputation
30 XGBoost NaN None Zero Imputation
31 XGBoost NaN NaN Zero Imputation
32 XGBoost NaN None Zero Imputation
33 XGBoost NaN None Zero Imputation
34 XGBoost NaN None Zero Imputation
35 XGBoost NaN None Zero Imputation
36 XGBoost NaN None Zero Imputation
37 XGBoost NaN None Zero Imputation
38 XGBoost NaN None Zero Imputation
39 XGBoost NaN None Zero Imputation
40 XGBoost NaN None Zero Imputation
41 XGBoost NaN None Zero Imputation
42 XGBoost NaN None Zero Imputation
43 XGBoost NaN None Zero Imputation
44 XGBoost NaN None Zero Imputation
45 XGBoost NaN None Zero Imputation
46 XGBoost 0.845931 NaN Zero Imputation
47 XGBoost 0.103578 NaN Zero Imputation
48 XGBoost 0.335366 NaN Zero Imputation
49 XGBoost 0.158273 NaN Zero Imputation
50 XGBoost 0.684136 NaN Zero Imputation
51 XGBoost 0.282450 NaN Zero Imputation
52 XGBoost 0.368272 NaN Zero Imputation
53 XGBoost NaN binary:logistic Zero Imputation
54 XGBoost NaN None Zero Imputation
55 XGBoost NaN None Zero Imputation
56 XGBoost NaN None Zero Imputation
57 XGBoost NaN None Zero Imputation
58 XGBoost NaN None Zero Imputation
59 XGBoost NaN None Zero Imputation
60 XGBoost NaN None Zero Imputation
61 XGBoost NaN None Zero Imputation
62 XGBoost NaN False Zero Imputation
63 XGBoost NaN None Zero Imputation
64 XGBoost NaN None Zero Imputation
65 XGBoost NaN None Zero Imputation
66 XGBoost NaN None Zero Imputation
67 XGBoost NaN None Zero Imputation
68 XGBoost NaN None Zero Imputation
69 XGBoost NaN None Zero Imputation
70 XGBoost NaN None Zero Imputation
71 XGBoost NaN None Zero Imputation
72 XGBoost NaN None Zero Imputation
73 XGBoost NaN None Zero Imputation
74 XGBoost NaN None Zero Imputation
75 XGBoost NaN None Zero Imputation
76 XGBoost NaN None Zero Imputation
77 XGBoost NaN NaN Zero Imputation
78 XGBoost NaN None Zero Imputation
79 XGBoost NaN None Zero Imputation
80 XGBoost NaN None Zero Imputation
81 XGBoost NaN None Zero Imputation
82 XGBoost NaN None Zero Imputation
83 XGBoost NaN None Zero Imputation
84 XGBoost NaN None Zero Imputation
85 XGBoost NaN None Zero Imputation
86 XGBoost NaN None Zero Imputation
87 XGBoost NaN None Zero Imputation
88 XGBoost NaN None Zero Imputation
89 XGBoost NaN None Zero Imputation
90 XGBoost NaN None Zero Imputation
91 XGBoost NaN None Zero Imputation
92 XGBoost 0.842507 NaN Zero Imputation
93 XGBoost 0.117318 NaN Zero Imputation
94 XGBoost 0.336898 NaN Zero Imputation
95 XGBoost 0.174033 NaN Zero Imputation
96 XGBoost 0.672556 NaN Zero Imputation
97 XGBoost 0.280732 NaN Zero Imputation
98 XGBoost 0.345112 NaN Zero Imputation
99 XGBoost NaN binary:logistic Zero Imputation
100 XGBoost NaN None Zero Imputation
101 XGBoost NaN None Zero Imputation
102 XGBoost NaN None Zero Imputation
103 XGBoost NaN None Zero Imputation
104 XGBoost NaN None Zero Imputation
105 XGBoost NaN None Zero Imputation
106 XGBoost NaN None Zero Imputation
107 XGBoost NaN None Zero Imputation
108 XGBoost NaN False Zero Imputation
109 XGBoost NaN None Zero Imputation
110 XGBoost NaN None Zero Imputation
111 XGBoost NaN None Zero Imputation
112 XGBoost NaN None Zero Imputation
113 XGBoost NaN None Zero Imputation
114 XGBoost NaN None Zero Imputation
115 XGBoost NaN None Zero Imputation
116 XGBoost NaN None Zero Imputation
117 XGBoost NaN None Zero Imputation
118 XGBoost NaN None Zero Imputation
119 XGBoost NaN None Zero Imputation
120 XGBoost NaN None Zero Imputation
121 XGBoost NaN None Zero Imputation
122 XGBoost NaN None Zero Imputation
123 XGBoost NaN NaN Zero Imputation
124 XGBoost NaN None Zero Imputation
125 XGBoost NaN None Zero Imputation
126 XGBoost NaN None Zero Imputation
127 XGBoost NaN None Zero Imputation
128 XGBoost NaN None Zero Imputation
129 XGBoost NaN None Zero Imputation
130 XGBoost NaN None Zero Imputation
131 XGBoost NaN None Zero Imputation
132 XGBoost NaN None Zero Imputation
133 XGBoost NaN None Zero Imputation
134 XGBoost NaN None Zero Imputation
135 XGBoost NaN None Zero Imputation
136 XGBoost NaN None Zero Imputation
137 XGBoost NaN None Zero Imputation
138 XGBoost 0.846944 NaN Zero Imputation
139 XGBoost 0.117296 NaN Zero Imputation
140 XGBoost 0.301020 NaN Zero Imputation
141 XGBoost 0.168813 NaN Zero Imputation
142 XGBoost 0.668695 NaN Zero Imputation
143 XGBoost 0.254439 NaN Zero Imputation
144 XGBoost 0.337389 NaN Zero Imputation
145 XGBoost NaN binary:logistic Zero Imputation
146 XGBoost NaN None Zero Imputation
147 XGBoost NaN None Zero Imputation
148 XGBoost NaN None Zero Imputation
149 XGBoost NaN None Zero Imputation
150 XGBoost NaN None Zero Imputation
151 XGBoost NaN None Zero Imputation
152 XGBoost NaN None Zero Imputation
153 XGBoost NaN None Zero Imputation
154 XGBoost NaN False Zero Imputation
155 XGBoost NaN None Zero Imputation
156 XGBoost NaN None Zero Imputation
157 XGBoost NaN None Zero Imputation
158 XGBoost NaN None Zero Imputation
159 XGBoost NaN None Zero Imputation
160 XGBoost NaN None Zero Imputation
161 XGBoost NaN None Zero Imputation
162 XGBoost NaN None Zero Imputation
163 XGBoost NaN None Zero Imputation
164 XGBoost NaN None Zero Imputation
165 XGBoost NaN None Zero Imputation
166 XGBoost NaN None Zero Imputation
167 XGBoost NaN None Zero Imputation
168 XGBoost NaN None Zero Imputation
169 XGBoost NaN NaN Zero Imputation
170 XGBoost NaN None Zero Imputation
171 XGBoost NaN None Zero Imputation
172 XGBoost NaN None Zero Imputation
173 XGBoost NaN None Zero Imputation
174 XGBoost NaN None Zero Imputation
175 XGBoost NaN None Zero Imputation
176 XGBoost NaN None Zero Imputation
177 XGBoost NaN None Zero Imputation
178 XGBoost NaN None Zero Imputation
179 XGBoost NaN None Zero Imputation
180 XGBoost NaN None Zero Imputation
181 XGBoost NaN None Zero Imputation
182 XGBoost NaN None Zero Imputation
183 XGBoost NaN None Zero Imputation
184 XGBoost 0.853793 NaN Zero Imputation
185 XGBoost 0.151951 NaN Zero Imputation
186 XGBoost 0.342593 NaN Zero Imputation
187 XGBoost 0.210526 NaN Zero Imputation
188 XGBoost 0.701327 NaN Zero Imputation
189 XGBoost 0.313423 NaN Zero Imputation
190 XGBoost 0.402654 NaN Zero Imputation
191 XGBoost NaN binary:logistic Zero Imputation
192 XGBoost NaN None Zero Imputation
193 XGBoost NaN None Zero Imputation
194 XGBoost NaN None Zero Imputation
195 XGBoost NaN None Zero Imputation
196 XGBoost NaN None Zero Imputation
197 XGBoost NaN None Zero Imputation
198 XGBoost NaN None Zero Imputation
199 XGBoost NaN None Zero Imputation
200 XGBoost NaN False Zero Imputation
201 XGBoost NaN None Zero Imputation
202 XGBoost NaN None Zero Imputation
203 XGBoost NaN None Zero Imputation
204 XGBoost NaN None Zero Imputation
205 XGBoost NaN None Zero Imputation
206 XGBoost NaN None Zero Imputation
207 XGBoost NaN None Zero Imputation
208 XGBoost NaN None Zero Imputation
209 XGBoost NaN None Zero Imputation
210 XGBoost NaN None Zero Imputation
211 XGBoost NaN None Zero Imputation
212 XGBoost NaN None Zero Imputation
213 XGBoost NaN None Zero Imputation
214 XGBoost NaN None Zero Imputation
215 XGBoost NaN NaN Zero Imputation
216 XGBoost NaN None Zero Imputation
217 XGBoost NaN None Zero Imputation
218 XGBoost NaN None Zero Imputation
219 XGBoost NaN None Zero Imputation
220 XGBoost NaN None Zero Imputation
221 XGBoost NaN None Zero Imputation
222 XGBoost NaN None Zero Imputation
223 XGBoost NaN None Zero Imputation
224 XGBoost NaN None Zero Imputation
225 XGBoost NaN None Zero Imputation
226 XGBoost NaN None Zero Imputation
227 XGBoost NaN None Zero Imputation
228 XGBoost NaN None Zero Imputation
229 XGBoost NaN None Zero Imputation
Imbalance Class Technique Model Unique Code
0 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
1 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
2 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
3 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
4 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
5 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
6 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
7 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
8 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
9 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
10 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
11 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
12 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
13 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
14 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
15 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
16 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
17 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
18 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
19 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
20 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
21 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
22 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
23 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
24 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
25 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
26 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
27 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
28 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
29 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
30 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
31 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
32 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
33 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
34 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
35 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
36 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
37 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
38 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
39 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
40 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
41 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
42 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
43 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
44 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
45 Oversampling XGBoost_Oversampling_Zero Imputation_fold_0
46 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
47 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
48 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
49 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
50 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
51 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
52 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
53 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
54 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
55 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
56 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
57 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
58 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
59 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
60 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
61 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
62 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
63 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
64 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
65 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
66 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
67 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
68 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
69 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
70 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
71 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
72 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
73 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
74 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
75 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
76 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
77 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
78 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
79 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
80 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
81 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
82 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
83 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
84 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
85 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
86 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
87 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
88 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
89 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
90 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
91 Oversampling XGBoost_Oversampling_Zero Imputation_fold_1
92 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
93 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
94 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
95 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
96 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
97 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
98 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
99 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
100 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
101 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
102 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
103 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
104 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
105 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
106 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
107 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
108 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
109 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
110 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
111 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
112 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
113 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
114 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
115 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
116 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
117 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
118 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
119 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
120 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
121 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
122 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
123 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
124 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
125 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
126 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
127 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
128 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
129 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
130 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
131 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
132 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
133 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
134 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
135 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
136 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
137 Oversampling XGBoost_Oversampling_Zero Imputation_fold_2
138 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
139 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
140 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
141 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
142 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
143 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
144 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
145 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
146 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
147 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
148 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
149 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
150 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
151 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
152 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
153 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
154 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
155 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
156 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
157 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
158 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
159 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
160 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
161 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
162 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
163 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
164 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
165 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
166 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
167 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
168 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
169 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
170 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
171 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
172 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
173 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
174 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
175 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
176 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
177 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
178 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
179 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
180 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
181 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
182 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
183 Oversampling XGBoost_Oversampling_Zero Imputation_fold_3
184 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
185 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
186 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
187 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
188 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
189 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
190 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
191 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
192 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
193 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
194 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
195 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
196 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
197 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
198 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
199 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
200 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
201 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
202 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
203 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
204 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
205 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
206 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
207 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
208 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
209 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
210 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
211 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
212 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
213 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
214 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
215 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
216 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
217 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
218 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
219 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
220 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
221 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
222 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
223 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
224 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
225 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
226 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
227 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
228 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4
229 Oversampling XGBoost_Oversampling_Zero Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 XGBoost 0.845404 NaN Mean Imputation
1 XGBoost 0.133891 NaN Mean Imputation
2 XGBoost 0.270042 NaN Mean Imputation
3 XGBoost 0.179021 NaN Mean Imputation
4 XGBoost 0.663638 NaN Mean Imputation
5 XGBoost 0.243710 NaN Mean Imputation
6 XGBoost 0.327277 NaN Mean Imputation
7 XGBoost NaN binary:logistic Mean Imputation
8 XGBoost NaN None Mean Imputation
9 XGBoost NaN None Mean Imputation
10 XGBoost NaN None Mean Imputation
11 XGBoost NaN None Mean Imputation
12 XGBoost NaN None Mean Imputation
13 XGBoost NaN None Mean Imputation
14 XGBoost NaN None Mean Imputation
15 XGBoost NaN None Mean Imputation
16 XGBoost NaN False Mean Imputation
17 XGBoost NaN None Mean Imputation
18 XGBoost NaN None Mean Imputation
19 XGBoost NaN None Mean Imputation
20 XGBoost NaN None Mean Imputation
21 XGBoost NaN None Mean Imputation
22 XGBoost NaN None Mean Imputation
23 XGBoost NaN None Mean Imputation
24 XGBoost NaN None Mean Imputation
25 XGBoost NaN None Mean Imputation
26 XGBoost NaN None Mean Imputation
27 XGBoost NaN None Mean Imputation
28 XGBoost NaN None Mean Imputation
29 XGBoost NaN None Mean Imputation
30 XGBoost NaN None Mean Imputation
31 XGBoost NaN NaN Mean Imputation
32 XGBoost NaN None Mean Imputation
33 XGBoost NaN None Mean Imputation
34 XGBoost NaN None Mean Imputation
35 XGBoost NaN None Mean Imputation
36 XGBoost NaN None Mean Imputation
37 XGBoost NaN None Mean Imputation
38 XGBoost NaN None Mean Imputation
39 XGBoost NaN None Mean Imputation
40 XGBoost NaN None Mean Imputation
41 XGBoost NaN None Mean Imputation
42 XGBoost NaN None Mean Imputation
43 XGBoost NaN None Mean Imputation
44 XGBoost NaN None Mean Imputation
45 XGBoost NaN None Mean Imputation
46 XGBoost 0.847511 NaN Mean Imputation
47 XGBoost 0.107750 NaN Mean Imputation
48 XGBoost 0.347561 NaN Mean Imputation
49 XGBoost 0.164502 NaN Mean Imputation
50 XGBoost 0.674761 NaN Mean Imputation
51 XGBoost 0.298336 NaN Mean Imputation
52 XGBoost 0.349521 NaN Mean Imputation
53 XGBoost NaN binary:logistic Mean Imputation
54 XGBoost NaN None Mean Imputation
55 XGBoost NaN None Mean Imputation
56 XGBoost NaN None Mean Imputation
57 XGBoost NaN None Mean Imputation
58 XGBoost NaN None Mean Imputation
59 XGBoost NaN None Mean Imputation
60 XGBoost NaN None Mean Imputation
61 XGBoost NaN None Mean Imputation
62 XGBoost NaN False Mean Imputation
63 XGBoost NaN None Mean Imputation
64 XGBoost NaN None Mean Imputation
65 XGBoost NaN None Mean Imputation
66 XGBoost NaN None Mean Imputation
67 XGBoost NaN None Mean Imputation
68 XGBoost NaN None Mean Imputation
69 XGBoost NaN None Mean Imputation
70 XGBoost NaN None Mean Imputation
71 XGBoost NaN None Mean Imputation
72 XGBoost NaN None Mean Imputation
73 XGBoost NaN None Mean Imputation
74 XGBoost NaN None Mean Imputation
75 XGBoost NaN None Mean Imputation
76 XGBoost NaN None Mean Imputation
77 XGBoost NaN NaN Mean Imputation
78 XGBoost NaN None Mean Imputation
79 XGBoost NaN None Mean Imputation
80 XGBoost NaN None Mean Imputation
81 XGBoost NaN None Mean Imputation
82 XGBoost NaN None Mean Imputation
83 XGBoost NaN None Mean Imputation
84 XGBoost NaN None Mean Imputation
85 XGBoost NaN None Mean Imputation
86 XGBoost NaN None Mean Imputation
87 XGBoost NaN None Mean Imputation
88 XGBoost NaN None Mean Imputation
89 XGBoost NaN None Mean Imputation
90 XGBoost NaN None Mean Imputation
91 XGBoost NaN None Mean Imputation
92 XGBoost 0.843297 NaN Mean Imputation
93 XGBoost 0.127737 NaN Mean Imputation
94 XGBoost 0.374332 NaN Mean Imputation
95 XGBoost 0.190476 NaN Mean Imputation
96 XGBoost 0.683479 NaN Mean Imputation
97 XGBoost 0.260429 NaN Mean Imputation
98 XGBoost 0.366959 NaN Mean Imputation
99 XGBoost NaN binary:logistic Mean Imputation
100 XGBoost NaN None Mean Imputation
101 XGBoost NaN None Mean Imputation
102 XGBoost NaN None Mean Imputation
103 XGBoost NaN None Mean Imputation
104 XGBoost NaN None Mean Imputation
105 XGBoost NaN None Mean Imputation
106 XGBoost NaN None Mean Imputation
107 XGBoost NaN None Mean Imputation
108 XGBoost NaN False Mean Imputation
109 XGBoost NaN None Mean Imputation
110 XGBoost NaN None Mean Imputation
111 XGBoost NaN None Mean Imputation
112 XGBoost NaN None Mean Imputation
113 XGBoost NaN None Mean Imputation
114 XGBoost NaN None Mean Imputation
115 XGBoost NaN None Mean Imputation
116 XGBoost NaN None Mean Imputation
117 XGBoost NaN None Mean Imputation
118 XGBoost NaN None Mean Imputation
119 XGBoost NaN None Mean Imputation
120 XGBoost NaN None Mean Imputation
121 XGBoost NaN None Mean Imputation
122 XGBoost NaN None Mean Imputation
123 XGBoost NaN NaN Mean Imputation
124 XGBoost NaN None Mean Imputation
125 XGBoost NaN None Mean Imputation
126 XGBoost NaN None Mean Imputation
127 XGBoost NaN None Mean Imputation
128 XGBoost NaN None Mean Imputation
129 XGBoost NaN None Mean Imputation
130 XGBoost NaN None Mean Imputation
131 XGBoost NaN None Mean Imputation
132 XGBoost NaN None Mean Imputation
133 XGBoost NaN None Mean Imputation
134 XGBoost NaN None Mean Imputation
135 XGBoost NaN None Mean Imputation
136 XGBoost NaN None Mean Imputation
137 XGBoost NaN None Mean Imputation
138 XGBoost 0.851159 NaN Mean Imputation
139 XGBoost 0.119588 NaN Mean Imputation
140 XGBoost 0.295918 NaN Mean Imputation
141 XGBoost 0.170338 NaN Mean Imputation
142 XGBoost 0.665867 NaN Mean Imputation
143 XGBoost 0.259507 NaN Mean Imputation
144 XGBoost 0.331733 NaN Mean Imputation
145 XGBoost NaN binary:logistic Mean Imputation
146 XGBoost NaN None Mean Imputation
147 XGBoost NaN None Mean Imputation
148 XGBoost NaN None Mean Imputation
149 XGBoost NaN None Mean Imputation
150 XGBoost NaN None Mean Imputation
151 XGBoost NaN None Mean Imputation
152 XGBoost NaN None Mean Imputation
153 XGBoost NaN None Mean Imputation
154 XGBoost NaN False Mean Imputation
155 XGBoost NaN None Mean Imputation
156 XGBoost NaN None Mean Imputation
157 XGBoost NaN None Mean Imputation
158 XGBoost NaN None Mean Imputation
159 XGBoost NaN None Mean Imputation
160 XGBoost NaN None Mean Imputation
161 XGBoost NaN None Mean Imputation
162 XGBoost NaN None Mean Imputation
163 XGBoost NaN None Mean Imputation
164 XGBoost NaN None Mean Imputation
165 XGBoost NaN None Mean Imputation
166 XGBoost NaN None Mean Imputation
167 XGBoost NaN None Mean Imputation
168 XGBoost NaN None Mean Imputation
169 XGBoost NaN NaN Mean Imputation
170 XGBoost NaN None Mean Imputation
171 XGBoost NaN None Mean Imputation
172 XGBoost NaN None Mean Imputation
173 XGBoost NaN None Mean Imputation
174 XGBoost NaN None Mean Imputation
175 XGBoost NaN None Mean Imputation
176 XGBoost NaN None Mean Imputation
177 XGBoost NaN None Mean Imputation
178 XGBoost NaN None Mean Imputation
179 XGBoost NaN None Mean Imputation
180 XGBoost NaN None Mean Imputation
181 XGBoost NaN None Mean Imputation
182 XGBoost NaN None Mean Imputation
183 XGBoost NaN None Mean Imputation
184 XGBoost 0.855638 NaN Mean Imputation
185 XGBoost 0.148305 NaN Mean Imputation
186 XGBoost 0.324074 NaN Mean Imputation
187 XGBoost 0.203488 NaN Mean Imputation
188 XGBoost 0.698515 NaN Mean Imputation
189 XGBoost 0.320458 NaN Mean Imputation
190 XGBoost 0.397030 NaN Mean Imputation
191 XGBoost NaN binary:logistic Mean Imputation
192 XGBoost NaN None Mean Imputation
193 XGBoost NaN None Mean Imputation
194 XGBoost NaN None Mean Imputation
195 XGBoost NaN None Mean Imputation
196 XGBoost NaN None Mean Imputation
197 XGBoost NaN None Mean Imputation
198 XGBoost NaN None Mean Imputation
199 XGBoost NaN None Mean Imputation
200 XGBoost NaN False Mean Imputation
201 XGBoost NaN None Mean Imputation
202 XGBoost NaN None Mean Imputation
203 XGBoost NaN None Mean Imputation
204 XGBoost NaN None Mean Imputation
205 XGBoost NaN None Mean Imputation
206 XGBoost NaN None Mean Imputation
207 XGBoost NaN None Mean Imputation
208 XGBoost NaN None Mean Imputation
209 XGBoost NaN None Mean Imputation
210 XGBoost NaN None Mean Imputation
211 XGBoost NaN None Mean Imputation
212 XGBoost NaN None Mean Imputation
213 XGBoost NaN None Mean Imputation
214 XGBoost NaN None Mean Imputation
215 XGBoost NaN NaN Mean Imputation
216 XGBoost NaN None Mean Imputation
217 XGBoost NaN None Mean Imputation
218 XGBoost NaN None Mean Imputation
219 XGBoost NaN None Mean Imputation
220 XGBoost NaN None Mean Imputation
221 XGBoost NaN None Mean Imputation
222 XGBoost NaN None Mean Imputation
223 XGBoost NaN None Mean Imputation
224 XGBoost NaN None Mean Imputation
225 XGBoost NaN None Mean Imputation
226 XGBoost NaN None Mean Imputation
227 XGBoost NaN None Mean Imputation
228 XGBoost NaN None Mean Imputation
229 XGBoost NaN None Mean Imputation
Imbalance Class Technique Model Unique Code
0 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
1 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
2 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
3 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
4 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
5 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
6 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
7 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
8 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
9 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
10 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
11 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
12 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
13 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
14 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
15 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
16 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
17 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
18 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
19 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
20 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
21 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
22 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
23 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
24 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
25 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
26 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
27 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
28 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
29 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
30 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
31 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
32 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
33 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
34 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
35 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
36 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
37 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
38 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
39 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
40 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
41 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
42 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
43 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
44 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
45 Oversampling XGBoost_Oversampling_Mean Imputation_fold_0
46 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
47 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
48 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
49 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
50 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
51 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
52 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
53 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
54 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
55 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
56 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
57 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
58 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
59 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
60 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
61 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
62 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
63 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
64 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
65 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
66 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
67 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
68 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
69 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
70 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
71 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
72 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
73 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
74 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
75 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
76 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
77 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
78 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
79 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
80 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
81 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
82 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
83 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
84 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
85 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
86 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
87 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
88 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
89 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
90 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
91 Oversampling XGBoost_Oversampling_Mean Imputation_fold_1
92 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
93 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
94 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
95 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
96 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
97 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
98 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
99 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
100 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
101 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
102 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
103 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
104 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
105 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
106 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
107 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
108 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
109 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
110 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
111 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
112 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
113 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
114 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
115 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
116 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
117 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
118 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
119 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
120 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
121 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
122 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
123 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
124 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
125 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
126 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
127 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
128 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
129 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
130 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
131 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
132 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
133 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
134 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
135 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
136 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
137 Oversampling XGBoost_Oversampling_Mean Imputation_fold_2
138 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
139 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
140 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
141 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
142 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
143 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
144 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
145 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
146 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
147 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
148 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
149 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
150 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
151 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
152 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
153 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
154 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
155 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
156 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
157 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
158 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
159 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
160 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
161 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
162 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
163 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
164 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
165 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
166 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
167 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
168 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
169 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
170 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
171 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
172 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
173 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
174 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
175 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
176 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
177 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
178 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
179 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
180 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
181 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
182 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
183 Oversampling XGBoost_Oversampling_Mean Imputation_fold_3
184 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
185 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
186 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
187 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
188 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
189 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
190 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
191 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
192 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
193 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
194 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
195 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
196 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
197 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
198 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
199 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
200 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
201 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
202 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
203 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
204 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
205 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
206 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
207 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
208 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
209 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
210 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
211 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
212 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
213 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
214 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
215 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
216 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
217 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
218 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
219 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
220 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
221 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
222 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
223 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
224 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
225 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
226 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
227 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
228 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4
229 Oversampling XGBoost_Oversampling_Mean Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 XGBoost 0.845668 NaN Median Imputation
1 XGBoost 0.129512 NaN Median Imputation
2 XGBoost 0.257384 NaN Median Imputation
3 XGBoost 0.172316 NaN Median Imputation
4 XGBoost 0.661887 NaN Median Imputation
5 XGBoost 0.257361 NaN Median Imputation
6 XGBoost 0.323774 NaN Median Imputation
7 XGBoost NaN binary:logistic Median Imputation
8 XGBoost NaN None Median Imputation
9 XGBoost NaN None Median Imputation
10 XGBoost NaN None Median Imputation
11 XGBoost NaN None Median Imputation
12 XGBoost NaN None Median Imputation
13 XGBoost NaN None Median Imputation
14 XGBoost NaN None Median Imputation
15 XGBoost NaN None Median Imputation
16 XGBoost NaN False Median Imputation
17 XGBoost NaN None Median Imputation
18 XGBoost NaN None Median Imputation
19 XGBoost NaN None Median Imputation
20 XGBoost NaN None Median Imputation
21 XGBoost NaN None Median Imputation
22 XGBoost NaN None Median Imputation
23 XGBoost NaN None Median Imputation
24 XGBoost NaN None Median Imputation
25 XGBoost NaN None Median Imputation
26 XGBoost NaN None Median Imputation
27 XGBoost NaN None Median Imputation
28 XGBoost NaN None Median Imputation
29 XGBoost NaN None Median Imputation
30 XGBoost NaN None Median Imputation
31 XGBoost NaN NaN Median Imputation
32 XGBoost NaN None Median Imputation
33 XGBoost NaN None Median Imputation
34 XGBoost NaN None Median Imputation
35 XGBoost NaN None Median Imputation
36 XGBoost NaN None Median Imputation
37 XGBoost NaN None Median Imputation
38 XGBoost NaN None Median Imputation
39 XGBoost NaN None Median Imputation
40 XGBoost NaN None Median Imputation
41 XGBoost NaN None Median Imputation
42 XGBoost NaN None Median Imputation
43 XGBoost NaN None Median Imputation
44 XGBoost NaN None Median Imputation
45 XGBoost NaN None Median Imputation
46 XGBoost 0.851462 NaN Median Imputation
47 XGBoost 0.100000 NaN Median Imputation
48 XGBoost 0.304878 NaN Median Imputation
49 XGBoost 0.150602 NaN Median Imputation
50 XGBoost 0.672507 NaN Median Imputation
51 XGBoost 0.295607 NaN Median Imputation
52 XGBoost 0.345015 NaN Median Imputation
53 XGBoost NaN binary:logistic Median Imputation
54 XGBoost NaN None Median Imputation
55 XGBoost NaN None Median Imputation
56 XGBoost NaN None Median Imputation
57 XGBoost NaN None Median Imputation
58 XGBoost NaN None Median Imputation
59 XGBoost NaN None Median Imputation
60 XGBoost NaN None Median Imputation
61 XGBoost NaN None Median Imputation
62 XGBoost NaN False Median Imputation
63 XGBoost NaN None Median Imputation
64 XGBoost NaN None Median Imputation
65 XGBoost NaN None Median Imputation
66 XGBoost NaN None Median Imputation
67 XGBoost NaN None Median Imputation
68 XGBoost NaN None Median Imputation
69 XGBoost NaN None Median Imputation
70 XGBoost NaN None Median Imputation
71 XGBoost NaN None Median Imputation
72 XGBoost NaN None Median Imputation
73 XGBoost NaN None Median Imputation
74 XGBoost NaN None Median Imputation
75 XGBoost NaN None Median Imputation
76 XGBoost NaN None Median Imputation
77 XGBoost NaN NaN Median Imputation
78 XGBoost NaN None Median Imputation
79 XGBoost NaN None Median Imputation
80 XGBoost NaN None Median Imputation
81 XGBoost NaN None Median Imputation
82 XGBoost NaN None Median Imputation
83 XGBoost NaN None Median Imputation
84 XGBoost NaN None Median Imputation
85 XGBoost NaN None Median Imputation
86 XGBoost NaN None Median Imputation
87 XGBoost NaN None Median Imputation
88 XGBoost NaN None Median Imputation
89 XGBoost NaN None Median Imputation
90 XGBoost NaN None Median Imputation
91 XGBoost NaN None Median Imputation
92 XGBoost 0.855676 NaN Median Imputation
93 XGBoost 0.132383 NaN Median Imputation
94 XGBoost 0.347594 NaN Median Imputation
95 XGBoost 0.191740 NaN Median Imputation
96 XGBoost 0.690471 NaN Median Imputation
97 XGBoost 0.299796 NaN Median Imputation
98 XGBoost 0.380943 NaN Median Imputation
99 XGBoost NaN binary:logistic Median Imputation
100 XGBoost NaN None Median Imputation
101 XGBoost NaN None Median Imputation
102 XGBoost NaN None Median Imputation
103 XGBoost NaN None Median Imputation
104 XGBoost NaN None Median Imputation
105 XGBoost NaN None Median Imputation
106 XGBoost NaN None Median Imputation
107 XGBoost NaN None Median Imputation
108 XGBoost NaN False Median Imputation
109 XGBoost NaN None Median Imputation
110 XGBoost NaN None Median Imputation
111 XGBoost NaN None Median Imputation
112 XGBoost NaN None Median Imputation
113 XGBoost NaN None Median Imputation
114 XGBoost NaN None Median Imputation
115 XGBoost NaN None Median Imputation
116 XGBoost NaN None Median Imputation
117 XGBoost NaN None Median Imputation
118 XGBoost NaN None Median Imputation
119 XGBoost NaN None Median Imputation
120 XGBoost NaN None Median Imputation
121 XGBoost NaN None Median Imputation
122 XGBoost NaN None Median Imputation
123 XGBoost NaN NaN Median Imputation
124 XGBoost NaN None Median Imputation
125 XGBoost NaN None Median Imputation
126 XGBoost NaN None Median Imputation
127 XGBoost NaN None Median Imputation
128 XGBoost NaN None Median Imputation
129 XGBoost NaN None Median Imputation
130 XGBoost NaN None Median Imputation
131 XGBoost NaN None Median Imputation
132 XGBoost NaN None Median Imputation
133 XGBoost NaN None Median Imputation
134 XGBoost NaN None Median Imputation
135 XGBoost NaN None Median Imputation
136 XGBoost NaN None Median Imputation
137 XGBoost NaN None Median Imputation
138 XGBoost 0.852740 NaN Median Imputation
139 XGBoost 0.125773 NaN Median Imputation
140 XGBoost 0.311224 NaN Median Imputation
141 XGBoost 0.179148 NaN Median Imputation
142 XGBoost 0.657651 NaN Median Imputation
143 XGBoost 0.238838 NaN Median Imputation
144 XGBoost 0.315302 NaN Median Imputation
145 XGBoost NaN binary:logistic Median Imputation
146 XGBoost NaN None Median Imputation
147 XGBoost NaN None Median Imputation
148 XGBoost NaN None Median Imputation
149 XGBoost NaN None Median Imputation
150 XGBoost NaN None Median Imputation
151 XGBoost NaN None Median Imputation
152 XGBoost NaN None Median Imputation
153 XGBoost NaN None Median Imputation
154 XGBoost NaN False Median Imputation
155 XGBoost NaN None Median Imputation
156 XGBoost NaN None Median Imputation
157 XGBoost NaN None Median Imputation
158 XGBoost NaN None Median Imputation
159 XGBoost NaN None Median Imputation
160 XGBoost NaN None Median Imputation
161 XGBoost NaN None Median Imputation
162 XGBoost NaN None Median Imputation
163 XGBoost NaN None Median Imputation
164 XGBoost NaN None Median Imputation
165 XGBoost NaN None Median Imputation
166 XGBoost NaN None Median Imputation
167 XGBoost NaN None Median Imputation
168 XGBoost NaN None Median Imputation
169 XGBoost NaN NaN Median Imputation
170 XGBoost NaN None Median Imputation
171 XGBoost NaN None Median Imputation
172 XGBoost NaN None Median Imputation
173 XGBoost NaN None Median Imputation
174 XGBoost NaN None Median Imputation
175 XGBoost NaN None Median Imputation
176 XGBoost NaN None Median Imputation
177 XGBoost NaN None Median Imputation
178 XGBoost NaN None Median Imputation
179 XGBoost NaN None Median Imputation
180 XGBoost NaN None Median Imputation
181 XGBoost NaN None Median Imputation
182 XGBoost NaN None Median Imputation
183 XGBoost NaN None Median Imputation
184 XGBoost 0.862487 NaN Median Imputation
185 XGBoost 0.147465 NaN Median Imputation
186 XGBoost 0.296296 NaN Median Imputation
187 XGBoost 0.196923 NaN Median Imputation
188 XGBoost 0.688897 NaN Median Imputation
189 XGBoost 0.288827 NaN Median Imputation
190 XGBoost 0.377795 NaN Median Imputation
191 XGBoost NaN binary:logistic Median Imputation
192 XGBoost NaN None Median Imputation
193 XGBoost NaN None Median Imputation
194 XGBoost NaN None Median Imputation
195 XGBoost NaN None Median Imputation
196 XGBoost NaN None Median Imputation
197 XGBoost NaN None Median Imputation
198 XGBoost NaN None Median Imputation
199 XGBoost NaN None Median Imputation
200 XGBoost NaN False Median Imputation
201 XGBoost NaN None Median Imputation
202 XGBoost NaN None Median Imputation
203 XGBoost NaN None Median Imputation
204 XGBoost NaN None Median Imputation
205 XGBoost NaN None Median Imputation
206 XGBoost NaN None Median Imputation
207 XGBoost NaN None Median Imputation
208 XGBoost NaN None Median Imputation
209 XGBoost NaN None Median Imputation
210 XGBoost NaN None Median Imputation
211 XGBoost NaN None Median Imputation
212 XGBoost NaN None Median Imputation
213 XGBoost NaN None Median Imputation
214 XGBoost NaN None Median Imputation
215 XGBoost NaN NaN Median Imputation
216 XGBoost NaN None Median Imputation
217 XGBoost NaN None Median Imputation
218 XGBoost NaN None Median Imputation
219 XGBoost NaN None Median Imputation
220 XGBoost NaN None Median Imputation
221 XGBoost NaN None Median Imputation
222 XGBoost NaN None Median Imputation
223 XGBoost NaN None Median Imputation
224 XGBoost NaN None Median Imputation
225 XGBoost NaN None Median Imputation
226 XGBoost NaN None Median Imputation
227 XGBoost NaN None Median Imputation
228 XGBoost NaN None Median Imputation
229 XGBoost NaN None Median Imputation
Imbalance Class Technique Model Unique Code
0 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
1 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
2 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
3 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
4 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
5 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
6 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
7 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
8 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
9 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
10 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
11 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
12 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
13 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
14 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
15 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
16 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
17 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
18 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
19 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
20 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
21 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
22 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
23 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
24 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
25 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
26 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
27 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
28 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
29 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
30 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
31 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
32 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
33 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
34 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
35 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
36 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
37 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
38 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
39 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
40 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
41 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
42 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
43 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
44 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
45 Oversampling XGBoost_Oversampling_Median Imputation_fold_0
46 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
47 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
48 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
49 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
50 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
51 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
52 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
53 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
54 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
55 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
56 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
57 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
58 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
59 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
60 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
61 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
62 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
63 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
64 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
65 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
66 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
67 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
68 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
69 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
70 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
71 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
72 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
73 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
74 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
75 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
76 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
77 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
78 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
79 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
80 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
81 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
82 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
83 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
84 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
85 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
86 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
87 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
88 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
89 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
90 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
91 Oversampling XGBoost_Oversampling_Median Imputation_fold_1
92 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
93 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
94 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
95 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
96 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
97 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
98 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
99 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
100 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
101 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
102 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
103 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
104 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
105 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
106 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
107 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
108 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
109 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
110 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
111 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
112 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
113 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
114 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
115 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
116 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
117 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
118 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
119 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
120 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
121 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
122 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
123 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
124 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
125 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
126 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
127 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
128 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
129 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
130 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
131 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
132 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
133 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
134 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
135 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
136 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
137 Oversampling XGBoost_Oversampling_Median Imputation_fold_2
138 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
139 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
140 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
141 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
142 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
143 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
144 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
145 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
146 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
147 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
148 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
149 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
150 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
151 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
152 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
153 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
154 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
155 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
156 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
157 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
158 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
159 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
160 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
161 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
162 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
163 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
164 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
165 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
166 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
167 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
168 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
169 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
170 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
171 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
172 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
173 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
174 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
175 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
176 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
177 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
178 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
179 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
180 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
181 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
182 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
183 Oversampling XGBoost_Oversampling_Median Imputation_fold_3
184 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
185 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
186 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
187 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
188 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
189 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
190 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
191 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
192 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
193 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
194 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
195 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
196 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
197 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
198 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
199 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
200 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
201 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
202 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
203 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
204 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
205 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
206 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
207 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
208 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
209 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
210 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
211 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
212 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
213 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
214 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
215 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
216 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
217 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
218 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
219 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
220 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
221 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
222 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
223 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
224 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
225 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
226 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
227 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
228 Oversampling XGBoost_Oversampling_Median Imputation_fold_4
229 Oversampling XGBoost_Oversampling_Median Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 XGBoost 0.646826 NaN Zero Imputation
1 XGBoost 0.106277 NaN Zero Imputation
2 XGBoost 0.628692 NaN Zero Imputation
3 XGBoost 0.181818 NaN Zero Imputation
4 XGBoost 0.686344 NaN Zero Imputation
5 XGBoost 0.311652 NaN Zero Imputation
6 XGBoost 0.372689 NaN Zero Imputation
7 XGBoost NaN binary:logistic Zero Imputation
8 XGBoost NaN None Zero Imputation
9 XGBoost NaN None Zero Imputation
10 XGBoost NaN None Zero Imputation
11 XGBoost NaN None Zero Imputation
12 XGBoost NaN None Zero Imputation
13 XGBoost NaN None Zero Imputation
14 XGBoost NaN None Zero Imputation
15 XGBoost NaN None Zero Imputation
16 XGBoost NaN False Zero Imputation
17 XGBoost NaN None Zero Imputation
18 XGBoost NaN None Zero Imputation
19 XGBoost NaN None Zero Imputation
20 XGBoost NaN None Zero Imputation
21 XGBoost NaN None Zero Imputation
22 XGBoost NaN None Zero Imputation
23 XGBoost NaN None Zero Imputation
24 XGBoost NaN None Zero Imputation
25 XGBoost NaN None Zero Imputation
26 XGBoost NaN None Zero Imputation
27 XGBoost NaN None Zero Imputation
28 XGBoost NaN None Zero Imputation
29 XGBoost NaN None Zero Imputation
30 XGBoost NaN None Zero Imputation
31 XGBoost NaN NaN Zero Imputation
32 XGBoost NaN None Zero Imputation
33 XGBoost NaN None Zero Imputation
34 XGBoost NaN None Zero Imputation
35 XGBoost NaN None Zero Imputation
36 XGBoost NaN None Zero Imputation
37 XGBoost NaN None Zero Imputation
38 XGBoost NaN None Zero Imputation
39 XGBoost NaN None Zero Imputation
40 XGBoost NaN None Zero Imputation
41 XGBoost NaN None Zero Imputation
42 XGBoost NaN None Zero Imputation
43 XGBoost NaN None Zero Imputation
44 XGBoost NaN None Zero Imputation
45 XGBoost NaN None Zero Imputation
46 XGBoost 0.614169 NaN Zero Imputation
47 XGBoost 0.068348 NaN Zero Imputation
48 XGBoost 0.628049 NaN Zero Imputation
49 XGBoost 0.123279 NaN Zero Imputation
50 XGBoost 0.670874 NaN Zero Imputation
51 XGBoost 0.273534 NaN Zero Imputation
52 XGBoost 0.341749 NaN Zero Imputation
53 XGBoost NaN binary:logistic Zero Imputation
54 XGBoost NaN None Zero Imputation
55 XGBoost NaN None Zero Imputation
56 XGBoost NaN None Zero Imputation
57 XGBoost NaN None Zero Imputation
58 XGBoost NaN None Zero Imputation
59 XGBoost NaN None Zero Imputation
60 XGBoost NaN None Zero Imputation
61 XGBoost NaN None Zero Imputation
62 XGBoost NaN False Zero Imputation
63 XGBoost NaN None Zero Imputation
64 XGBoost NaN None Zero Imputation
65 XGBoost NaN None Zero Imputation
66 XGBoost NaN None Zero Imputation
67 XGBoost NaN None Zero Imputation
68 XGBoost NaN None Zero Imputation
69 XGBoost NaN None Zero Imputation
70 XGBoost NaN None Zero Imputation
71 XGBoost NaN None Zero Imputation
72 XGBoost NaN None Zero Imputation
73 XGBoost NaN None Zero Imputation
74 XGBoost NaN None Zero Imputation
75 XGBoost NaN None Zero Imputation
76 XGBoost NaN None Zero Imputation
77 XGBoost NaN NaN Zero Imputation
78 XGBoost NaN None Zero Imputation
79 XGBoost NaN None Zero Imputation
80 XGBoost NaN None Zero Imputation
81 XGBoost NaN None Zero Imputation
82 XGBoost NaN None Zero Imputation
83 XGBoost NaN None Zero Imputation
84 XGBoost NaN None Zero Imputation
85 XGBoost NaN None Zero Imputation
86 XGBoost NaN None Zero Imputation
87 XGBoost NaN None Zero Imputation
88 XGBoost NaN None Zero Imputation
89 XGBoost NaN None Zero Imputation
90 XGBoost NaN None Zero Imputation
91 XGBoost NaN None Zero Imputation
92 XGBoost 0.619173 NaN Zero Imputation
93 XGBoost 0.082283 NaN Zero Imputation
94 XGBoost 0.663102 NaN Zero Imputation
95 XGBoost 0.146399 NaN Zero Imputation
96 XGBoost 0.673209 NaN Zero Imputation
97 XGBoost 0.299799 NaN Zero Imputation
98 XGBoost 0.346417 NaN Zero Imputation
99 XGBoost NaN binary:logistic Zero Imputation
100 XGBoost NaN None Zero Imputation
101 XGBoost NaN None Zero Imputation
102 XGBoost NaN None Zero Imputation
103 XGBoost NaN None Zero Imputation
104 XGBoost NaN None Zero Imputation
105 XGBoost NaN None Zero Imputation
106 XGBoost NaN None Zero Imputation
107 XGBoost NaN None Zero Imputation
108 XGBoost NaN False Zero Imputation
109 XGBoost NaN None Zero Imputation
110 XGBoost NaN None Zero Imputation
111 XGBoost NaN None Zero Imputation
112 XGBoost NaN None Zero Imputation
113 XGBoost NaN None Zero Imputation
114 XGBoost NaN None Zero Imputation
115 XGBoost NaN None Zero Imputation
116 XGBoost NaN None Zero Imputation
117 XGBoost NaN None Zero Imputation
118 XGBoost NaN None Zero Imputation
119 XGBoost NaN None Zero Imputation
120 XGBoost NaN None Zero Imputation
121 XGBoost NaN None Zero Imputation
122 XGBoost NaN None Zero Imputation
123 XGBoost NaN NaN Zero Imputation
124 XGBoost NaN None Zero Imputation
125 XGBoost NaN None Zero Imputation
126 XGBoost NaN None Zero Imputation
127 XGBoost NaN None Zero Imputation
128 XGBoost NaN None Zero Imputation
129 XGBoost NaN None Zero Imputation
130 XGBoost NaN None Zero Imputation
131 XGBoost NaN None Zero Imputation
132 XGBoost NaN None Zero Imputation
133 XGBoost NaN None Zero Imputation
134 XGBoost NaN None Zero Imputation
135 XGBoost NaN None Zero Imputation
136 XGBoost NaN None Zero Imputation
137 XGBoost NaN None Zero Imputation
138 XGBoost 0.629347 NaN Zero Imputation
139 XGBoost 0.084420 NaN Zero Imputation
140 XGBoost 0.627551 NaN Zero Imputation
141 XGBoost 0.148820 NaN Zero Imputation
142 XGBoost 0.648162 NaN Zero Imputation
143 XGBoost 0.269495 NaN Zero Imputation
144 XGBoost 0.296324 NaN Zero Imputation
145 XGBoost NaN binary:logistic Zero Imputation
146 XGBoost NaN None Zero Imputation
147 XGBoost NaN None Zero Imputation
148 XGBoost NaN None Zero Imputation
149 XGBoost NaN None Zero Imputation
150 XGBoost NaN None Zero Imputation
151 XGBoost NaN None Zero Imputation
152 XGBoost NaN None Zero Imputation
153 XGBoost NaN None Zero Imputation
154 XGBoost NaN False Zero Imputation
155 XGBoost NaN None Zero Imputation
156 XGBoost NaN None Zero Imputation
157 XGBoost NaN None Zero Imputation
158 XGBoost NaN None Zero Imputation
159 XGBoost NaN None Zero Imputation
160 XGBoost NaN None Zero Imputation
161 XGBoost NaN None Zero Imputation
162 XGBoost NaN None Zero Imputation
163 XGBoost NaN None Zero Imputation
164 XGBoost NaN None Zero Imputation
165 XGBoost NaN None Zero Imputation
166 XGBoost NaN None Zero Imputation
167 XGBoost NaN None Zero Imputation
168 XGBoost NaN None Zero Imputation
169 XGBoost NaN NaN Zero Imputation
170 XGBoost NaN None Zero Imputation
171 XGBoost NaN None Zero Imputation
172 XGBoost NaN None Zero Imputation
173 XGBoost NaN None Zero Imputation
174 XGBoost NaN None Zero Imputation
175 XGBoost NaN None Zero Imputation
176 XGBoost NaN None Zero Imputation
177 XGBoost NaN None Zero Imputation
178 XGBoost NaN None Zero Imputation
179 XGBoost NaN None Zero Imputation
180 XGBoost NaN None Zero Imputation
181 XGBoost NaN None Zero Imputation
182 XGBoost NaN None Zero Imputation
183 XGBoost NaN None Zero Imputation
184 XGBoost 0.624868 NaN Zero Imputation
185 XGBoost 0.096257 NaN Zero Imputation
186 XGBoost 0.666667 NaN Zero Imputation
187 XGBoost 0.168224 NaN Zero Imputation
188 XGBoost 0.693474 NaN Zero Imputation
189 XGBoost 0.306590 NaN Zero Imputation
190 XGBoost 0.386948 NaN Zero Imputation
191 XGBoost NaN binary:logistic Zero Imputation
192 XGBoost NaN None Zero Imputation
193 XGBoost NaN None Zero Imputation
194 XGBoost NaN None Zero Imputation
195 XGBoost NaN None Zero Imputation
196 XGBoost NaN None Zero Imputation
197 XGBoost NaN None Zero Imputation
198 XGBoost NaN None Zero Imputation
199 XGBoost NaN None Zero Imputation
200 XGBoost NaN False Zero Imputation
201 XGBoost NaN None Zero Imputation
202 XGBoost NaN None Zero Imputation
203 XGBoost NaN None Zero Imputation
204 XGBoost NaN None Zero Imputation
205 XGBoost NaN None Zero Imputation
206 XGBoost NaN None Zero Imputation
207 XGBoost NaN None Zero Imputation
208 XGBoost NaN None Zero Imputation
209 XGBoost NaN None Zero Imputation
210 XGBoost NaN None Zero Imputation
211 XGBoost NaN None Zero Imputation
212 XGBoost NaN None Zero Imputation
213 XGBoost NaN None Zero Imputation
214 XGBoost NaN None Zero Imputation
215 XGBoost NaN NaN Zero Imputation
216 XGBoost NaN None Zero Imputation
217 XGBoost NaN None Zero Imputation
218 XGBoost NaN None Zero Imputation
219 XGBoost NaN None Zero Imputation
220 XGBoost NaN None Zero Imputation
221 XGBoost NaN None Zero Imputation
222 XGBoost NaN None Zero Imputation
223 XGBoost NaN None Zero Imputation
224 XGBoost NaN None Zero Imputation
225 XGBoost NaN None Zero Imputation
226 XGBoost NaN None Zero Imputation
227 XGBoost NaN None Zero Imputation
228 XGBoost NaN None Zero Imputation
229 XGBoost NaN None Zero Imputation
Imbalance Class Technique Model Unique Code
0 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
1 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
2 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
3 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
4 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
5 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
6 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
7 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
8 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
9 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
10 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
11 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
12 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
13 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
14 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
15 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
16 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
17 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
18 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
19 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
20 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
21 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
22 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
23 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
24 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
25 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
26 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
27 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
28 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
29 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
30 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
31 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
32 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
33 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
34 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
35 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
36 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
37 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
38 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
39 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
40 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
41 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
42 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
43 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
44 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
45 Undersampling XGBoost_Undersampling_Zero Imputation_fold_0
46 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
47 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
48 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
49 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
50 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
51 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
52 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
53 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
54 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
55 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
56 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
57 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
58 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
59 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
60 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
61 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
62 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
63 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
64 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
65 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
66 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
67 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
68 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
69 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
70 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
71 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
72 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
73 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
74 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
75 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
76 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
77 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
78 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
79 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
80 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
81 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
82 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
83 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
84 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
85 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
86 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
87 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
88 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
89 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
90 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
91 Undersampling XGBoost_Undersampling_Zero Imputation_fold_1
92 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
93 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
94 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
95 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
96 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
97 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
98 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
99 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
100 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
101 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
102 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
103 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
104 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
105 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
106 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
107 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
108 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
109 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
110 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
111 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
112 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
113 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
114 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
115 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
116 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
117 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
118 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
119 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
120 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
121 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
122 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
123 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
124 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
125 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
126 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
127 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
128 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
129 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
130 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
131 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
132 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
133 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
134 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
135 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
136 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
137 Undersampling XGBoost_Undersampling_Zero Imputation_fold_2
138 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
139 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
140 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
141 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
142 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
143 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
144 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
145 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
146 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
147 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
148 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
149 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
150 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
151 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
152 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
153 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
154 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
155 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
156 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
157 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
158 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
159 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
160 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
161 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
162 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
163 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
164 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
165 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
166 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
167 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
168 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
169 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
170 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
171 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
172 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
173 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
174 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
175 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
176 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
177 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
178 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
179 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
180 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
181 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
182 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
183 Undersampling XGBoost_Undersampling_Zero Imputation_fold_3
184 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
185 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
186 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
187 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
188 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
189 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
190 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
191 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
192 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
193 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
194 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
195 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
196 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
197 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
198 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
199 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
200 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
201 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
202 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
203 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
204 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
205 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
206 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
207 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
208 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
209 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
210 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
211 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
212 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
213 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
214 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
215 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
216 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
217 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
218 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
219 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
220 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
221 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
222 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
223 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
224 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
225 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
226 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
227 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
228 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4
229 Undersampling XGBoost_Undersampling_Zero Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 XGBoost 0.634975 NaN Mean Imputation
1 XGBoost 0.105697 NaN Mean Imputation
2 XGBoost 0.649789 NaN Mean Imputation
3 XGBoost 0.181818 NaN Mean Imputation
4 XGBoost 0.666268 NaN Mean Imputation
5 XGBoost 0.292480 NaN Mean Imputation
6 XGBoost 0.332537 NaN Mean Imputation
7 XGBoost NaN binary:logistic Mean Imputation
8 XGBoost NaN None Mean Imputation
9 XGBoost NaN None Mean Imputation
10 XGBoost NaN None Mean Imputation
11 XGBoost NaN None Mean Imputation
12 XGBoost NaN None Mean Imputation
13 XGBoost NaN None Mean Imputation
14 XGBoost NaN None Mean Imputation
15 XGBoost NaN None Mean Imputation
16 XGBoost NaN False Mean Imputation
17 XGBoost NaN None Mean Imputation
18 XGBoost NaN None Mean Imputation
19 XGBoost NaN None Mean Imputation
20 XGBoost NaN None Mean Imputation
21 XGBoost NaN None Mean Imputation
22 XGBoost NaN None Mean Imputation
23 XGBoost NaN None Mean Imputation
24 XGBoost NaN None Mean Imputation
25 XGBoost NaN None Mean Imputation
26 XGBoost NaN None Mean Imputation
27 XGBoost NaN None Mean Imputation
28 XGBoost NaN None Mean Imputation
29 XGBoost NaN None Mean Imputation
30 XGBoost NaN None Mean Imputation
31 XGBoost NaN NaN Mean Imputation
32 XGBoost NaN None Mean Imputation
33 XGBoost NaN None Mean Imputation
34 XGBoost NaN None Mean Imputation
35 XGBoost NaN None Mean Imputation
36 XGBoost NaN None Mean Imputation
37 XGBoost NaN None Mean Imputation
38 XGBoost NaN None Mean Imputation
39 XGBoost NaN None Mean Imputation
40 XGBoost NaN None Mean Imputation
41 XGBoost NaN None Mean Imputation
42 XGBoost NaN None Mean Imputation
43 XGBoost NaN None Mean Imputation
44 XGBoost NaN None Mean Imputation
45 XGBoost NaN None Mean Imputation
46 XGBoost 0.631815 NaN Mean Imputation
47 XGBoost 0.077397 NaN Mean Imputation
48 XGBoost 0.689024 NaN Mean Imputation
49 XGBoost 0.139163 NaN Mean Imputation
50 XGBoost 0.709903 NaN Mean Imputation
51 XGBoost 0.345441 NaN Mean Imputation
52 XGBoost 0.419807 NaN Mean Imputation
53 XGBoost NaN binary:logistic Mean Imputation
54 XGBoost NaN None Mean Imputation
55 XGBoost NaN None Mean Imputation
56 XGBoost NaN None Mean Imputation
57 XGBoost NaN None Mean Imputation
58 XGBoost NaN None Mean Imputation
59 XGBoost NaN None Mean Imputation
60 XGBoost NaN None Mean Imputation
61 XGBoost NaN None Mean Imputation
62 XGBoost NaN False Mean Imputation
63 XGBoost NaN None Mean Imputation
64 XGBoost NaN None Mean Imputation
65 XGBoost NaN None Mean Imputation
66 XGBoost NaN None Mean Imputation
67 XGBoost NaN None Mean Imputation
68 XGBoost NaN None Mean Imputation
69 XGBoost NaN None Mean Imputation
70 XGBoost NaN None Mean Imputation
71 XGBoost NaN None Mean Imputation
72 XGBoost NaN None Mean Imputation
73 XGBoost NaN None Mean Imputation
74 XGBoost NaN None Mean Imputation
75 XGBoost NaN None Mean Imputation
76 XGBoost NaN None Mean Imputation
77 XGBoost NaN NaN Mean Imputation
78 XGBoost NaN None Mean Imputation
79 XGBoost NaN None Mean Imputation
80 XGBoost NaN None Mean Imputation
81 XGBoost NaN None Mean Imputation
82 XGBoost NaN None Mean Imputation
83 XGBoost NaN None Mean Imputation
84 XGBoost NaN None Mean Imputation
85 XGBoost NaN None Mean Imputation
86 XGBoost NaN None Mean Imputation
87 XGBoost NaN None Mean Imputation
88 XGBoost NaN None Mean Imputation
89 XGBoost NaN None Mean Imputation
90 XGBoost NaN None Mean Imputation
91 XGBoost NaN None Mean Imputation
92 XGBoost 0.628391 NaN Mean Imputation
93 XGBoost 0.080822 NaN Mean Imputation
94 XGBoost 0.631016 NaN Mean Imputation
95 XGBoost 0.143291 NaN Mean Imputation
96 XGBoost 0.669068 NaN Mean Imputation
97 XGBoost 0.279817 NaN Mean Imputation
98 XGBoost 0.338135 NaN Mean Imputation
99 XGBoost NaN binary:logistic Mean Imputation
100 XGBoost NaN None Mean Imputation
101 XGBoost NaN None Mean Imputation
102 XGBoost NaN None Mean Imputation
103 XGBoost NaN None Mean Imputation
104 XGBoost NaN None Mean Imputation
105 XGBoost NaN None Mean Imputation
106 XGBoost NaN None Mean Imputation
107 XGBoost NaN None Mean Imputation
108 XGBoost NaN False Mean Imputation
109 XGBoost NaN None Mean Imputation
110 XGBoost NaN None Mean Imputation
111 XGBoost NaN None Mean Imputation
112 XGBoost NaN None Mean Imputation
113 XGBoost NaN None Mean Imputation
114 XGBoost NaN None Mean Imputation
115 XGBoost NaN None Mean Imputation
116 XGBoost NaN None Mean Imputation
117 XGBoost NaN None Mean Imputation
118 XGBoost NaN None Mean Imputation
119 XGBoost NaN None Mean Imputation
120 XGBoost NaN None Mean Imputation
121 XGBoost NaN None Mean Imputation
122 XGBoost NaN None Mean Imputation
123 XGBoost NaN NaN Mean Imputation
124 XGBoost NaN None Mean Imputation
125 XGBoost NaN None Mean Imputation
126 XGBoost NaN None Mean Imputation
127 XGBoost NaN None Mean Imputation
128 XGBoost NaN None Mean Imputation
129 XGBoost NaN None Mean Imputation
130 XGBoost NaN None Mean Imputation
131 XGBoost NaN None Mean Imputation
132 XGBoost NaN None Mean Imputation
133 XGBoost NaN None Mean Imputation
134 XGBoost NaN None Mean Imputation
135 XGBoost NaN None Mean Imputation
136 XGBoost NaN None Mean Imputation
137 XGBoost NaN None Mean Imputation
138 XGBoost 0.618282 NaN Mean Imputation
139 XGBoost 0.082055 NaN Mean Imputation
140 XGBoost 0.627551 NaN Mean Imputation
141 XGBoost 0.145133 NaN Mean Imputation
142 XGBoost 0.659335 NaN Mean Imputation
143 XGBoost 0.254467 NaN Mean Imputation
144 XGBoost 0.318671 NaN Mean Imputation
145 XGBoost NaN binary:logistic Mean Imputation
146 XGBoost NaN None Mean Imputation
147 XGBoost NaN None Mean Imputation
148 XGBoost NaN None Mean Imputation
149 XGBoost NaN None Mean Imputation
150 XGBoost NaN None Mean Imputation
151 XGBoost NaN None Mean Imputation
152 XGBoost NaN None Mean Imputation
153 XGBoost NaN None Mean Imputation
154 XGBoost NaN False Mean Imputation
155 XGBoost NaN None Mean Imputation
156 XGBoost NaN None Mean Imputation
157 XGBoost NaN None Mean Imputation
158 XGBoost NaN None Mean Imputation
159 XGBoost NaN None Mean Imputation
160 XGBoost NaN None Mean Imputation
161 XGBoost NaN None Mean Imputation
162 XGBoost NaN None Mean Imputation
163 XGBoost NaN None Mean Imputation
164 XGBoost NaN None Mean Imputation
165 XGBoost NaN None Mean Imputation
166 XGBoost NaN None Mean Imputation
167 XGBoost NaN None Mean Imputation
168 XGBoost NaN None Mean Imputation
169 XGBoost NaN NaN Mean Imputation
170 XGBoost NaN None Mean Imputation
171 XGBoost NaN None Mean Imputation
172 XGBoost NaN None Mean Imputation
173 XGBoost NaN None Mean Imputation
174 XGBoost NaN None Mean Imputation
175 XGBoost NaN None Mean Imputation
176 XGBoost NaN None Mean Imputation
177 XGBoost NaN None Mean Imputation
178 XGBoost NaN None Mean Imputation
179 XGBoost NaN None Mean Imputation
180 XGBoost NaN None Mean Imputation
181 XGBoost NaN None Mean Imputation
182 XGBoost NaN None Mean Imputation
183 XGBoost NaN None Mean Imputation
184 XGBoost 0.635406 NaN Mean Imputation
185 XGBoost 0.092748 NaN Mean Imputation
186 XGBoost 0.615741 NaN Mean Imputation
187 XGBoost 0.161212 NaN Mean Imputation
188 XGBoost 0.677085 NaN Mean Imputation
189 XGBoost 0.270717 NaN Mean Imputation
190 XGBoost 0.354171 NaN Mean Imputation
191 XGBoost NaN binary:logistic Mean Imputation
192 XGBoost NaN None Mean Imputation
193 XGBoost NaN None Mean Imputation
194 XGBoost NaN None Mean Imputation
195 XGBoost NaN None Mean Imputation
196 XGBoost NaN None Mean Imputation
197 XGBoost NaN None Mean Imputation
198 XGBoost NaN None Mean Imputation
199 XGBoost NaN None Mean Imputation
200 XGBoost NaN False Mean Imputation
201 XGBoost NaN None Mean Imputation
202 XGBoost NaN None Mean Imputation
203 XGBoost NaN None Mean Imputation
204 XGBoost NaN None Mean Imputation
205 XGBoost NaN None Mean Imputation
206 XGBoost NaN None Mean Imputation
207 XGBoost NaN None Mean Imputation
208 XGBoost NaN None Mean Imputation
209 XGBoost NaN None Mean Imputation
210 XGBoost NaN None Mean Imputation
211 XGBoost NaN None Mean Imputation
212 XGBoost NaN None Mean Imputation
213 XGBoost NaN None Mean Imputation
214 XGBoost NaN None Mean Imputation
215 XGBoost NaN NaN Mean Imputation
216 XGBoost NaN None Mean Imputation
217 XGBoost NaN None Mean Imputation
218 XGBoost NaN None Mean Imputation
219 XGBoost NaN None Mean Imputation
220 XGBoost NaN None Mean Imputation
221 XGBoost NaN None Mean Imputation
222 XGBoost NaN None Mean Imputation
223 XGBoost NaN None Mean Imputation
224 XGBoost NaN None Mean Imputation
225 XGBoost NaN None Mean Imputation
226 XGBoost NaN None Mean Imputation
227 XGBoost NaN None Mean Imputation
228 XGBoost NaN None Mean Imputation
229 XGBoost NaN None Mean Imputation
Imbalance Class Technique Model Unique Code
0 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
1 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
2 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
3 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
4 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
5 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
6 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
7 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
8 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
9 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
10 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
11 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
12 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
13 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
14 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
15 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
16 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
17 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
18 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
19 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
20 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
21 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
22 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
23 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
24 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
25 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
26 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
27 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
28 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
29 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
30 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
31 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
32 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
33 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
34 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
35 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
36 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
37 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
38 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
39 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
40 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
41 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
42 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
43 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
44 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
45 Undersampling XGBoost_Undersampling_Mean Imputation_fold_0
46 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
47 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
48 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
49 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
50 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
51 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
52 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
53 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
54 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
55 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
56 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
57 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
58 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
59 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
60 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
61 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
62 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
63 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
64 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
65 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
66 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
67 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
68 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
69 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
70 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
71 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
72 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
73 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
74 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
75 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
76 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
77 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
78 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
79 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
80 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
81 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
82 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
83 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
84 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
85 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
86 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
87 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
88 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
89 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
90 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
91 Undersampling XGBoost_Undersampling_Mean Imputation_fold_1
92 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
93 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
94 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
95 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
96 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
97 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
98 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
99 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
100 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
101 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
102 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
103 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
104 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
105 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
106 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
107 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
108 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
109 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
110 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
111 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
112 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
113 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
114 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
115 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
116 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
117 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
118 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
119 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
120 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
121 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
122 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
123 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
124 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
125 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
126 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
127 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
128 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
129 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
130 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
131 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
132 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
133 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
134 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
135 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
136 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
137 Undersampling XGBoost_Undersampling_Mean Imputation_fold_2
138 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
139 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
140 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
141 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
142 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
143 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
144 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
145 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
146 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
147 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
148 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
149 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
150 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
151 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
152 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
153 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
154 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
155 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
156 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
157 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
158 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
159 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
160 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
161 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
162 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
163 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
164 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
165 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
166 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
167 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
168 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
169 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
170 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
171 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
172 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
173 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
174 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
175 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
176 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
177 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
178 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
179 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
180 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
181 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
182 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
183 Undersampling XGBoost_Undersampling_Mean Imputation_fold_3
184 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
185 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
186 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
187 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
188 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
189 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
190 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
191 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
192 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
193 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
194 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
195 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
196 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
197 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
198 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
199 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
200 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
201 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
202 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
203 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
204 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
205 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
206 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
207 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
208 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
209 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
210 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
211 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
212 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
213 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
214 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
215 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
216 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
217 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
218 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
219 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
220 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
221 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
222 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
223 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
224 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
225 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
226 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
227 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
228 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4
229 Undersampling XGBoost_Undersampling_Mean Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 XGBoost 0.647617 NaN Median Imputation
1 XGBoost 0.110954 NaN Median Imputation
2 XGBoost 0.662447 NaN Median Imputation
3 XGBoost 0.190073 NaN Median Imputation
4 XGBoost 0.689952 NaN Median Imputation
5 XGBoost 0.317252 NaN Median Imputation
6 XGBoost 0.379903 NaN Median Imputation
7 XGBoost NaN binary:logistic Median Imputation
8 XGBoost NaN None Median Imputation
9 XGBoost NaN None Median Imputation
10 XGBoost NaN None Median Imputation
11 XGBoost NaN None Median Imputation
12 XGBoost NaN None Median Imputation
13 XGBoost NaN None Median Imputation
14 XGBoost NaN None Median Imputation
15 XGBoost NaN None Median Imputation
16 XGBoost NaN False Median Imputation
17 XGBoost NaN None Median Imputation
18 XGBoost NaN None Median Imputation
19 XGBoost NaN None Median Imputation
20 XGBoost NaN None Median Imputation
21 XGBoost NaN None Median Imputation
22 XGBoost NaN None Median Imputation
23 XGBoost NaN None Median Imputation
24 XGBoost NaN None Median Imputation
25 XGBoost NaN None Median Imputation
26 XGBoost NaN None Median Imputation
27 XGBoost NaN None Median Imputation
28 XGBoost NaN None Median Imputation
29 XGBoost NaN None Median Imputation
30 XGBoost NaN None Median Imputation
31 XGBoost NaN NaN Median Imputation
32 XGBoost NaN None Median Imputation
33 XGBoost NaN None Median Imputation
34 XGBoost NaN None Median Imputation
35 XGBoost NaN None Median Imputation
36 XGBoost NaN None Median Imputation
37 XGBoost NaN None Median Imputation
38 XGBoost NaN None Median Imputation
39 XGBoost NaN None Median Imputation
40 XGBoost NaN None Median Imputation
41 XGBoost NaN None Median Imputation
42 XGBoost NaN None Median Imputation
43 XGBoost NaN None Median Imputation
44 XGBoost NaN None Median Imputation
45 XGBoost NaN None Median Imputation
46 XGBoost 0.658678 NaN Median Imputation
47 XGBoost 0.074436 NaN Median Imputation
48 XGBoost 0.603659 NaN Median Imputation
49 XGBoost 0.132530 NaN Median Imputation
50 XGBoost 0.673760 NaN Median Imputation
51 XGBoost 0.291203 NaN Median Imputation
52 XGBoost 0.347521 NaN Median Imputation
53 XGBoost NaN binary:logistic Median Imputation
54 XGBoost NaN None Median Imputation
55 XGBoost NaN None Median Imputation
56 XGBoost NaN None Median Imputation
57 XGBoost NaN None Median Imputation
58 XGBoost NaN None Median Imputation
59 XGBoost NaN None Median Imputation
60 XGBoost NaN None Median Imputation
61 XGBoost NaN None Median Imputation
62 XGBoost NaN False Median Imputation
63 XGBoost NaN None Median Imputation
64 XGBoost NaN None Median Imputation
65 XGBoost NaN None Median Imputation
66 XGBoost NaN None Median Imputation
67 XGBoost NaN None Median Imputation
68 XGBoost NaN None Median Imputation
69 XGBoost NaN None Median Imputation
70 XGBoost NaN None Median Imputation
71 XGBoost NaN None Median Imputation
72 XGBoost NaN None Median Imputation
73 XGBoost NaN None Median Imputation
74 XGBoost NaN None Median Imputation
75 XGBoost NaN None Median Imputation
76 XGBoost NaN None Median Imputation
77 XGBoost NaN NaN Median Imputation
78 XGBoost NaN None Median Imputation
79 XGBoost NaN None Median Imputation
80 XGBoost NaN None Median Imputation
81 XGBoost NaN None Median Imputation
82 XGBoost NaN None Median Imputation
83 XGBoost NaN None Median Imputation
84 XGBoost NaN None Median Imputation
85 XGBoost NaN None Median Imputation
86 XGBoost NaN None Median Imputation
87 XGBoost NaN None Median Imputation
88 XGBoost NaN None Median Imputation
89 XGBoost NaN None Median Imputation
90 XGBoost NaN None Median Imputation
91 XGBoost NaN None Median Imputation
92 XGBoost 0.625230 NaN Median Imputation
93 XGBoost 0.082996 NaN Median Imputation
94 XGBoost 0.657754 NaN Median Imputation
95 XGBoost 0.147394 NaN Median Imputation
96 XGBoost 0.677525 NaN Median Imputation
97 XGBoost 0.283901 NaN Median Imputation
98 XGBoost 0.355049 NaN Median Imputation
99 XGBoost NaN binary:logistic Median Imputation
100 XGBoost NaN None Median Imputation
101 XGBoost NaN None Median Imputation
102 XGBoost NaN None Median Imputation
103 XGBoost NaN None Median Imputation
104 XGBoost NaN None Median Imputation
105 XGBoost NaN None Median Imputation
106 XGBoost NaN None Median Imputation
107 XGBoost NaN None Median Imputation
108 XGBoost NaN False Median Imputation
109 XGBoost NaN None Median Imputation
110 XGBoost NaN None Median Imputation
111 XGBoost NaN None Median Imputation
112 XGBoost NaN None Median Imputation
113 XGBoost NaN None Median Imputation
114 XGBoost NaN None Median Imputation
115 XGBoost NaN None Median Imputation
116 XGBoost NaN None Median Imputation
117 XGBoost NaN None Median Imputation
118 XGBoost NaN None Median Imputation
119 XGBoost NaN None Median Imputation
120 XGBoost NaN None Median Imputation
121 XGBoost NaN None Median Imputation
122 XGBoost NaN None Median Imputation
123 XGBoost NaN NaN Median Imputation
124 XGBoost NaN None Median Imputation
125 XGBoost NaN None Median Imputation
126 XGBoost NaN None Median Imputation
127 XGBoost NaN None Median Imputation
128 XGBoost NaN None Median Imputation
129 XGBoost NaN None Median Imputation
130 XGBoost NaN None Median Imputation
131 XGBoost NaN None Median Imputation
132 XGBoost NaN None Median Imputation
133 XGBoost NaN None Median Imputation
134 XGBoost NaN None Median Imputation
135 XGBoost NaN None Median Imputation
136 XGBoost NaN None Median Imputation
137 XGBoost NaN None Median Imputation
138 XGBoost 0.628556 NaN Median Imputation
139 XGBoost 0.086512 NaN Median Imputation
140 XGBoost 0.647959 NaN Median Imputation
141 XGBoost 0.152644 NaN Median Imputation
142 XGBoost 0.683165 NaN Median Imputation
143 XGBoost 0.296434 NaN Median Imputation
144 XGBoost 0.366331 NaN Median Imputation
145 XGBoost NaN binary:logistic Median Imputation
146 XGBoost NaN None Median Imputation
147 XGBoost NaN None Median Imputation
148 XGBoost NaN None Median Imputation
149 XGBoost NaN None Median Imputation
150 XGBoost NaN None Median Imputation
151 XGBoost NaN None Median Imputation
152 XGBoost NaN None Median Imputation
153 XGBoost NaN None Median Imputation
154 XGBoost NaN False Median Imputation
155 XGBoost NaN None Median Imputation
156 XGBoost NaN None Median Imputation
157 XGBoost NaN None Median Imputation
158 XGBoost NaN None Median Imputation
159 XGBoost NaN None Median Imputation
160 XGBoost NaN None Median Imputation
161 XGBoost NaN None Median Imputation
162 XGBoost NaN None Median Imputation
163 XGBoost NaN None Median Imputation
164 XGBoost NaN None Median Imputation
165 XGBoost NaN None Median Imputation
166 XGBoost NaN None Median Imputation
167 XGBoost NaN None Median Imputation
168 XGBoost NaN None Median Imputation
169 XGBoost NaN NaN Median Imputation
170 XGBoost NaN None Median Imputation
171 XGBoost NaN None Median Imputation
172 XGBoost NaN None Median Imputation
173 XGBoost NaN None Median Imputation
174 XGBoost NaN None Median Imputation
175 XGBoost NaN None Median Imputation
176 XGBoost NaN None Median Imputation
177 XGBoost NaN None Median Imputation
178 XGBoost NaN None Median Imputation
179 XGBoost NaN None Median Imputation
180 XGBoost NaN None Median Imputation
181 XGBoost NaN None Median Imputation
182 XGBoost NaN None Median Imputation
183 XGBoost NaN None Median Imputation
184 XGBoost 0.638567 NaN Median Imputation
185 XGBoost 0.091231 NaN Median Imputation
186 XGBoost 0.597222 NaN Median Imputation
187 XGBoost 0.158282 NaN Median Imputation
188 XGBoost 0.682583 NaN Median Imputation
189 XGBoost 0.284275 NaN Median Imputation
190 XGBoost 0.365167 NaN Median Imputation
191 XGBoost NaN binary:logistic Median Imputation
192 XGBoost NaN None Median Imputation
193 XGBoost NaN None Median Imputation
194 XGBoost NaN None Median Imputation
195 XGBoost NaN None Median Imputation
196 XGBoost NaN None Median Imputation
197 XGBoost NaN None Median Imputation
198 XGBoost NaN None Median Imputation
199 XGBoost NaN None Median Imputation
200 XGBoost NaN False Median Imputation
201 XGBoost NaN None Median Imputation
202 XGBoost NaN None Median Imputation
203 XGBoost NaN None Median Imputation
204 XGBoost NaN None Median Imputation
205 XGBoost NaN None Median Imputation
206 XGBoost NaN None Median Imputation
207 XGBoost NaN None Median Imputation
208 XGBoost NaN None Median Imputation
209 XGBoost NaN None Median Imputation
210 XGBoost NaN None Median Imputation
211 XGBoost NaN None Median Imputation
212 XGBoost NaN None Median Imputation
213 XGBoost NaN None Median Imputation
214 XGBoost NaN None Median Imputation
215 XGBoost NaN NaN Median Imputation
216 XGBoost NaN None Median Imputation
217 XGBoost NaN None Median Imputation
218 XGBoost NaN None Median Imputation
219 XGBoost NaN None Median Imputation
220 XGBoost NaN None Median Imputation
221 XGBoost NaN None Median Imputation
222 XGBoost NaN None Median Imputation
223 XGBoost NaN None Median Imputation
224 XGBoost NaN None Median Imputation
225 XGBoost NaN None Median Imputation
226 XGBoost NaN None Median Imputation
227 XGBoost NaN None Median Imputation
228 XGBoost NaN None Median Imputation
229 XGBoost NaN None Median Imputation
Imbalance Class Technique Model Unique Code
0 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
1 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
2 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
3 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
4 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
5 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
6 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
7 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
8 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
9 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
10 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
11 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
12 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
13 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
14 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
15 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
16 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
17 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
18 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
19 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
20 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
21 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
22 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
23 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
24 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
25 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
26 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
27 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
28 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
29 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
30 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
31 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
32 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
33 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
34 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
35 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
36 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
37 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
38 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
39 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
40 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
41 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
42 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
43 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
44 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
45 Undersampling XGBoost_Undersampling_Median Imputation_fold_0
46 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
47 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
48 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
49 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
50 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
51 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
52 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
53 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
54 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
55 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
56 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
57 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
58 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
59 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
60 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
61 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
62 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
63 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
64 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
65 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
66 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
67 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
68 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
69 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
70 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
71 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
72 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
73 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
74 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
75 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
76 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
77 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
78 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
79 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
80 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
81 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
82 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
83 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
84 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
85 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
86 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
87 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
88 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
89 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
90 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
91 Undersampling XGBoost_Undersampling_Median Imputation_fold_1
92 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
93 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
94 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
95 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
96 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
97 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
98 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
99 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
100 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
101 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
102 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
103 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
104 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
105 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
106 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
107 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
108 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
109 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
110 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
111 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
112 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
113 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
114 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
115 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
116 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
117 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
118 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
119 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
120 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
121 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
122 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
123 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
124 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
125 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
126 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
127 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
128 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
129 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
130 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
131 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
132 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
133 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
134 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
135 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
136 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
137 Undersampling XGBoost_Undersampling_Median Imputation_fold_2
138 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
139 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
140 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
141 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
142 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
143 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
144 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
145 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
146 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
147 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
148 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
149 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
150 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
151 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
152 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
153 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
154 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
155 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
156 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
157 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
158 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
159 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
160 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
161 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
162 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
163 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
164 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
165 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
166 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
167 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
168 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
169 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
170 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
171 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
172 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
173 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
174 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
175 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
176 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
177 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
178 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
179 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
180 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
181 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
182 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
183 Undersampling XGBoost_Undersampling_Median Imputation_fold_3
184 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
185 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
186 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
187 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
188 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
189 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
190 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
191 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
192 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
193 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
194 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
195 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
196 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
197 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
198 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
199 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
200 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
201 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
202 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
203 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
204 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
205 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
206 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
207 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
208 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
209 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
210 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
211 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
212 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
213 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
214 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
215 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
216 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
217 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
218 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
219 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
220 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
221 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
222 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
223 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
224 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
225 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
226 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
227 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
228 Undersampling XGBoost_Undersampling_Median Imputation_fold_4
229 Undersampling XGBoost_Undersampling_Median Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 XGBoost 0.880169 NaN Zero Imputation
1 XGBoost 0.131757 NaN Zero Imputation
2 XGBoost 0.164557 NaN Zero Imputation
3 XGBoost 0.146341 NaN Zero Imputation
4 XGBoost 0.683441 NaN Zero Imputation
5 XGBoost 0.301803 NaN Zero Imputation
6 XGBoost 0.366882 NaN Zero Imputation
7 XGBoost NaN binary:logistic Zero Imputation
8 XGBoost NaN None Zero Imputation
9 XGBoost NaN None Zero Imputation
10 XGBoost NaN None Zero Imputation
11 XGBoost NaN None Zero Imputation
12 XGBoost NaN None Zero Imputation
13 XGBoost NaN None Zero Imputation
14 XGBoost NaN None Zero Imputation
15 XGBoost NaN None Zero Imputation
16 XGBoost NaN False Zero Imputation
17 XGBoost NaN None Zero Imputation
18 XGBoost NaN None Zero Imputation
19 XGBoost NaN None Zero Imputation
20 XGBoost NaN None Zero Imputation
21 XGBoost NaN None Zero Imputation
22 XGBoost NaN None Zero Imputation
23 XGBoost NaN None Zero Imputation
24 XGBoost NaN None Zero Imputation
25 XGBoost NaN None Zero Imputation
26 XGBoost NaN None Zero Imputation
27 XGBoost NaN None Zero Imputation
28 XGBoost NaN None Zero Imputation
29 XGBoost NaN None Zero Imputation
30 XGBoost NaN None Zero Imputation
31 XGBoost NaN NaN Zero Imputation
32 XGBoost NaN None Zero Imputation
33 XGBoost NaN None Zero Imputation
34 XGBoost NaN None Zero Imputation
35 XGBoost NaN None Zero Imputation
36 XGBoost NaN None Zero Imputation
37 XGBoost NaN None Zero Imputation
38 XGBoost NaN None Zero Imputation
39 XGBoost NaN None Zero Imputation
40 XGBoost NaN None Zero Imputation
41 XGBoost NaN None Zero Imputation
42 XGBoost NaN None Zero Imputation
43 XGBoost NaN None Zero Imputation
44 XGBoost NaN None Zero Imputation
45 XGBoost NaN None Zero Imputation
46 XGBoost 0.894390 NaN Zero Imputation
47 XGBoost 0.114007 NaN Zero Imputation
48 XGBoost 0.213415 NaN Zero Imputation
49 XGBoost 0.148620 NaN Zero Imputation
50 XGBoost 0.706000 NaN Zero Imputation
51 XGBoost 0.345082 NaN Zero Imputation
52 XGBoost 0.411999 NaN Zero Imputation
53 XGBoost NaN binary:logistic Zero Imputation
54 XGBoost NaN None Zero Imputation
55 XGBoost NaN None Zero Imputation
56 XGBoost NaN None Zero Imputation
57 XGBoost NaN None Zero Imputation
58 XGBoost NaN None Zero Imputation
59 XGBoost NaN None Zero Imputation
60 XGBoost NaN None Zero Imputation
61 XGBoost NaN None Zero Imputation
62 XGBoost NaN False Zero Imputation
63 XGBoost NaN None Zero Imputation
64 XGBoost NaN None Zero Imputation
65 XGBoost NaN None Zero Imputation
66 XGBoost NaN None Zero Imputation
67 XGBoost NaN None Zero Imputation
68 XGBoost NaN None Zero Imputation
69 XGBoost NaN None Zero Imputation
70 XGBoost NaN None Zero Imputation
71 XGBoost NaN None Zero Imputation
72 XGBoost NaN None Zero Imputation
73 XGBoost NaN None Zero Imputation
74 XGBoost NaN None Zero Imputation
75 XGBoost NaN None Zero Imputation
76 XGBoost NaN None Zero Imputation
77 XGBoost NaN NaN Zero Imputation
78 XGBoost NaN None Zero Imputation
79 XGBoost NaN None Zero Imputation
80 XGBoost NaN None Zero Imputation
81 XGBoost NaN None Zero Imputation
82 XGBoost NaN None Zero Imputation
83 XGBoost NaN None Zero Imputation
84 XGBoost NaN None Zero Imputation
85 XGBoost NaN None Zero Imputation
86 XGBoost NaN None Zero Imputation
87 XGBoost NaN None Zero Imputation
88 XGBoost NaN None Zero Imputation
89 XGBoost NaN None Zero Imputation
90 XGBoost NaN None Zero Imputation
91 XGBoost NaN None Zero Imputation
92 XGBoost 0.891757 NaN Zero Imputation
93 XGBoost 0.119048 NaN Zero Imputation
94 XGBoost 0.187166 NaN Zero Imputation
95 XGBoost 0.145530 NaN Zero Imputation
96 XGBoost 0.673546 NaN Zero Imputation
97 XGBoost 0.274377 NaN Zero Imputation
98 XGBoost 0.347091 NaN Zero Imputation
99 XGBoost NaN binary:logistic Zero Imputation
100 XGBoost NaN None Zero Imputation
101 XGBoost NaN None Zero Imputation
102 XGBoost NaN None Zero Imputation
103 XGBoost NaN None Zero Imputation
104 XGBoost NaN None Zero Imputation
105 XGBoost NaN None Zero Imputation
106 XGBoost NaN None Zero Imputation
107 XGBoost NaN None Zero Imputation
108 XGBoost NaN False Zero Imputation
109 XGBoost NaN None Zero Imputation
110 XGBoost NaN None Zero Imputation
111 XGBoost NaN None Zero Imputation
112 XGBoost NaN None Zero Imputation
113 XGBoost NaN None Zero Imputation
114 XGBoost NaN None Zero Imputation
115 XGBoost NaN None Zero Imputation
116 XGBoost NaN None Zero Imputation
117 XGBoost NaN None Zero Imputation
118 XGBoost NaN None Zero Imputation
119 XGBoost NaN None Zero Imputation
120 XGBoost NaN None Zero Imputation
121 XGBoost NaN None Zero Imputation
122 XGBoost NaN None Zero Imputation
123 XGBoost NaN NaN Zero Imputation
124 XGBoost NaN None Zero Imputation
125 XGBoost NaN None Zero Imputation
126 XGBoost NaN None Zero Imputation
127 XGBoost NaN None Zero Imputation
128 XGBoost NaN None Zero Imputation
129 XGBoost NaN None Zero Imputation
130 XGBoost NaN None Zero Imputation
131 XGBoost NaN None Zero Imputation
132 XGBoost NaN None Zero Imputation
133 XGBoost NaN None Zero Imputation
134 XGBoost NaN None Zero Imputation
135 XGBoost NaN None Zero Imputation
136 XGBoost NaN None Zero Imputation
137 XGBoost NaN None Zero Imputation
138 XGBoost 0.891992 NaN Zero Imputation
139 XGBoost 0.133562 NaN Zero Imputation
140 XGBoost 0.198980 NaN Zero Imputation
141 XGBoost 0.159836 NaN Zero Imputation
142 XGBoost 0.654897 NaN Zero Imputation
143 XGBoost 0.218271 NaN Zero Imputation
144 XGBoost 0.309795 NaN Zero Imputation
145 XGBoost NaN binary:logistic Zero Imputation
146 XGBoost NaN None Zero Imputation
147 XGBoost NaN None Zero Imputation
148 XGBoost NaN None Zero Imputation
149 XGBoost NaN None Zero Imputation
150 XGBoost NaN None Zero Imputation
151 XGBoost NaN None Zero Imputation
152 XGBoost NaN None Zero Imputation
153 XGBoost NaN None Zero Imputation
154 XGBoost NaN False Zero Imputation
155 XGBoost NaN None Zero Imputation
156 XGBoost NaN None Zero Imputation
157 XGBoost NaN None Zero Imputation
158 XGBoost NaN None Zero Imputation
159 XGBoost NaN None Zero Imputation
160 XGBoost NaN None Zero Imputation
161 XGBoost NaN None Zero Imputation
162 XGBoost NaN None Zero Imputation
163 XGBoost NaN None Zero Imputation
164 XGBoost NaN None Zero Imputation
165 XGBoost NaN None Zero Imputation
166 XGBoost NaN None Zero Imputation
167 XGBoost NaN None Zero Imputation
168 XGBoost NaN None Zero Imputation
169 XGBoost NaN NaN Zero Imputation
170 XGBoost NaN None Zero Imputation
171 XGBoost NaN None Zero Imputation
172 XGBoost NaN None Zero Imputation
173 XGBoost NaN None Zero Imputation
174 XGBoost NaN None Zero Imputation
175 XGBoost NaN None Zero Imputation
176 XGBoost NaN None Zero Imputation
177 XGBoost NaN None Zero Imputation
178 XGBoost NaN None Zero Imputation
179 XGBoost NaN None Zero Imputation
180 XGBoost NaN None Zero Imputation
181 XGBoost NaN None Zero Imputation
182 XGBoost NaN None Zero Imputation
183 XGBoost NaN None Zero Imputation
184 XGBoost 0.896733 NaN Zero Imputation
185 XGBoost 0.169173 NaN Zero Imputation
186 XGBoost 0.208333 NaN Zero Imputation
187 XGBoost 0.186722 NaN Zero Imputation
188 XGBoost 0.708842 NaN Zero Imputation
189 XGBoost 0.342122 NaN Zero Imputation
190 XGBoost 0.417684 NaN Zero Imputation
191 XGBoost NaN binary:logistic Zero Imputation
192 XGBoost NaN None Zero Imputation
193 XGBoost NaN None Zero Imputation
194 XGBoost NaN None Zero Imputation
195 XGBoost NaN None Zero Imputation
196 XGBoost NaN None Zero Imputation
197 XGBoost NaN None Zero Imputation
198 XGBoost NaN None Zero Imputation
199 XGBoost NaN None Zero Imputation
200 XGBoost NaN False Zero Imputation
201 XGBoost NaN None Zero Imputation
202 XGBoost NaN None Zero Imputation
203 XGBoost NaN None Zero Imputation
204 XGBoost NaN None Zero Imputation
205 XGBoost NaN None Zero Imputation
206 XGBoost NaN None Zero Imputation
207 XGBoost NaN None Zero Imputation
208 XGBoost NaN None Zero Imputation
209 XGBoost NaN None Zero Imputation
210 XGBoost NaN None Zero Imputation
211 XGBoost NaN None Zero Imputation
212 XGBoost NaN None Zero Imputation
213 XGBoost NaN None Zero Imputation
214 XGBoost NaN None Zero Imputation
215 XGBoost NaN NaN Zero Imputation
216 XGBoost NaN None Zero Imputation
217 XGBoost NaN None Zero Imputation
218 XGBoost NaN None Zero Imputation
219 XGBoost NaN None Zero Imputation
220 XGBoost NaN None Zero Imputation
221 XGBoost NaN None Zero Imputation
222 XGBoost NaN None Zero Imputation
223 XGBoost NaN None Zero Imputation
224 XGBoost NaN None Zero Imputation
225 XGBoost NaN None Zero Imputation
226 XGBoost NaN None Zero Imputation
227 XGBoost NaN None Zero Imputation
228 XGBoost NaN None Zero Imputation
229 XGBoost NaN None Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
95 Hybrid Sampling (SMOTEENN)
96 Hybrid Sampling (SMOTEENN)
97 Hybrid Sampling (SMOTEENN)
98 Hybrid Sampling (SMOTEENN)
99 Hybrid Sampling (SMOTEENN)
100 Hybrid Sampling (SMOTEENN)
101 Hybrid Sampling (SMOTEENN)
102 Hybrid Sampling (SMOTEENN)
103 Hybrid Sampling (SMOTEENN)
104 Hybrid Sampling (SMOTEENN)
105 Hybrid Sampling (SMOTEENN)
106 Hybrid Sampling (SMOTEENN)
107 Hybrid Sampling (SMOTEENN)
108 Hybrid Sampling (SMOTEENN)
109 Hybrid Sampling (SMOTEENN)
110 Hybrid Sampling (SMOTEENN)
111 Hybrid Sampling (SMOTEENN)
112 Hybrid Sampling (SMOTEENN)
113 Hybrid Sampling (SMOTEENN)
114 Hybrid Sampling (SMOTEENN)
115 Hybrid Sampling (SMOTEENN)
116 Hybrid Sampling (SMOTEENN)
117 Hybrid Sampling (SMOTEENN)
118 Hybrid Sampling (SMOTEENN)
119 Hybrid Sampling (SMOTEENN)
120 Hybrid Sampling (SMOTEENN)
121 Hybrid Sampling (SMOTEENN)
122 Hybrid Sampling (SMOTEENN)
123 Hybrid Sampling (SMOTEENN)
124 Hybrid Sampling (SMOTEENN)
125 Hybrid Sampling (SMOTEENN)
126 Hybrid Sampling (SMOTEENN)
127 Hybrid Sampling (SMOTEENN)
128 Hybrid Sampling (SMOTEENN)
129 Hybrid Sampling (SMOTEENN)
130 Hybrid Sampling (SMOTEENN)
131 Hybrid Sampling (SMOTEENN)
132 Hybrid Sampling (SMOTEENN)
133 Hybrid Sampling (SMOTEENN)
134 Hybrid Sampling (SMOTEENN)
135 Hybrid Sampling (SMOTEENN)
136 Hybrid Sampling (SMOTEENN)
137 Hybrid Sampling (SMOTEENN)
138 Hybrid Sampling (SMOTEENN)
139 Hybrid Sampling (SMOTEENN)
140 Hybrid Sampling (SMOTEENN)
141 Hybrid Sampling (SMOTEENN)
142 Hybrid Sampling (SMOTEENN)
143 Hybrid Sampling (SMOTEENN)
144 Hybrid Sampling (SMOTEENN)
145 Hybrid Sampling (SMOTEENN)
146 Hybrid Sampling (SMOTEENN)
147 Hybrid Sampling (SMOTEENN)
148 Hybrid Sampling (SMOTEENN)
149 Hybrid Sampling (SMOTEENN)
150 Hybrid Sampling (SMOTEENN)
151 Hybrid Sampling (SMOTEENN)
152 Hybrid Sampling (SMOTEENN)
153 Hybrid Sampling (SMOTEENN)
154 Hybrid Sampling (SMOTEENN)
155 Hybrid Sampling (SMOTEENN)
156 Hybrid Sampling (SMOTEENN)
157 Hybrid Sampling (SMOTEENN)
158 Hybrid Sampling (SMOTEENN)
159 Hybrid Sampling (SMOTEENN)
160 Hybrid Sampling (SMOTEENN)
161 Hybrid Sampling (SMOTEENN)
162 Hybrid Sampling (SMOTEENN)
163 Hybrid Sampling (SMOTEENN)
164 Hybrid Sampling (SMOTEENN)
165 Hybrid Sampling (SMOTEENN)
166 Hybrid Sampling (SMOTEENN)
167 Hybrid Sampling (SMOTEENN)
168 Hybrid Sampling (SMOTEENN)
169 Hybrid Sampling (SMOTEENN)
170 Hybrid Sampling (SMOTEENN)
171 Hybrid Sampling (SMOTEENN)
172 Hybrid Sampling (SMOTEENN)
173 Hybrid Sampling (SMOTEENN)
174 Hybrid Sampling (SMOTEENN)
175 Hybrid Sampling (SMOTEENN)
176 Hybrid Sampling (SMOTEENN)
177 Hybrid Sampling (SMOTEENN)
178 Hybrid Sampling (SMOTEENN)
179 Hybrid Sampling (SMOTEENN)
180 Hybrid Sampling (SMOTEENN)
181 Hybrid Sampling (SMOTEENN)
182 Hybrid Sampling (SMOTEENN)
183 Hybrid Sampling (SMOTEENN)
184 Hybrid Sampling (SMOTEENN)
185 Hybrid Sampling (SMOTEENN)
186 Hybrid Sampling (SMOTEENN)
187 Hybrid Sampling (SMOTEENN)
188 Hybrid Sampling (SMOTEENN)
189 Hybrid Sampling (SMOTEENN)
190 Hybrid Sampling (SMOTEENN)
191 Hybrid Sampling (SMOTEENN)
192 Hybrid Sampling (SMOTEENN)
193 Hybrid Sampling (SMOTEENN)
194 Hybrid Sampling (SMOTEENN)
195 Hybrid Sampling (SMOTEENN)
196 Hybrid Sampling (SMOTEENN)
197 Hybrid Sampling (SMOTEENN)
198 Hybrid Sampling (SMOTEENN)
199 Hybrid Sampling (SMOTEENN)
200 Hybrid Sampling (SMOTEENN)
201 Hybrid Sampling (SMOTEENN)
202 Hybrid Sampling (SMOTEENN)
203 Hybrid Sampling (SMOTEENN)
204 Hybrid Sampling (SMOTEENN)
205 Hybrid Sampling (SMOTEENN)
206 Hybrid Sampling (SMOTEENN)
207 Hybrid Sampling (SMOTEENN)
208 Hybrid Sampling (SMOTEENN)
209 Hybrid Sampling (SMOTEENN)
210 Hybrid Sampling (SMOTEENN)
211 Hybrid Sampling (SMOTEENN)
212 Hybrid Sampling (SMOTEENN)
213 Hybrid Sampling (SMOTEENN)
214 Hybrid Sampling (SMOTEENN)
215 Hybrid Sampling (SMOTEENN)
216 Hybrid Sampling (SMOTEENN)
217 Hybrid Sampling (SMOTEENN)
218 Hybrid Sampling (SMOTEENN)
219 Hybrid Sampling (SMOTEENN)
220 Hybrid Sampling (SMOTEENN)
221 Hybrid Sampling (SMOTEENN)
222 Hybrid Sampling (SMOTEENN)
223 Hybrid Sampling (SMOTEENN)
224 Hybrid Sampling (SMOTEENN)
225 Hybrid Sampling (SMOTEENN)
226 Hybrid Sampling (SMOTEENN)
227 Hybrid Sampling (SMOTEENN)
228 Hybrid Sampling (SMOTEENN)
229 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
1 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
2 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
3 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
4 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
5 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
6 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
7 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
8 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
9 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
10 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
11 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
12 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
13 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
14 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
15 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
16 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
17 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
18 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
19 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
20 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
21 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
22 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
23 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
24 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
25 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
26 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
27 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
28 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
29 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
30 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
31 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
32 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
33 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
34 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
35 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
36 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
37 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
38 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
39 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
40 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
41 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
42 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
43 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
44 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
45 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
46 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
47 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
48 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
49 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
50 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
51 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
52 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
53 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
54 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
55 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
56 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
57 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
58 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
59 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
60 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
61 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
62 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
63 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
64 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
65 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
66 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
67 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
68 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
69 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
70 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
71 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
72 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
73 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
74 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
75 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
76 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
77 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
78 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
79 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
80 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
81 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
82 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
83 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
84 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
85 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
86 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
87 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
88 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
89 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
90 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
91 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
92 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
93 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
94 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
95 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
96 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
97 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
98 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
99 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
100 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
101 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
102 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
103 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
104 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
105 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
106 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
107 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
108 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
109 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
110 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
111 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
112 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
113 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
114 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
115 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
116 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
117 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
118 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
119 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
120 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
121 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
122 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
123 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
124 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
125 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
126 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
127 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
128 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
129 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
130 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
131 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
132 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
133 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
134 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
135 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
136 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
137 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
138 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
139 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
140 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
141 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
142 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
143 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
144 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
145 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
146 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
147 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
148 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
149 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
150 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
151 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
152 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
153 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
154 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
155 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
156 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
157 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
158 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
159 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
160 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
161 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
162 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
163 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
164 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
165 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
166 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
167 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
168 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
169 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
170 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
171 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
172 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
173 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
174 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
175 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
176 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
177 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
178 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
179 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
180 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
181 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
182 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
183 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
184 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
185 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
186 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
187 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
188 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
189 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
190 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
191 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
192 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
193 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
194 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
195 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
196 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
197 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
198 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
199 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
200 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
201 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
202 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
203 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
204 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
205 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
206 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
207 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
208 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
209 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
210 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
211 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
212 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
213 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
214 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
215 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
216 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
217 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
218 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
219 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
220 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
221 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
222 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
223 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
224 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
225 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
226 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
227 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
228 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa...
229 XGBoost_Hybrid Sampling (SMOTEENN)_Zero Imputa... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 XGBoost 0.894917 NaN Mean Imputation
1 XGBoost 0.141593 NaN Mean Imputation
2 XGBoost 0.135021 NaN Mean Imputation
3 XGBoost 0.138229 NaN Mean Imputation
4 XGBoost 0.673920 NaN Mean Imputation
5 XGBoost 0.260803 NaN Mean Imputation
6 XGBoost 0.347841 NaN Mean Imputation
7 XGBoost NaN binary:logistic Mean Imputation
8 XGBoost NaN None Mean Imputation
9 XGBoost NaN None Mean Imputation
10 XGBoost NaN None Mean Imputation
11 XGBoost NaN None Mean Imputation
12 XGBoost NaN None Mean Imputation
13 XGBoost NaN None Mean Imputation
14 XGBoost NaN None Mean Imputation
15 XGBoost NaN None Mean Imputation
16 XGBoost NaN False Mean Imputation
17 XGBoost NaN None Mean Imputation
18 XGBoost NaN None Mean Imputation
19 XGBoost NaN None Mean Imputation
20 XGBoost NaN None Mean Imputation
21 XGBoost NaN None Mean Imputation
22 XGBoost NaN None Mean Imputation
23 XGBoost NaN None Mean Imputation
24 XGBoost NaN None Mean Imputation
25 XGBoost NaN None Mean Imputation
26 XGBoost NaN None Mean Imputation
27 XGBoost NaN None Mean Imputation
28 XGBoost NaN None Mean Imputation
29 XGBoost NaN None Mean Imputation
30 XGBoost NaN None Mean Imputation
31 XGBoost NaN NaN Mean Imputation
32 XGBoost NaN None Mean Imputation
33 XGBoost NaN None Mean Imputation
34 XGBoost NaN None Mean Imputation
35 XGBoost NaN None Mean Imputation
36 XGBoost NaN None Mean Imputation
37 XGBoost NaN None Mean Imputation
38 XGBoost NaN None Mean Imputation
39 XGBoost NaN None Mean Imputation
40 XGBoost NaN None Mean Imputation
41 XGBoost NaN None Mean Imputation
42 XGBoost NaN None Mean Imputation
43 XGBoost NaN None Mean Imputation
44 XGBoost NaN None Mean Imputation
45 XGBoost NaN None Mean Imputation
46 XGBoost 0.902291 NaN Mean Imputation
47 XGBoost 0.129032 NaN Mean Imputation
48 XGBoost 0.219512 NaN Mean Imputation
49 XGBoost 0.162528 NaN Mean Imputation
50 XGBoost 0.680034 NaN Mean Imputation
51 XGBoost 0.310942 NaN Mean Imputation
52 XGBoost 0.360068 NaN Mean Imputation
53 XGBoost NaN binary:logistic Mean Imputation
54 XGBoost NaN None Mean Imputation
55 XGBoost NaN None Mean Imputation
56 XGBoost NaN None Mean Imputation
57 XGBoost NaN None Mean Imputation
58 XGBoost NaN None Mean Imputation
59 XGBoost NaN None Mean Imputation
60 XGBoost NaN None Mean Imputation
61 XGBoost NaN None Mean Imputation
62 XGBoost NaN False Mean Imputation
63 XGBoost NaN None Mean Imputation
64 XGBoost NaN None Mean Imputation
65 XGBoost NaN None Mean Imputation
66 XGBoost NaN None Mean Imputation
67 XGBoost NaN None Mean Imputation
68 XGBoost NaN None Mean Imputation
69 XGBoost NaN None Mean Imputation
70 XGBoost NaN None Mean Imputation
71 XGBoost NaN None Mean Imputation
72 XGBoost NaN None Mean Imputation
73 XGBoost NaN None Mean Imputation
74 XGBoost NaN None Mean Imputation
75 XGBoost NaN None Mean Imputation
76 XGBoost NaN None Mean Imputation
77 XGBoost NaN NaN Mean Imputation
78 XGBoost NaN None Mean Imputation
79 XGBoost NaN None Mean Imputation
80 XGBoost NaN None Mean Imputation
81 XGBoost NaN None Mean Imputation
82 XGBoost NaN None Mean Imputation
83 XGBoost NaN None Mean Imputation
84 XGBoost NaN None Mean Imputation
85 XGBoost NaN None Mean Imputation
86 XGBoost NaN None Mean Imputation
87 XGBoost NaN None Mean Imputation
88 XGBoost NaN None Mean Imputation
89 XGBoost NaN None Mean Imputation
90 XGBoost NaN None Mean Imputation
91 XGBoost NaN None Mean Imputation
92 XGBoost 0.886226 NaN Mean Imputation
93 XGBoost 0.103560 NaN Mean Imputation
94 XGBoost 0.171123 NaN Mean Imputation
95 XGBoost 0.129032 NaN Mean Imputation
96 XGBoost 0.657033 NaN Mean Imputation
97 XGBoost 0.252343 NaN Mean Imputation
98 XGBoost 0.314065 NaN Mean Imputation
99 XGBoost NaN binary:logistic Mean Imputation
100 XGBoost NaN None Mean Imputation
101 XGBoost NaN None Mean Imputation
102 XGBoost NaN None Mean Imputation
103 XGBoost NaN None Mean Imputation
104 XGBoost NaN None Mean Imputation
105 XGBoost NaN None Mean Imputation
106 XGBoost NaN None Mean Imputation
107 XGBoost NaN None Mean Imputation
108 XGBoost NaN False Mean Imputation
109 XGBoost NaN None Mean Imputation
110 XGBoost NaN None Mean Imputation
111 XGBoost NaN None Mean Imputation
112 XGBoost NaN None Mean Imputation
113 XGBoost NaN None Mean Imputation
114 XGBoost NaN None Mean Imputation
115 XGBoost NaN None Mean Imputation
116 XGBoost NaN None Mean Imputation
117 XGBoost NaN None Mean Imputation
118 XGBoost NaN None Mean Imputation
119 XGBoost NaN None Mean Imputation
120 XGBoost NaN None Mean Imputation
121 XGBoost NaN None Mean Imputation
122 XGBoost NaN None Mean Imputation
123 XGBoost NaN NaN Mean Imputation
124 XGBoost NaN None Mean Imputation
125 XGBoost NaN None Mean Imputation
126 XGBoost NaN None Mean Imputation
127 XGBoost NaN None Mean Imputation
128 XGBoost NaN None Mean Imputation
129 XGBoost NaN None Mean Imputation
130 XGBoost NaN None Mean Imputation
131 XGBoost NaN None Mean Imputation
132 XGBoost NaN None Mean Imputation
133 XGBoost NaN None Mean Imputation
134 XGBoost NaN None Mean Imputation
135 XGBoost NaN None Mean Imputation
136 XGBoost NaN None Mean Imputation
137 XGBoost NaN None Mean Imputation
138 XGBoost 0.893836 NaN Mean Imputation
139 XGBoost 0.129032 NaN Mean Imputation
140 XGBoost 0.183673 NaN Mean Imputation
141 XGBoost 0.151579 NaN Mean Imputation
142 XGBoost 0.666504 NaN Mean Imputation
143 XGBoost 0.288906 NaN Mean Imputation
144 XGBoost 0.333007 NaN Mean Imputation
145 XGBoost NaN binary:logistic Mean Imputation
146 XGBoost NaN None Mean Imputation
147 XGBoost NaN None Mean Imputation
148 XGBoost NaN None Mean Imputation
149 XGBoost NaN None Mean Imputation
150 XGBoost NaN None Mean Imputation
151 XGBoost NaN None Mean Imputation
152 XGBoost NaN None Mean Imputation
153 XGBoost NaN None Mean Imputation
154 XGBoost NaN False Mean Imputation
155 XGBoost NaN None Mean Imputation
156 XGBoost NaN None Mean Imputation
157 XGBoost NaN None Mean Imputation
158 XGBoost NaN None Mean Imputation
159 XGBoost NaN None Mean Imputation
160 XGBoost NaN None Mean Imputation
161 XGBoost NaN None Mean Imputation
162 XGBoost NaN None Mean Imputation
163 XGBoost NaN None Mean Imputation
164 XGBoost NaN None Mean Imputation
165 XGBoost NaN None Mean Imputation
166 XGBoost NaN None Mean Imputation
167 XGBoost NaN None Mean Imputation
168 XGBoost NaN None Mean Imputation
169 XGBoost NaN NaN Mean Imputation
170 XGBoost NaN None Mean Imputation
171 XGBoost NaN None Mean Imputation
172 XGBoost NaN None Mean Imputation
173 XGBoost NaN None Mean Imputation
174 XGBoost NaN None Mean Imputation
175 XGBoost NaN None Mean Imputation
176 XGBoost NaN None Mean Imputation
177 XGBoost NaN None Mean Imputation
178 XGBoost NaN None Mean Imputation
179 XGBoost NaN None Mean Imputation
180 XGBoost NaN None Mean Imputation
181 XGBoost NaN None Mean Imputation
182 XGBoost NaN None Mean Imputation
183 XGBoost NaN None Mean Imputation
184 XGBoost 0.892518 NaN Mean Imputation
185 XGBoost 0.139098 NaN Mean Imputation
186 XGBoost 0.171296 NaN Mean Imputation
187 XGBoost 0.153527 NaN Mean Imputation
188 XGBoost 0.664519 NaN Mean Imputation
189 XGBoost 0.246250 NaN Mean Imputation
190 XGBoost 0.329037 NaN Mean Imputation
191 XGBoost NaN binary:logistic Mean Imputation
192 XGBoost NaN None Mean Imputation
193 XGBoost NaN None Mean Imputation
194 XGBoost NaN None Mean Imputation
195 XGBoost NaN None Mean Imputation
196 XGBoost NaN None Mean Imputation
197 XGBoost NaN None Mean Imputation
198 XGBoost NaN None Mean Imputation
199 XGBoost NaN None Mean Imputation
200 XGBoost NaN False Mean Imputation
201 XGBoost NaN None Mean Imputation
202 XGBoost NaN None Mean Imputation
203 XGBoost NaN None Mean Imputation
204 XGBoost NaN None Mean Imputation
205 XGBoost NaN None Mean Imputation
206 XGBoost NaN None Mean Imputation
207 XGBoost NaN None Mean Imputation
208 XGBoost NaN None Mean Imputation
209 XGBoost NaN None Mean Imputation
210 XGBoost NaN None Mean Imputation
211 XGBoost NaN None Mean Imputation
212 XGBoost NaN None Mean Imputation
213 XGBoost NaN None Mean Imputation
214 XGBoost NaN None Mean Imputation
215 XGBoost NaN NaN Mean Imputation
216 XGBoost NaN None Mean Imputation
217 XGBoost NaN None Mean Imputation
218 XGBoost NaN None Mean Imputation
219 XGBoost NaN None Mean Imputation
220 XGBoost NaN None Mean Imputation
221 XGBoost NaN None Mean Imputation
222 XGBoost NaN None Mean Imputation
223 XGBoost NaN None Mean Imputation
224 XGBoost NaN None Mean Imputation
225 XGBoost NaN None Mean Imputation
226 XGBoost NaN None Mean Imputation
227 XGBoost NaN None Mean Imputation
228 XGBoost NaN None Mean Imputation
229 XGBoost NaN None Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
95 Hybrid Sampling (SMOTEENN)
96 Hybrid Sampling (SMOTEENN)
97 Hybrid Sampling (SMOTEENN)
98 Hybrid Sampling (SMOTEENN)
99 Hybrid Sampling (SMOTEENN)
100 Hybrid Sampling (SMOTEENN)
101 Hybrid Sampling (SMOTEENN)
102 Hybrid Sampling (SMOTEENN)
103 Hybrid Sampling (SMOTEENN)
104 Hybrid Sampling (SMOTEENN)
105 Hybrid Sampling (SMOTEENN)
106 Hybrid Sampling (SMOTEENN)
107 Hybrid Sampling (SMOTEENN)
108 Hybrid Sampling (SMOTEENN)
109 Hybrid Sampling (SMOTEENN)
110 Hybrid Sampling (SMOTEENN)
111 Hybrid Sampling (SMOTEENN)
112 Hybrid Sampling (SMOTEENN)
113 Hybrid Sampling (SMOTEENN)
114 Hybrid Sampling (SMOTEENN)
115 Hybrid Sampling (SMOTEENN)
116 Hybrid Sampling (SMOTEENN)
117 Hybrid Sampling (SMOTEENN)
118 Hybrid Sampling (SMOTEENN)
119 Hybrid Sampling (SMOTEENN)
120 Hybrid Sampling (SMOTEENN)
121 Hybrid Sampling (SMOTEENN)
122 Hybrid Sampling (SMOTEENN)
123 Hybrid Sampling (SMOTEENN)
124 Hybrid Sampling (SMOTEENN)
125 Hybrid Sampling (SMOTEENN)
126 Hybrid Sampling (SMOTEENN)
127 Hybrid Sampling (SMOTEENN)
128 Hybrid Sampling (SMOTEENN)
129 Hybrid Sampling (SMOTEENN)
130 Hybrid Sampling (SMOTEENN)
131 Hybrid Sampling (SMOTEENN)
132 Hybrid Sampling (SMOTEENN)
133 Hybrid Sampling (SMOTEENN)
134 Hybrid Sampling (SMOTEENN)
135 Hybrid Sampling (SMOTEENN)
136 Hybrid Sampling (SMOTEENN)
137 Hybrid Sampling (SMOTEENN)
138 Hybrid Sampling (SMOTEENN)
139 Hybrid Sampling (SMOTEENN)
140 Hybrid Sampling (SMOTEENN)
141 Hybrid Sampling (SMOTEENN)
142 Hybrid Sampling (SMOTEENN)
143 Hybrid Sampling (SMOTEENN)
144 Hybrid Sampling (SMOTEENN)
145 Hybrid Sampling (SMOTEENN)
146 Hybrid Sampling (SMOTEENN)
147 Hybrid Sampling (SMOTEENN)
148 Hybrid Sampling (SMOTEENN)
149 Hybrid Sampling (SMOTEENN)
150 Hybrid Sampling (SMOTEENN)
151 Hybrid Sampling (SMOTEENN)
152 Hybrid Sampling (SMOTEENN)
153 Hybrid Sampling (SMOTEENN)
154 Hybrid Sampling (SMOTEENN)
155 Hybrid Sampling (SMOTEENN)
156 Hybrid Sampling (SMOTEENN)
157 Hybrid Sampling (SMOTEENN)
158 Hybrid Sampling (SMOTEENN)
159 Hybrid Sampling (SMOTEENN)
160 Hybrid Sampling (SMOTEENN)
161 Hybrid Sampling (SMOTEENN)
162 Hybrid Sampling (SMOTEENN)
163 Hybrid Sampling (SMOTEENN)
164 Hybrid Sampling (SMOTEENN)
165 Hybrid Sampling (SMOTEENN)
166 Hybrid Sampling (SMOTEENN)
167 Hybrid Sampling (SMOTEENN)
168 Hybrid Sampling (SMOTEENN)
169 Hybrid Sampling (SMOTEENN)
170 Hybrid Sampling (SMOTEENN)
171 Hybrid Sampling (SMOTEENN)
172 Hybrid Sampling (SMOTEENN)
173 Hybrid Sampling (SMOTEENN)
174 Hybrid Sampling (SMOTEENN)
175 Hybrid Sampling (SMOTEENN)
176 Hybrid Sampling (SMOTEENN)
177 Hybrid Sampling (SMOTEENN)
178 Hybrid Sampling (SMOTEENN)
179 Hybrid Sampling (SMOTEENN)
180 Hybrid Sampling (SMOTEENN)
181 Hybrid Sampling (SMOTEENN)
182 Hybrid Sampling (SMOTEENN)
183 Hybrid Sampling (SMOTEENN)
184 Hybrid Sampling (SMOTEENN)
185 Hybrid Sampling (SMOTEENN)
186 Hybrid Sampling (SMOTEENN)
187 Hybrid Sampling (SMOTEENN)
188 Hybrid Sampling (SMOTEENN)
189 Hybrid Sampling (SMOTEENN)
190 Hybrid Sampling (SMOTEENN)
191 Hybrid Sampling (SMOTEENN)
192 Hybrid Sampling (SMOTEENN)
193 Hybrid Sampling (SMOTEENN)
194 Hybrid Sampling (SMOTEENN)
195 Hybrid Sampling (SMOTEENN)
196 Hybrid Sampling (SMOTEENN)
197 Hybrid Sampling (SMOTEENN)
198 Hybrid Sampling (SMOTEENN)
199 Hybrid Sampling (SMOTEENN)
200 Hybrid Sampling (SMOTEENN)
201 Hybrid Sampling (SMOTEENN)
202 Hybrid Sampling (SMOTEENN)
203 Hybrid Sampling (SMOTEENN)
204 Hybrid Sampling (SMOTEENN)
205 Hybrid Sampling (SMOTEENN)
206 Hybrid Sampling (SMOTEENN)
207 Hybrid Sampling (SMOTEENN)
208 Hybrid Sampling (SMOTEENN)
209 Hybrid Sampling (SMOTEENN)
210 Hybrid Sampling (SMOTEENN)
211 Hybrid Sampling (SMOTEENN)
212 Hybrid Sampling (SMOTEENN)
213 Hybrid Sampling (SMOTEENN)
214 Hybrid Sampling (SMOTEENN)
215 Hybrid Sampling (SMOTEENN)
216 Hybrid Sampling (SMOTEENN)
217 Hybrid Sampling (SMOTEENN)
218 Hybrid Sampling (SMOTEENN)
219 Hybrid Sampling (SMOTEENN)
220 Hybrid Sampling (SMOTEENN)
221 Hybrid Sampling (SMOTEENN)
222 Hybrid Sampling (SMOTEENN)
223 Hybrid Sampling (SMOTEENN)
224 Hybrid Sampling (SMOTEENN)
225 Hybrid Sampling (SMOTEENN)
226 Hybrid Sampling (SMOTEENN)
227 Hybrid Sampling (SMOTEENN)
228 Hybrid Sampling (SMOTEENN)
229 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
1 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
2 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
3 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
4 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
5 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
6 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
7 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
8 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
9 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
10 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
11 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
12 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
13 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
14 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
15 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
16 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
17 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
18 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
19 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
20 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
21 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
22 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
23 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
24 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
25 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
26 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
27 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
28 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
29 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
30 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
31 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
32 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
33 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
34 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
35 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
36 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
37 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
38 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
39 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
40 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
41 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
42 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
43 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
44 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
45 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
46 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
47 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
48 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
49 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
50 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
51 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
52 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
53 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
54 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
55 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
56 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
57 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
58 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
59 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
60 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
61 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
62 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
63 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
64 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
65 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
66 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
67 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
68 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
69 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
70 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
71 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
72 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
73 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
74 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
75 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
76 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
77 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
78 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
79 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
80 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
81 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
82 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
83 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
84 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
85 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
86 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
87 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
88 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
89 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
90 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
91 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
92 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
93 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
94 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
95 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
96 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
97 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
98 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
99 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
100 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
101 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
102 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
103 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
104 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
105 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
106 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
107 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
108 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
109 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
110 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
111 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
112 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
113 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
114 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
115 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
116 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
117 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
118 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
119 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
120 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
121 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
122 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
123 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
124 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
125 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
126 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
127 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
128 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
129 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
130 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
131 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
132 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
133 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
134 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
135 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
136 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
137 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
138 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
139 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
140 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
141 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
142 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
143 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
144 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
145 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
146 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
147 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
148 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
149 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
150 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
151 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
152 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
153 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
154 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
155 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
156 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
157 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
158 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
159 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
160 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
161 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
162 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
163 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
164 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
165 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
166 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
167 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
168 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
169 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
170 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
171 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
172 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
173 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
174 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
175 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
176 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
177 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
178 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
179 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
180 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
181 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
182 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
183 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
184 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
185 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
186 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
187 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
188 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
189 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
190 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
191 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
192 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
193 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
194 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
195 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
196 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
197 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
198 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
199 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
200 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
201 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
202 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
203 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
204 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
205 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
206 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
207 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
208 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
209 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
210 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
211 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
212 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
213 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
214 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
215 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
216 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
217 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
218 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
219 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
220 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
221 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
222 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
223 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
224 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
225 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
226 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
227 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
228 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa...
229 XGBoost_Hybrid Sampling (SMOTEENN)_Mean Imputa... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 XGBoost 0.890703 NaN Median Imputation
1 XGBoost 0.132231 NaN Median Imputation
2 XGBoost 0.135021 NaN Median Imputation
3 XGBoost 0.133612 NaN Median Imputation
4 XGBoost 0.661779 NaN Median Imputation
5 XGBoost 0.253255 NaN Median Imputation
6 XGBoost 0.323558 NaN Median Imputation
7 XGBoost NaN binary:logistic Median Imputation
8 XGBoost NaN None Median Imputation
9 XGBoost NaN None Median Imputation
10 XGBoost NaN None Median Imputation
11 XGBoost NaN None Median Imputation
12 XGBoost NaN None Median Imputation
13 XGBoost NaN None Median Imputation
14 XGBoost NaN None Median Imputation
15 XGBoost NaN None Median Imputation
16 XGBoost NaN False Median Imputation
17 XGBoost NaN None Median Imputation
18 XGBoost NaN None Median Imputation
19 XGBoost NaN None Median Imputation
20 XGBoost NaN None Median Imputation
21 XGBoost NaN None Median Imputation
22 XGBoost NaN None Median Imputation
23 XGBoost NaN None Median Imputation
24 XGBoost NaN None Median Imputation
25 XGBoost NaN None Median Imputation
26 XGBoost NaN None Median Imputation
27 XGBoost NaN None Median Imputation
28 XGBoost NaN None Median Imputation
29 XGBoost NaN None Median Imputation
30 XGBoost NaN None Median Imputation
31 XGBoost NaN NaN Median Imputation
32 XGBoost NaN None Median Imputation
33 XGBoost NaN None Median Imputation
34 XGBoost NaN None Median Imputation
35 XGBoost NaN None Median Imputation
36 XGBoost NaN None Median Imputation
37 XGBoost NaN None Median Imputation
38 XGBoost NaN None Median Imputation
39 XGBoost NaN None Median Imputation
40 XGBoost NaN None Median Imputation
41 XGBoost NaN None Median Imputation
42 XGBoost NaN None Median Imputation
43 XGBoost NaN None Median Imputation
44 XGBoost NaN None Median Imputation
45 XGBoost NaN None Median Imputation
46 XGBoost 0.893337 NaN Median Imputation
47 XGBoost 0.091525 NaN Median Imputation
48 XGBoost 0.164634 NaN Median Imputation
49 XGBoost 0.117647 NaN Median Imputation
50 XGBoost 0.659554 NaN Median Imputation
51 XGBoost 0.268086 NaN Median Imputation
52 XGBoost 0.319107 NaN Median Imputation
53 XGBoost NaN binary:logistic Median Imputation
54 XGBoost NaN None Median Imputation
55 XGBoost NaN None Median Imputation
56 XGBoost NaN None Median Imputation
57 XGBoost NaN None Median Imputation
58 XGBoost NaN None Median Imputation
59 XGBoost NaN None Median Imputation
60 XGBoost NaN None Median Imputation
61 XGBoost NaN None Median Imputation
62 XGBoost NaN False Median Imputation
63 XGBoost NaN None Median Imputation
64 XGBoost NaN None Median Imputation
65 XGBoost NaN None Median Imputation
66 XGBoost NaN None Median Imputation
67 XGBoost NaN None Median Imputation
68 XGBoost NaN None Median Imputation
69 XGBoost NaN None Median Imputation
70 XGBoost NaN None Median Imputation
71 XGBoost NaN None Median Imputation
72 XGBoost NaN None Median Imputation
73 XGBoost NaN None Median Imputation
74 XGBoost NaN None Median Imputation
75 XGBoost NaN None Median Imputation
76 XGBoost NaN None Median Imputation
77 XGBoost NaN NaN Median Imputation
78 XGBoost NaN None Median Imputation
79 XGBoost NaN None Median Imputation
80 XGBoost NaN None Median Imputation
81 XGBoost NaN None Median Imputation
82 XGBoost NaN None Median Imputation
83 XGBoost NaN None Median Imputation
84 XGBoost NaN None Median Imputation
85 XGBoost NaN None Median Imputation
86 XGBoost NaN None Median Imputation
87 XGBoost NaN None Median Imputation
88 XGBoost NaN None Median Imputation
89 XGBoost NaN None Median Imputation
90 XGBoost NaN None Median Imputation
91 XGBoost NaN None Median Imputation
92 XGBoost 0.894390 NaN Median Imputation
93 XGBoost 0.125874 NaN Median Imputation
94 XGBoost 0.192513 NaN Median Imputation
95 XGBoost 0.152220 NaN Median Imputation
96 XGBoost 0.672655 NaN Median Imputation
97 XGBoost 0.255957 NaN Median Imputation
98 XGBoost 0.345311 NaN Median Imputation
99 XGBoost NaN binary:logistic Median Imputation
100 XGBoost NaN None Median Imputation
101 XGBoost NaN None Median Imputation
102 XGBoost NaN None Median Imputation
103 XGBoost NaN None Median Imputation
104 XGBoost NaN None Median Imputation
105 XGBoost NaN None Median Imputation
106 XGBoost NaN None Median Imputation
107 XGBoost NaN None Median Imputation
108 XGBoost NaN False Median Imputation
109 XGBoost NaN None Median Imputation
110 XGBoost NaN None Median Imputation
111 XGBoost NaN None Median Imputation
112 XGBoost NaN None Median Imputation
113 XGBoost NaN None Median Imputation
114 XGBoost NaN None Median Imputation
115 XGBoost NaN None Median Imputation
116 XGBoost NaN None Median Imputation
117 XGBoost NaN None Median Imputation
118 XGBoost NaN None Median Imputation
119 XGBoost NaN None Median Imputation
120 XGBoost NaN None Median Imputation
121 XGBoost NaN None Median Imputation
122 XGBoost NaN None Median Imputation
123 XGBoost NaN NaN Median Imputation
124 XGBoost NaN None Median Imputation
125 XGBoost NaN None Median Imputation
126 XGBoost NaN None Median Imputation
127 XGBoost NaN None Median Imputation
128 XGBoost NaN None Median Imputation
129 XGBoost NaN None Median Imputation
130 XGBoost NaN None Median Imputation
131 XGBoost NaN None Median Imputation
132 XGBoost NaN None Median Imputation
133 XGBoost NaN None Median Imputation
134 XGBoost NaN None Median Imputation
135 XGBoost NaN None Median Imputation
136 XGBoost NaN None Median Imputation
137 XGBoost NaN None Median Imputation
138 XGBoost 0.886459 NaN Median Imputation
139 XGBoost 0.117264 NaN Median Imputation
140 XGBoost 0.183673 NaN Median Imputation
141 XGBoost 0.143141 NaN Median Imputation
142 XGBoost 0.652479 NaN Median Imputation
143 XGBoost 0.254031 NaN Median Imputation
144 XGBoost 0.304959 NaN Median Imputation
145 XGBoost NaN binary:logistic Median Imputation
146 XGBoost NaN None Median Imputation
147 XGBoost NaN None Median Imputation
148 XGBoost NaN None Median Imputation
149 XGBoost NaN None Median Imputation
150 XGBoost NaN None Median Imputation
151 XGBoost NaN None Median Imputation
152 XGBoost NaN None Median Imputation
153 XGBoost NaN None Median Imputation
154 XGBoost NaN False Median Imputation
155 XGBoost NaN None Median Imputation
156 XGBoost NaN None Median Imputation
157 XGBoost NaN None Median Imputation
158 XGBoost NaN None Median Imputation
159 XGBoost NaN None Median Imputation
160 XGBoost NaN None Median Imputation
161 XGBoost NaN None Median Imputation
162 XGBoost NaN None Median Imputation
163 XGBoost NaN None Median Imputation
164 XGBoost NaN None Median Imputation
165 XGBoost NaN None Median Imputation
166 XGBoost NaN None Median Imputation
167 XGBoost NaN None Median Imputation
168 XGBoost NaN None Median Imputation
169 XGBoost NaN NaN Median Imputation
170 XGBoost NaN None Median Imputation
171 XGBoost NaN None Median Imputation
172 XGBoost NaN None Median Imputation
173 XGBoost NaN None Median Imputation
174 XGBoost NaN None Median Imputation
175 XGBoost NaN None Median Imputation
176 XGBoost NaN None Median Imputation
177 XGBoost NaN None Median Imputation
178 XGBoost NaN None Median Imputation
179 XGBoost NaN None Median Imputation
180 XGBoost NaN None Median Imputation
181 XGBoost NaN None Median Imputation
182 XGBoost NaN None Median Imputation
183 XGBoost NaN None Median Imputation
184 XGBoost 0.890938 NaN Median Imputation
185 XGBoost 0.148936 NaN Median Imputation
186 XGBoost 0.194444 NaN Median Imputation
187 XGBoost 0.168675 NaN Median Imputation
188 XGBoost 0.686883 NaN Median Imputation
189 XGBoost 0.292075 NaN Median Imputation
190 XGBoost 0.373766 NaN Median Imputation
191 XGBoost NaN binary:logistic Median Imputation
192 XGBoost NaN None Median Imputation
193 XGBoost NaN None Median Imputation
194 XGBoost NaN None Median Imputation
195 XGBoost NaN None Median Imputation
196 XGBoost NaN None Median Imputation
197 XGBoost NaN None Median Imputation
198 XGBoost NaN None Median Imputation
199 XGBoost NaN None Median Imputation
200 XGBoost NaN False Median Imputation
201 XGBoost NaN None Median Imputation
202 XGBoost NaN None Median Imputation
203 XGBoost NaN None Median Imputation
204 XGBoost NaN None Median Imputation
205 XGBoost NaN None Median Imputation
206 XGBoost NaN None Median Imputation
207 XGBoost NaN None Median Imputation
208 XGBoost NaN None Median Imputation
209 XGBoost NaN None Median Imputation
210 XGBoost NaN None Median Imputation
211 XGBoost NaN None Median Imputation
212 XGBoost NaN None Median Imputation
213 XGBoost NaN None Median Imputation
214 XGBoost NaN None Median Imputation
215 XGBoost NaN NaN Median Imputation
216 XGBoost NaN None Median Imputation
217 XGBoost NaN None Median Imputation
218 XGBoost NaN None Median Imputation
219 XGBoost NaN None Median Imputation
220 XGBoost NaN None Median Imputation
221 XGBoost NaN None Median Imputation
222 XGBoost NaN None Median Imputation
223 XGBoost NaN None Median Imputation
224 XGBoost NaN None Median Imputation
225 XGBoost NaN None Median Imputation
226 XGBoost NaN None Median Imputation
227 XGBoost NaN None Median Imputation
228 XGBoost NaN None Median Imputation
229 XGBoost NaN None Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
95 Hybrid Sampling (SMOTEENN)
96 Hybrid Sampling (SMOTEENN)
97 Hybrid Sampling (SMOTEENN)
98 Hybrid Sampling (SMOTEENN)
99 Hybrid Sampling (SMOTEENN)
100 Hybrid Sampling (SMOTEENN)
101 Hybrid Sampling (SMOTEENN)
102 Hybrid Sampling (SMOTEENN)
103 Hybrid Sampling (SMOTEENN)
104 Hybrid Sampling (SMOTEENN)
105 Hybrid Sampling (SMOTEENN)
106 Hybrid Sampling (SMOTEENN)
107 Hybrid Sampling (SMOTEENN)
108 Hybrid Sampling (SMOTEENN)
109 Hybrid Sampling (SMOTEENN)
110 Hybrid Sampling (SMOTEENN)
111 Hybrid Sampling (SMOTEENN)
112 Hybrid Sampling (SMOTEENN)
113 Hybrid Sampling (SMOTEENN)
114 Hybrid Sampling (SMOTEENN)
115 Hybrid Sampling (SMOTEENN)
116 Hybrid Sampling (SMOTEENN)
117 Hybrid Sampling (SMOTEENN)
118 Hybrid Sampling (SMOTEENN)
119 Hybrid Sampling (SMOTEENN)
120 Hybrid Sampling (SMOTEENN)
121 Hybrid Sampling (SMOTEENN)
122 Hybrid Sampling (SMOTEENN)
123 Hybrid Sampling (SMOTEENN)
124 Hybrid Sampling (SMOTEENN)
125 Hybrid Sampling (SMOTEENN)
126 Hybrid Sampling (SMOTEENN)
127 Hybrid Sampling (SMOTEENN)
128 Hybrid Sampling (SMOTEENN)
129 Hybrid Sampling (SMOTEENN)
130 Hybrid Sampling (SMOTEENN)
131 Hybrid Sampling (SMOTEENN)
132 Hybrid Sampling (SMOTEENN)
133 Hybrid Sampling (SMOTEENN)
134 Hybrid Sampling (SMOTEENN)
135 Hybrid Sampling (SMOTEENN)
136 Hybrid Sampling (SMOTEENN)
137 Hybrid Sampling (SMOTEENN)
138 Hybrid Sampling (SMOTEENN)
139 Hybrid Sampling (SMOTEENN)
140 Hybrid Sampling (SMOTEENN)
141 Hybrid Sampling (SMOTEENN)
142 Hybrid Sampling (SMOTEENN)
143 Hybrid Sampling (SMOTEENN)
144 Hybrid Sampling (SMOTEENN)
145 Hybrid Sampling (SMOTEENN)
146 Hybrid Sampling (SMOTEENN)
147 Hybrid Sampling (SMOTEENN)
148 Hybrid Sampling (SMOTEENN)
149 Hybrid Sampling (SMOTEENN)
150 Hybrid Sampling (SMOTEENN)
151 Hybrid Sampling (SMOTEENN)
152 Hybrid Sampling (SMOTEENN)
153 Hybrid Sampling (SMOTEENN)
154 Hybrid Sampling (SMOTEENN)
155 Hybrid Sampling (SMOTEENN)
156 Hybrid Sampling (SMOTEENN)
157 Hybrid Sampling (SMOTEENN)
158 Hybrid Sampling (SMOTEENN)
159 Hybrid Sampling (SMOTEENN)
160 Hybrid Sampling (SMOTEENN)
161 Hybrid Sampling (SMOTEENN)
162 Hybrid Sampling (SMOTEENN)
163 Hybrid Sampling (SMOTEENN)
164 Hybrid Sampling (SMOTEENN)
165 Hybrid Sampling (SMOTEENN)
166 Hybrid Sampling (SMOTEENN)
167 Hybrid Sampling (SMOTEENN)
168 Hybrid Sampling (SMOTEENN)
169 Hybrid Sampling (SMOTEENN)
170 Hybrid Sampling (SMOTEENN)
171 Hybrid Sampling (SMOTEENN)
172 Hybrid Sampling (SMOTEENN)
173 Hybrid Sampling (SMOTEENN)
174 Hybrid Sampling (SMOTEENN)
175 Hybrid Sampling (SMOTEENN)
176 Hybrid Sampling (SMOTEENN)
177 Hybrid Sampling (SMOTEENN)
178 Hybrid Sampling (SMOTEENN)
179 Hybrid Sampling (SMOTEENN)
180 Hybrid Sampling (SMOTEENN)
181 Hybrid Sampling (SMOTEENN)
182 Hybrid Sampling (SMOTEENN)
183 Hybrid Sampling (SMOTEENN)
184 Hybrid Sampling (SMOTEENN)
185 Hybrid Sampling (SMOTEENN)
186 Hybrid Sampling (SMOTEENN)
187 Hybrid Sampling (SMOTEENN)
188 Hybrid Sampling (SMOTEENN)
189 Hybrid Sampling (SMOTEENN)
190 Hybrid Sampling (SMOTEENN)
191 Hybrid Sampling (SMOTEENN)
192 Hybrid Sampling (SMOTEENN)
193 Hybrid Sampling (SMOTEENN)
194 Hybrid Sampling (SMOTEENN)
195 Hybrid Sampling (SMOTEENN)
196 Hybrid Sampling (SMOTEENN)
197 Hybrid Sampling (SMOTEENN)
198 Hybrid Sampling (SMOTEENN)
199 Hybrid Sampling (SMOTEENN)
200 Hybrid Sampling (SMOTEENN)
201 Hybrid Sampling (SMOTEENN)
202 Hybrid Sampling (SMOTEENN)
203 Hybrid Sampling (SMOTEENN)
204 Hybrid Sampling (SMOTEENN)
205 Hybrid Sampling (SMOTEENN)
206 Hybrid Sampling (SMOTEENN)
207 Hybrid Sampling (SMOTEENN)
208 Hybrid Sampling (SMOTEENN)
209 Hybrid Sampling (SMOTEENN)
210 Hybrid Sampling (SMOTEENN)
211 Hybrid Sampling (SMOTEENN)
212 Hybrid Sampling (SMOTEENN)
213 Hybrid Sampling (SMOTEENN)
214 Hybrid Sampling (SMOTEENN)
215 Hybrid Sampling (SMOTEENN)
216 Hybrid Sampling (SMOTEENN)
217 Hybrid Sampling (SMOTEENN)
218 Hybrid Sampling (SMOTEENN)
219 Hybrid Sampling (SMOTEENN)
220 Hybrid Sampling (SMOTEENN)
221 Hybrid Sampling (SMOTEENN)
222 Hybrid Sampling (SMOTEENN)
223 Hybrid Sampling (SMOTEENN)
224 Hybrid Sampling (SMOTEENN)
225 Hybrid Sampling (SMOTEENN)
226 Hybrid Sampling (SMOTEENN)
227 Hybrid Sampling (SMOTEENN)
228 Hybrid Sampling (SMOTEENN)
229 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
1 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
2 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
3 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
4 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
5 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
6 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
7 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
8 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
9 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
10 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
11 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
12 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
13 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
14 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
15 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
16 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
17 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
18 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
19 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
20 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
21 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
22 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
23 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
24 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
25 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
26 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
27 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
28 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
29 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
30 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
31 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
32 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
33 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
34 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
35 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
36 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
37 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
38 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
39 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
40 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
41 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
42 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
43 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
44 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
45 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
46 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
47 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
48 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
49 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
50 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
51 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
52 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
53 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
54 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
55 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
56 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
57 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
58 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
59 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
60 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
61 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
62 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
63 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
64 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
65 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
66 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
67 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
68 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
69 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
70 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
71 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
72 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
73 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
74 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
75 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
76 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
77 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
78 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
79 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
80 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
81 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
82 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
83 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
84 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
85 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
86 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
87 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
88 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
89 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
90 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
91 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
92 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
93 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
94 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
95 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
96 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
97 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
98 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
99 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
100 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
101 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
102 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
103 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
104 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
105 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
106 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
107 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
108 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
109 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
110 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
111 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
112 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
113 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
114 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
115 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
116 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
117 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
118 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
119 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
120 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
121 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
122 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
123 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
124 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
125 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
126 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
127 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
128 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
129 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
130 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
131 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
132 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
133 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
134 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
135 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
136 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
137 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
138 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
139 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
140 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
141 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
142 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
143 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
144 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
145 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
146 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
147 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
148 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
149 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
150 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
151 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
152 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
153 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
154 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
155 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
156 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
157 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
158 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
159 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
160 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
161 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
162 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
163 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
164 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
165 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
166 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
167 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
168 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
169 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
170 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
171 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
172 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
173 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
174 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
175 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
176 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
177 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
178 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
179 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
180 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
181 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
182 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
183 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
184 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
185 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
186 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
187 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
188 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
189 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
190 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
191 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
192 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
193 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
194 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
195 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
196 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
197 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
198 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
199 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
200 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
201 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
202 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
203 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
204 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
205 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
206 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
207 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
208 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
209 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
210 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
211 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
212 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
213 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
214 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
215 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
216 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
217 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
218 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
219 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
220 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
221 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
222 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
223 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
224 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
225 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
226 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
227 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
228 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu...
229 XGBoost_Hybrid Sampling (SMOTEENN)_Median Impu... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 XGBoost 0.889913 NaN Zero Imputation
1 XGBoost 0.127572 NaN Zero Imputation
2 XGBoost 0.130802 NaN Zero Imputation
3 XGBoost 0.129167 NaN Zero Imputation
4 XGBoost 0.668105 NaN Zero Imputation
5 XGBoost 0.282391 NaN Zero Imputation
6 XGBoost 0.336210 NaN Zero Imputation
7 XGBoost NaN binary:logistic Zero Imputation
8 XGBoost NaN None Zero Imputation
9 XGBoost NaN None Zero Imputation
10 XGBoost NaN None Zero Imputation
11 XGBoost NaN None Zero Imputation
12 XGBoost NaN None Zero Imputation
13 XGBoost NaN None Zero Imputation
14 XGBoost NaN None Zero Imputation
15 XGBoost NaN None Zero Imputation
16 XGBoost NaN False Zero Imputation
17 XGBoost NaN None Zero Imputation
18 XGBoost NaN None Zero Imputation
19 XGBoost NaN None Zero Imputation
20 XGBoost NaN None Zero Imputation
21 XGBoost NaN None Zero Imputation
22 XGBoost NaN None Zero Imputation
23 XGBoost NaN None Zero Imputation
24 XGBoost NaN None Zero Imputation
25 XGBoost NaN None Zero Imputation
26 XGBoost NaN None Zero Imputation
27 XGBoost NaN None Zero Imputation
28 XGBoost NaN None Zero Imputation
29 XGBoost NaN None Zero Imputation
30 XGBoost NaN None Zero Imputation
31 XGBoost NaN NaN Zero Imputation
32 XGBoost NaN None Zero Imputation
33 XGBoost NaN None Zero Imputation
34 XGBoost NaN None Zero Imputation
35 XGBoost NaN None Zero Imputation
36 XGBoost NaN None Zero Imputation
37 XGBoost NaN None Zero Imputation
38 XGBoost NaN None Zero Imputation
39 XGBoost NaN None Zero Imputation
40 XGBoost NaN None Zero Imputation
41 XGBoost NaN None Zero Imputation
42 XGBoost NaN None Zero Imputation
43 XGBoost NaN None Zero Imputation
44 XGBoost NaN None Zero Imputation
45 XGBoost NaN None Zero Imputation
46 XGBoost 0.903871 NaN Zero Imputation
47 XGBoost 0.131868 NaN Zero Imputation
48 XGBoost 0.219512 NaN Zero Imputation
49 XGBoost 0.164760 NaN Zero Imputation
50 XGBoost 0.695321 NaN Zero Imputation
51 XGBoost 0.296330 NaN Zero Imputation
52 XGBoost 0.390642 NaN Zero Imputation
53 XGBoost NaN binary:logistic Zero Imputation
54 XGBoost NaN None Zero Imputation
55 XGBoost NaN None Zero Imputation
56 XGBoost NaN None Zero Imputation
57 XGBoost NaN None Zero Imputation
58 XGBoost NaN None Zero Imputation
59 XGBoost NaN None Zero Imputation
60 XGBoost NaN None Zero Imputation
61 XGBoost NaN None Zero Imputation
62 XGBoost NaN False Zero Imputation
63 XGBoost NaN None Zero Imputation
64 XGBoost NaN None Zero Imputation
65 XGBoost NaN None Zero Imputation
66 XGBoost NaN None Zero Imputation
67 XGBoost NaN None Zero Imputation
68 XGBoost NaN None Zero Imputation
69 XGBoost NaN None Zero Imputation
70 XGBoost NaN None Zero Imputation
71 XGBoost NaN None Zero Imputation
72 XGBoost NaN None Zero Imputation
73 XGBoost NaN None Zero Imputation
74 XGBoost NaN None Zero Imputation
75 XGBoost NaN None Zero Imputation
76 XGBoost NaN None Zero Imputation
77 XGBoost NaN NaN Zero Imputation
78 XGBoost NaN None Zero Imputation
79 XGBoost NaN None Zero Imputation
80 XGBoost NaN None Zero Imputation
81 XGBoost NaN None Zero Imputation
82 XGBoost NaN None Zero Imputation
83 XGBoost NaN None Zero Imputation
84 XGBoost NaN None Zero Imputation
85 XGBoost NaN None Zero Imputation
86 XGBoost NaN None Zero Imputation
87 XGBoost NaN None Zero Imputation
88 XGBoost NaN None Zero Imputation
89 XGBoost NaN None Zero Imputation
90 XGBoost NaN None Zero Imputation
91 XGBoost NaN None Zero Imputation
92 XGBoost 0.900184 NaN Zero Imputation
93 XGBoost 0.136364 NaN Zero Imputation
94 XGBoost 0.192513 NaN Zero Imputation
95 XGBoost 0.159645 NaN Zero Imputation
96 XGBoost 0.686458 NaN Zero Imputation
97 XGBoost 0.284813 NaN Zero Imputation
98 XGBoost 0.372915 NaN Zero Imputation
99 XGBoost NaN binary:logistic Zero Imputation
100 XGBoost NaN None Zero Imputation
101 XGBoost NaN None Zero Imputation
102 XGBoost NaN None Zero Imputation
103 XGBoost NaN None Zero Imputation
104 XGBoost NaN None Zero Imputation
105 XGBoost NaN None Zero Imputation
106 XGBoost NaN None Zero Imputation
107 XGBoost NaN None Zero Imputation
108 XGBoost NaN False Zero Imputation
109 XGBoost NaN None Zero Imputation
110 XGBoost NaN None Zero Imputation
111 XGBoost NaN None Zero Imputation
112 XGBoost NaN None Zero Imputation
113 XGBoost NaN None Zero Imputation
114 XGBoost NaN None Zero Imputation
115 XGBoost NaN None Zero Imputation
116 XGBoost NaN None Zero Imputation
117 XGBoost NaN None Zero Imputation
118 XGBoost NaN None Zero Imputation
119 XGBoost NaN None Zero Imputation
120 XGBoost NaN None Zero Imputation
121 XGBoost NaN None Zero Imputation
122 XGBoost NaN None Zero Imputation
123 XGBoost NaN NaN Zero Imputation
124 XGBoost NaN None Zero Imputation
125 XGBoost NaN None Zero Imputation
126 XGBoost NaN None Zero Imputation
127 XGBoost NaN None Zero Imputation
128 XGBoost NaN None Zero Imputation
129 XGBoost NaN None Zero Imputation
130 XGBoost NaN None Zero Imputation
131 XGBoost NaN None Zero Imputation
132 XGBoost NaN None Zero Imputation
133 XGBoost NaN None Zero Imputation
134 XGBoost NaN None Zero Imputation
135 XGBoost NaN None Zero Imputation
136 XGBoost NaN None Zero Imputation
137 XGBoost NaN None Zero Imputation
138 XGBoost 0.897524 NaN Zero Imputation
139 XGBoost 0.118577 NaN Zero Imputation
140 XGBoost 0.153061 NaN Zero Imputation
141 XGBoost 0.133630 NaN Zero Imputation
142 XGBoost 0.671283 NaN Zero Imputation
143 XGBoost 0.263730 NaN Zero Imputation
144 XGBoost 0.342567 NaN Zero Imputation
145 XGBoost NaN binary:logistic Zero Imputation
146 XGBoost NaN None Zero Imputation
147 XGBoost NaN None Zero Imputation
148 XGBoost NaN None Zero Imputation
149 XGBoost NaN None Zero Imputation
150 XGBoost NaN None Zero Imputation
151 XGBoost NaN None Zero Imputation
152 XGBoost NaN None Zero Imputation
153 XGBoost NaN None Zero Imputation
154 XGBoost NaN False Zero Imputation
155 XGBoost NaN None Zero Imputation
156 XGBoost NaN None Zero Imputation
157 XGBoost NaN None Zero Imputation
158 XGBoost NaN None Zero Imputation
159 XGBoost NaN None Zero Imputation
160 XGBoost NaN None Zero Imputation
161 XGBoost NaN None Zero Imputation
162 XGBoost NaN None Zero Imputation
163 XGBoost NaN None Zero Imputation
164 XGBoost NaN None Zero Imputation
165 XGBoost NaN None Zero Imputation
166 XGBoost NaN None Zero Imputation
167 XGBoost NaN None Zero Imputation
168 XGBoost NaN None Zero Imputation
169 XGBoost NaN NaN Zero Imputation
170 XGBoost NaN None Zero Imputation
171 XGBoost NaN None Zero Imputation
172 XGBoost NaN None Zero Imputation
173 XGBoost NaN None Zero Imputation
174 XGBoost NaN None Zero Imputation
175 XGBoost NaN None Zero Imputation
176 XGBoost NaN None Zero Imputation
177 XGBoost NaN None Zero Imputation
178 XGBoost NaN None Zero Imputation
179 XGBoost NaN None Zero Imputation
180 XGBoost NaN None Zero Imputation
181 XGBoost NaN None Zero Imputation
182 XGBoost NaN None Zero Imputation
183 XGBoost NaN None Zero Imputation
184 XGBoost 0.901475 NaN Zero Imputation
185 XGBoost 0.165254 NaN Zero Imputation
186 XGBoost 0.180556 NaN Zero Imputation
187 XGBoost 0.172566 NaN Zero Imputation
188 XGBoost 0.708571 NaN Zero Imputation
189 XGBoost 0.338687 NaN Zero Imputation
190 XGBoost 0.417141 NaN Zero Imputation
191 XGBoost NaN binary:logistic Zero Imputation
192 XGBoost NaN None Zero Imputation
193 XGBoost NaN None Zero Imputation
194 XGBoost NaN None Zero Imputation
195 XGBoost NaN None Zero Imputation
196 XGBoost NaN None Zero Imputation
197 XGBoost NaN None Zero Imputation
198 XGBoost NaN None Zero Imputation
199 XGBoost NaN None Zero Imputation
200 XGBoost NaN False Zero Imputation
201 XGBoost NaN None Zero Imputation
202 XGBoost NaN None Zero Imputation
203 XGBoost NaN None Zero Imputation
204 XGBoost NaN None Zero Imputation
205 XGBoost NaN None Zero Imputation
206 XGBoost NaN None Zero Imputation
207 XGBoost NaN None Zero Imputation
208 XGBoost NaN None Zero Imputation
209 XGBoost NaN None Zero Imputation
210 XGBoost NaN None Zero Imputation
211 XGBoost NaN None Zero Imputation
212 XGBoost NaN None Zero Imputation
213 XGBoost NaN None Zero Imputation
214 XGBoost NaN None Zero Imputation
215 XGBoost NaN NaN Zero Imputation
216 XGBoost NaN None Zero Imputation
217 XGBoost NaN None Zero Imputation
218 XGBoost NaN None Zero Imputation
219 XGBoost NaN None Zero Imputation
220 XGBoost NaN None Zero Imputation
221 XGBoost NaN None Zero Imputation
222 XGBoost NaN None Zero Imputation
223 XGBoost NaN None Zero Imputation
224 XGBoost NaN None Zero Imputation
225 XGBoost NaN None Zero Imputation
226 XGBoost NaN None Zero Imputation
227 XGBoost NaN None Zero Imputation
228 XGBoost NaN None Zero Imputation
229 XGBoost NaN None Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
95 Hybrid Sampling (SMOTETomek)
96 Hybrid Sampling (SMOTETomek)
97 Hybrid Sampling (SMOTETomek)
98 Hybrid Sampling (SMOTETomek)
99 Hybrid Sampling (SMOTETomek)
100 Hybrid Sampling (SMOTETomek)
101 Hybrid Sampling (SMOTETomek)
102 Hybrid Sampling (SMOTETomek)
103 Hybrid Sampling (SMOTETomek)
104 Hybrid Sampling (SMOTETomek)
105 Hybrid Sampling (SMOTETomek)
106 Hybrid Sampling (SMOTETomek)
107 Hybrid Sampling (SMOTETomek)
108 Hybrid Sampling (SMOTETomek)
109 Hybrid Sampling (SMOTETomek)
110 Hybrid Sampling (SMOTETomek)
111 Hybrid Sampling (SMOTETomek)
112 Hybrid Sampling (SMOTETomek)
113 Hybrid Sampling (SMOTETomek)
114 Hybrid Sampling (SMOTETomek)
115 Hybrid Sampling (SMOTETomek)
116 Hybrid Sampling (SMOTETomek)
117 Hybrid Sampling (SMOTETomek)
118 Hybrid Sampling (SMOTETomek)
119 Hybrid Sampling (SMOTETomek)
120 Hybrid Sampling (SMOTETomek)
121 Hybrid Sampling (SMOTETomek)
122 Hybrid Sampling (SMOTETomek)
123 Hybrid Sampling (SMOTETomek)
124 Hybrid Sampling (SMOTETomek)
125 Hybrid Sampling (SMOTETomek)
126 Hybrid Sampling (SMOTETomek)
127 Hybrid Sampling (SMOTETomek)
128 Hybrid Sampling (SMOTETomek)
129 Hybrid Sampling (SMOTETomek)
130 Hybrid Sampling (SMOTETomek)
131 Hybrid Sampling (SMOTETomek)
132 Hybrid Sampling (SMOTETomek)
133 Hybrid Sampling (SMOTETomek)
134 Hybrid Sampling (SMOTETomek)
135 Hybrid Sampling (SMOTETomek)
136 Hybrid Sampling (SMOTETomek)
137 Hybrid Sampling (SMOTETomek)
138 Hybrid Sampling (SMOTETomek)
139 Hybrid Sampling (SMOTETomek)
140 Hybrid Sampling (SMOTETomek)
141 Hybrid Sampling (SMOTETomek)
142 Hybrid Sampling (SMOTETomek)
143 Hybrid Sampling (SMOTETomek)
144 Hybrid Sampling (SMOTETomek)
145 Hybrid Sampling (SMOTETomek)
146 Hybrid Sampling (SMOTETomek)
147 Hybrid Sampling (SMOTETomek)
148 Hybrid Sampling (SMOTETomek)
149 Hybrid Sampling (SMOTETomek)
150 Hybrid Sampling (SMOTETomek)
151 Hybrid Sampling (SMOTETomek)
152 Hybrid Sampling (SMOTETomek)
153 Hybrid Sampling (SMOTETomek)
154 Hybrid Sampling (SMOTETomek)
155 Hybrid Sampling (SMOTETomek)
156 Hybrid Sampling (SMOTETomek)
157 Hybrid Sampling (SMOTETomek)
158 Hybrid Sampling (SMOTETomek)
159 Hybrid Sampling (SMOTETomek)
160 Hybrid Sampling (SMOTETomek)
161 Hybrid Sampling (SMOTETomek)
162 Hybrid Sampling (SMOTETomek)
163 Hybrid Sampling (SMOTETomek)
164 Hybrid Sampling (SMOTETomek)
165 Hybrid Sampling (SMOTETomek)
166 Hybrid Sampling (SMOTETomek)
167 Hybrid Sampling (SMOTETomek)
168 Hybrid Sampling (SMOTETomek)
169 Hybrid Sampling (SMOTETomek)
170 Hybrid Sampling (SMOTETomek)
171 Hybrid Sampling (SMOTETomek)
172 Hybrid Sampling (SMOTETomek)
173 Hybrid Sampling (SMOTETomek)
174 Hybrid Sampling (SMOTETomek)
175 Hybrid Sampling (SMOTETomek)
176 Hybrid Sampling (SMOTETomek)
177 Hybrid Sampling (SMOTETomek)
178 Hybrid Sampling (SMOTETomek)
179 Hybrid Sampling (SMOTETomek)
180 Hybrid Sampling (SMOTETomek)
181 Hybrid Sampling (SMOTETomek)
182 Hybrid Sampling (SMOTETomek)
183 Hybrid Sampling (SMOTETomek)
184 Hybrid Sampling (SMOTETomek)
185 Hybrid Sampling (SMOTETomek)
186 Hybrid Sampling (SMOTETomek)
187 Hybrid Sampling (SMOTETomek)
188 Hybrid Sampling (SMOTETomek)
189 Hybrid Sampling (SMOTETomek)
190 Hybrid Sampling (SMOTETomek)
191 Hybrid Sampling (SMOTETomek)
192 Hybrid Sampling (SMOTETomek)
193 Hybrid Sampling (SMOTETomek)
194 Hybrid Sampling (SMOTETomek)
195 Hybrid Sampling (SMOTETomek)
196 Hybrid Sampling (SMOTETomek)
197 Hybrid Sampling (SMOTETomek)
198 Hybrid Sampling (SMOTETomek)
199 Hybrid Sampling (SMOTETomek)
200 Hybrid Sampling (SMOTETomek)
201 Hybrid Sampling (SMOTETomek)
202 Hybrid Sampling (SMOTETomek)
203 Hybrid Sampling (SMOTETomek)
204 Hybrid Sampling (SMOTETomek)
205 Hybrid Sampling (SMOTETomek)
206 Hybrid Sampling (SMOTETomek)
207 Hybrid Sampling (SMOTETomek)
208 Hybrid Sampling (SMOTETomek)
209 Hybrid Sampling (SMOTETomek)
210 Hybrid Sampling (SMOTETomek)
211 Hybrid Sampling (SMOTETomek)
212 Hybrid Sampling (SMOTETomek)
213 Hybrid Sampling (SMOTETomek)
214 Hybrid Sampling (SMOTETomek)
215 Hybrid Sampling (SMOTETomek)
216 Hybrid Sampling (SMOTETomek)
217 Hybrid Sampling (SMOTETomek)
218 Hybrid Sampling (SMOTETomek)
219 Hybrid Sampling (SMOTETomek)
220 Hybrid Sampling (SMOTETomek)
221 Hybrid Sampling (SMOTETomek)
222 Hybrid Sampling (SMOTETomek)
223 Hybrid Sampling (SMOTETomek)
224 Hybrid Sampling (SMOTETomek)
225 Hybrid Sampling (SMOTETomek)
226 Hybrid Sampling (SMOTETomek)
227 Hybrid Sampling (SMOTETomek)
228 Hybrid Sampling (SMOTETomek)
229 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
1 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
2 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
3 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
4 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
5 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
6 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
7 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
8 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
9 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
10 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
11 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
12 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
13 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
14 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
15 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
16 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
17 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
18 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
19 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
20 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
21 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
22 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
23 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
24 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
25 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
26 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
27 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
28 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
29 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
30 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
31 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
32 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
33 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
34 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
35 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
36 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
37 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
38 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
39 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
40 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
41 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
42 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
43 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
44 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
45 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
46 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
47 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
48 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
49 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
50 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
51 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
52 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
53 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
54 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
55 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
56 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
57 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
58 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
59 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
60 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
61 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
62 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
63 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
64 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
65 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
66 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
67 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
68 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
69 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
70 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
71 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
72 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
73 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
74 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
75 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
76 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
77 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
78 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
79 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
80 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
81 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
82 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
83 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
84 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
85 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
86 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
87 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
88 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
89 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
90 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
91 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
92 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
93 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
94 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
95 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
96 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
97 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
98 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
99 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
100 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
101 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
102 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
103 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
104 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
105 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
106 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
107 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
108 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
109 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
110 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
111 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
112 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
113 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
114 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
115 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
116 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
117 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
118 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
119 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
120 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
121 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
122 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
123 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
124 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
125 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
126 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
127 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
128 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
129 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
130 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
131 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
132 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
133 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
134 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
135 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
136 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
137 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
138 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
139 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
140 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
141 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
142 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
143 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
144 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
145 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
146 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
147 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
148 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
149 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
150 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
151 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
152 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
153 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
154 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
155 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
156 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
157 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
158 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
159 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
160 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
161 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
162 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
163 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
164 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
165 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
166 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
167 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
168 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
169 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
170 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
171 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
172 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
173 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
174 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
175 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
176 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
177 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
178 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
179 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
180 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
181 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
182 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
183 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
184 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
185 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
186 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
187 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
188 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
189 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
190 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
191 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
192 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
193 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
194 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
195 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
196 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
197 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
198 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
199 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
200 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
201 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
202 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
203 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
204 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
205 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
206 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
207 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
208 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
209 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
210 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
211 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
212 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
213 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
214 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
215 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
216 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
217 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
218 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
219 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
220 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
221 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
222 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
223 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
224 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
225 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
226 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
227 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
228 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu...
229 XGBoost_Hybrid Sampling (SMOTETomek)_Zero Impu... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 XGBoost 0.888333 NaN Mean Imputation
1 XGBoost 0.154982 NaN Mean Imputation
2 XGBoost 0.177215 NaN Mean Imputation
3 XGBoost 0.165354 NaN Mean Imputation
4 XGBoost 0.659546 NaN Mean Imputation
5 XGBoost 0.239434 NaN Mean Imputation
6 XGBoost 0.319093 NaN Mean Imputation
7 XGBoost NaN binary:logistic Mean Imputation
8 XGBoost NaN None Mean Imputation
9 XGBoost NaN None Mean Imputation
10 XGBoost NaN None Mean Imputation
11 XGBoost NaN None Mean Imputation
12 XGBoost NaN None Mean Imputation
13 XGBoost NaN None Mean Imputation
14 XGBoost NaN None Mean Imputation
15 XGBoost NaN None Mean Imputation
16 XGBoost NaN False Mean Imputation
17 XGBoost NaN None Mean Imputation
18 XGBoost NaN None Mean Imputation
19 XGBoost NaN None Mean Imputation
20 XGBoost NaN None Mean Imputation
21 XGBoost NaN None Mean Imputation
22 XGBoost NaN None Mean Imputation
23 XGBoost NaN None Mean Imputation
24 XGBoost NaN None Mean Imputation
25 XGBoost NaN None Mean Imputation
26 XGBoost NaN None Mean Imputation
27 XGBoost NaN None Mean Imputation
28 XGBoost NaN None Mean Imputation
29 XGBoost NaN None Mean Imputation
30 XGBoost NaN None Mean Imputation
31 XGBoost NaN NaN Mean Imputation
32 XGBoost NaN None Mean Imputation
33 XGBoost NaN None Mean Imputation
34 XGBoost NaN None Mean Imputation
35 XGBoost NaN None Mean Imputation
36 XGBoost NaN None Mean Imputation
37 XGBoost NaN None Mean Imputation
38 XGBoost NaN None Mean Imputation
39 XGBoost NaN None Mean Imputation
40 XGBoost NaN None Mean Imputation
41 XGBoost NaN None Mean Imputation
42 XGBoost NaN None Mean Imputation
43 XGBoost NaN None Mean Imputation
44 XGBoost NaN None Mean Imputation
45 XGBoost NaN None Mean Imputation
46 XGBoost 0.907032 NaN Mean Imputation
47 XGBoost 0.107884 NaN Mean Imputation
48 XGBoost 0.158537 NaN Mean Imputation
49 XGBoost 0.128395 NaN Mean Imputation
50 XGBoost 0.698087 NaN Mean Imputation
51 XGBoost 0.299992 NaN Mean Imputation
52 XGBoost 0.396174 NaN Mean Imputation
53 XGBoost NaN binary:logistic Mean Imputation
54 XGBoost NaN None Mean Imputation
55 XGBoost NaN None Mean Imputation
56 XGBoost NaN None Mean Imputation
57 XGBoost NaN None Mean Imputation
58 XGBoost NaN None Mean Imputation
59 XGBoost NaN None Mean Imputation
60 XGBoost NaN None Mean Imputation
61 XGBoost NaN None Mean Imputation
62 XGBoost NaN False Mean Imputation
63 XGBoost NaN None Mean Imputation
64 XGBoost NaN None Mean Imputation
65 XGBoost NaN None Mean Imputation
66 XGBoost NaN None Mean Imputation
67 XGBoost NaN None Mean Imputation
68 XGBoost NaN None Mean Imputation
69 XGBoost NaN None Mean Imputation
70 XGBoost NaN None Mean Imputation
71 XGBoost NaN None Mean Imputation
72 XGBoost NaN None Mean Imputation
73 XGBoost NaN None Mean Imputation
74 XGBoost NaN None Mean Imputation
75 XGBoost NaN None Mean Imputation
76 XGBoost NaN None Mean Imputation
77 XGBoost NaN NaN Mean Imputation
78 XGBoost NaN None Mean Imputation
79 XGBoost NaN None Mean Imputation
80 XGBoost NaN None Mean Imputation
81 XGBoost NaN None Mean Imputation
82 XGBoost NaN None Mean Imputation
83 XGBoost NaN None Mean Imputation
84 XGBoost NaN None Mean Imputation
85 XGBoost NaN None Mean Imputation
86 XGBoost NaN None Mean Imputation
87 XGBoost NaN None Mean Imputation
88 XGBoost NaN None Mean Imputation
89 XGBoost NaN None Mean Imputation
90 XGBoost NaN None Mean Imputation
91 XGBoost NaN None Mean Imputation
92 XGBoost 0.898604 NaN Mean Imputation
93 XGBoost 0.143885 NaN Mean Imputation
94 XGBoost 0.213904 NaN Mean Imputation
95 XGBoost 0.172043 NaN Mean Imputation
96 XGBoost 0.669749 NaN Mean Imputation
97 XGBoost 0.278310 NaN Mean Imputation
98 XGBoost 0.339498 NaN Mean Imputation
99 XGBoost NaN binary:logistic Mean Imputation
100 XGBoost NaN None Mean Imputation
101 XGBoost NaN None Mean Imputation
102 XGBoost NaN None Mean Imputation
103 XGBoost NaN None Mean Imputation
104 XGBoost NaN None Mean Imputation
105 XGBoost NaN None Mean Imputation
106 XGBoost NaN None Mean Imputation
107 XGBoost NaN None Mean Imputation
108 XGBoost NaN False Mean Imputation
109 XGBoost NaN None Mean Imputation
110 XGBoost NaN None Mean Imputation
111 XGBoost NaN None Mean Imputation
112 XGBoost NaN None Mean Imputation
113 XGBoost NaN None Mean Imputation
114 XGBoost NaN None Mean Imputation
115 XGBoost NaN None Mean Imputation
116 XGBoost NaN None Mean Imputation
117 XGBoost NaN None Mean Imputation
118 XGBoost NaN None Mean Imputation
119 XGBoost NaN None Mean Imputation
120 XGBoost NaN None Mean Imputation
121 XGBoost NaN None Mean Imputation
122 XGBoost NaN None Mean Imputation
123 XGBoost NaN NaN Mean Imputation
124 XGBoost NaN None Mean Imputation
125 XGBoost NaN None Mean Imputation
126 XGBoost NaN None Mean Imputation
127 XGBoost NaN None Mean Imputation
128 XGBoost NaN None Mean Imputation
129 XGBoost NaN None Mean Imputation
130 XGBoost NaN None Mean Imputation
131 XGBoost NaN None Mean Imputation
132 XGBoost NaN None Mean Imputation
133 XGBoost NaN None Mean Imputation
134 XGBoost NaN None Mean Imputation
135 XGBoost NaN None Mean Imputation
136 XGBoost NaN None Mean Imputation
137 XGBoost NaN None Mean Imputation
138 XGBoost 0.893836 NaN Mean Imputation
139 XGBoost 0.126354 NaN Mean Imputation
140 XGBoost 0.178571 NaN Mean Imputation
141 XGBoost 0.147992 NaN Mean Imputation
142 XGBoost 0.668641 NaN Mean Imputation
143 XGBoost 0.259717 NaN Mean Imputation
144 XGBoost 0.337282 NaN Mean Imputation
145 XGBoost NaN binary:logistic Mean Imputation
146 XGBoost NaN None Mean Imputation
147 XGBoost NaN None Mean Imputation
148 XGBoost NaN None Mean Imputation
149 XGBoost NaN None Mean Imputation
150 XGBoost NaN None Mean Imputation
151 XGBoost NaN None Mean Imputation
152 XGBoost NaN None Mean Imputation
153 XGBoost NaN None Mean Imputation
154 XGBoost NaN False Mean Imputation
155 XGBoost NaN None Mean Imputation
156 XGBoost NaN None Mean Imputation
157 XGBoost NaN None Mean Imputation
158 XGBoost NaN None Mean Imputation
159 XGBoost NaN None Mean Imputation
160 XGBoost NaN None Mean Imputation
161 XGBoost NaN None Mean Imputation
162 XGBoost NaN None Mean Imputation
163 XGBoost NaN None Mean Imputation
164 XGBoost NaN None Mean Imputation
165 XGBoost NaN None Mean Imputation
166 XGBoost NaN None Mean Imputation
167 XGBoost NaN None Mean Imputation
168 XGBoost NaN None Mean Imputation
169 XGBoost NaN NaN Mean Imputation
170 XGBoost NaN None Mean Imputation
171 XGBoost NaN None Mean Imputation
172 XGBoost NaN None Mean Imputation
173 XGBoost NaN None Mean Imputation
174 XGBoost NaN None Mean Imputation
175 XGBoost NaN None Mean Imputation
176 XGBoost NaN None Mean Imputation
177 XGBoost NaN None Mean Imputation
178 XGBoost NaN None Mean Imputation
179 XGBoost NaN None Mean Imputation
180 XGBoost NaN None Mean Imputation
181 XGBoost NaN None Mean Imputation
182 XGBoost NaN None Mean Imputation
183 XGBoost NaN None Mean Imputation
184 XGBoost 0.889884 NaN Mean Imputation
185 XGBoost 0.144366 NaN Mean Imputation
186 XGBoost 0.189815 NaN Mean Imputation
187 XGBoost 0.164000 NaN Mean Imputation
188 XGBoost 0.702099 NaN Mean Imputation
189 XGBoost 0.349193 NaN Mean Imputation
190 XGBoost 0.404199 NaN Mean Imputation
191 XGBoost NaN binary:logistic Mean Imputation
192 XGBoost NaN None Mean Imputation
193 XGBoost NaN None Mean Imputation
194 XGBoost NaN None Mean Imputation
195 XGBoost NaN None Mean Imputation
196 XGBoost NaN None Mean Imputation
197 XGBoost NaN None Mean Imputation
198 XGBoost NaN None Mean Imputation
199 XGBoost NaN None Mean Imputation
200 XGBoost NaN False Mean Imputation
201 XGBoost NaN None Mean Imputation
202 XGBoost NaN None Mean Imputation
203 XGBoost NaN None Mean Imputation
204 XGBoost NaN None Mean Imputation
205 XGBoost NaN None Mean Imputation
206 XGBoost NaN None Mean Imputation
207 XGBoost NaN None Mean Imputation
208 XGBoost NaN None Mean Imputation
209 XGBoost NaN None Mean Imputation
210 XGBoost NaN None Mean Imputation
211 XGBoost NaN None Mean Imputation
212 XGBoost NaN None Mean Imputation
213 XGBoost NaN None Mean Imputation
214 XGBoost NaN None Mean Imputation
215 XGBoost NaN NaN Mean Imputation
216 XGBoost NaN None Mean Imputation
217 XGBoost NaN None Mean Imputation
218 XGBoost NaN None Mean Imputation
219 XGBoost NaN None Mean Imputation
220 XGBoost NaN None Mean Imputation
221 XGBoost NaN None Mean Imputation
222 XGBoost NaN None Mean Imputation
223 XGBoost NaN None Mean Imputation
224 XGBoost NaN None Mean Imputation
225 XGBoost NaN None Mean Imputation
226 XGBoost NaN None Mean Imputation
227 XGBoost NaN None Mean Imputation
228 XGBoost NaN None Mean Imputation
229 XGBoost NaN None Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
95 Hybrid Sampling (SMOTETomek)
96 Hybrid Sampling (SMOTETomek)
97 Hybrid Sampling (SMOTETomek)
98 Hybrid Sampling (SMOTETomek)
99 Hybrid Sampling (SMOTETomek)
100 Hybrid Sampling (SMOTETomek)
101 Hybrid Sampling (SMOTETomek)
102 Hybrid Sampling (SMOTETomek)
103 Hybrid Sampling (SMOTETomek)
104 Hybrid Sampling (SMOTETomek)
105 Hybrid Sampling (SMOTETomek)
106 Hybrid Sampling (SMOTETomek)
107 Hybrid Sampling (SMOTETomek)
108 Hybrid Sampling (SMOTETomek)
109 Hybrid Sampling (SMOTETomek)
110 Hybrid Sampling (SMOTETomek)
111 Hybrid Sampling (SMOTETomek)
112 Hybrid Sampling (SMOTETomek)
113 Hybrid Sampling (SMOTETomek)
114 Hybrid Sampling (SMOTETomek)
115 Hybrid Sampling (SMOTETomek)
116 Hybrid Sampling (SMOTETomek)
117 Hybrid Sampling (SMOTETomek)
118 Hybrid Sampling (SMOTETomek)
119 Hybrid Sampling (SMOTETomek)
120 Hybrid Sampling (SMOTETomek)
121 Hybrid Sampling (SMOTETomek)
122 Hybrid Sampling (SMOTETomek)
123 Hybrid Sampling (SMOTETomek)
124 Hybrid Sampling (SMOTETomek)
125 Hybrid Sampling (SMOTETomek)
126 Hybrid Sampling (SMOTETomek)
127 Hybrid Sampling (SMOTETomek)
128 Hybrid Sampling (SMOTETomek)
129 Hybrid Sampling (SMOTETomek)
130 Hybrid Sampling (SMOTETomek)
131 Hybrid Sampling (SMOTETomek)
132 Hybrid Sampling (SMOTETomek)
133 Hybrid Sampling (SMOTETomek)
134 Hybrid Sampling (SMOTETomek)
135 Hybrid Sampling (SMOTETomek)
136 Hybrid Sampling (SMOTETomek)
137 Hybrid Sampling (SMOTETomek)
138 Hybrid Sampling (SMOTETomek)
139 Hybrid Sampling (SMOTETomek)
140 Hybrid Sampling (SMOTETomek)
141 Hybrid Sampling (SMOTETomek)
142 Hybrid Sampling (SMOTETomek)
143 Hybrid Sampling (SMOTETomek)
144 Hybrid Sampling (SMOTETomek)
145 Hybrid Sampling (SMOTETomek)
146 Hybrid Sampling (SMOTETomek)
147 Hybrid Sampling (SMOTETomek)
148 Hybrid Sampling (SMOTETomek)
149 Hybrid Sampling (SMOTETomek)
150 Hybrid Sampling (SMOTETomek)
151 Hybrid Sampling (SMOTETomek)
152 Hybrid Sampling (SMOTETomek)
153 Hybrid Sampling (SMOTETomek)
154 Hybrid Sampling (SMOTETomek)
155 Hybrid Sampling (SMOTETomek)
156 Hybrid Sampling (SMOTETomek)
157 Hybrid Sampling (SMOTETomek)
158 Hybrid Sampling (SMOTETomek)
159 Hybrid Sampling (SMOTETomek)
160 Hybrid Sampling (SMOTETomek)
161 Hybrid Sampling (SMOTETomek)
162 Hybrid Sampling (SMOTETomek)
163 Hybrid Sampling (SMOTETomek)
164 Hybrid Sampling (SMOTETomek)
165 Hybrid Sampling (SMOTETomek)
166 Hybrid Sampling (SMOTETomek)
167 Hybrid Sampling (SMOTETomek)
168 Hybrid Sampling (SMOTETomek)
169 Hybrid Sampling (SMOTETomek)
170 Hybrid Sampling (SMOTETomek)
171 Hybrid Sampling (SMOTETomek)
172 Hybrid Sampling (SMOTETomek)
173 Hybrid Sampling (SMOTETomek)
174 Hybrid Sampling (SMOTETomek)
175 Hybrid Sampling (SMOTETomek)
176 Hybrid Sampling (SMOTETomek)
177 Hybrid Sampling (SMOTETomek)
178 Hybrid Sampling (SMOTETomek)
179 Hybrid Sampling (SMOTETomek)
180 Hybrid Sampling (SMOTETomek)
181 Hybrid Sampling (SMOTETomek)
182 Hybrid Sampling (SMOTETomek)
183 Hybrid Sampling (SMOTETomek)
184 Hybrid Sampling (SMOTETomek)
185 Hybrid Sampling (SMOTETomek)
186 Hybrid Sampling (SMOTETomek)
187 Hybrid Sampling (SMOTETomek)
188 Hybrid Sampling (SMOTETomek)
189 Hybrid Sampling (SMOTETomek)
190 Hybrid Sampling (SMOTETomek)
191 Hybrid Sampling (SMOTETomek)
192 Hybrid Sampling (SMOTETomek)
193 Hybrid Sampling (SMOTETomek)
194 Hybrid Sampling (SMOTETomek)
195 Hybrid Sampling (SMOTETomek)
196 Hybrid Sampling (SMOTETomek)
197 Hybrid Sampling (SMOTETomek)
198 Hybrid Sampling (SMOTETomek)
199 Hybrid Sampling (SMOTETomek)
200 Hybrid Sampling (SMOTETomek)
201 Hybrid Sampling (SMOTETomek)
202 Hybrid Sampling (SMOTETomek)
203 Hybrid Sampling (SMOTETomek)
204 Hybrid Sampling (SMOTETomek)
205 Hybrid Sampling (SMOTETomek)
206 Hybrid Sampling (SMOTETomek)
207 Hybrid Sampling (SMOTETomek)
208 Hybrid Sampling (SMOTETomek)
209 Hybrid Sampling (SMOTETomek)
210 Hybrid Sampling (SMOTETomek)
211 Hybrid Sampling (SMOTETomek)
212 Hybrid Sampling (SMOTETomek)
213 Hybrid Sampling (SMOTETomek)
214 Hybrid Sampling (SMOTETomek)
215 Hybrid Sampling (SMOTETomek)
216 Hybrid Sampling (SMOTETomek)
217 Hybrid Sampling (SMOTETomek)
218 Hybrid Sampling (SMOTETomek)
219 Hybrid Sampling (SMOTETomek)
220 Hybrid Sampling (SMOTETomek)
221 Hybrid Sampling (SMOTETomek)
222 Hybrid Sampling (SMOTETomek)
223 Hybrid Sampling (SMOTETomek)
224 Hybrid Sampling (SMOTETomek)
225 Hybrid Sampling (SMOTETomek)
226 Hybrid Sampling (SMOTETomek)
227 Hybrid Sampling (SMOTETomek)
228 Hybrid Sampling (SMOTETomek)
229 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
1 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
2 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
3 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
4 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
5 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
6 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
7 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
8 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
9 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
10 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
11 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
12 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
13 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
14 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
15 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
16 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
17 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
18 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
19 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
20 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
21 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
22 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
23 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
24 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
25 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
26 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
27 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
28 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
29 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
30 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
31 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
32 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
33 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
34 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
35 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
36 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
37 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
38 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
39 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
40 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
41 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
42 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
43 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
44 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
45 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
46 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
47 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
48 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
49 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
50 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
51 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
52 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
53 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
54 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
55 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
56 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
57 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
58 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
59 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
60 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
61 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
62 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
63 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
64 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
65 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
66 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
67 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
68 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
69 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
70 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
71 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
72 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
73 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
74 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
75 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
76 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
77 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
78 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
79 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
80 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
81 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
82 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
83 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
84 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
85 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
86 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
87 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
88 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
89 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
90 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
91 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
92 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
93 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
94 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
95 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
96 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
97 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
98 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
99 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
100 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
101 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
102 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
103 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
104 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
105 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
106 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
107 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
108 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
109 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
110 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
111 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
112 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
113 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
114 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
115 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
116 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
117 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
118 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
119 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
120 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
121 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
122 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
123 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
124 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
125 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
126 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
127 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
128 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
129 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
130 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
131 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
132 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
133 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
134 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
135 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
136 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
137 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
138 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
139 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
140 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
141 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
142 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
143 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
144 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
145 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
146 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
147 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
148 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
149 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
150 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
151 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
152 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
153 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
154 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
155 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
156 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
157 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
158 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
159 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
160 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
161 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
162 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
163 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
164 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
165 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
166 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
167 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
168 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
169 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
170 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
171 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
172 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
173 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
174 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
175 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
176 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
177 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
178 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
179 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
180 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
181 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
182 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
183 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
184 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
185 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
186 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
187 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
188 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
189 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
190 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
191 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
192 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
193 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
194 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
195 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
196 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
197 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
198 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
199 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
200 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
201 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
202 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
203 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
204 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
205 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
206 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
207 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
208 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
209 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
210 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
211 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
212 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
213 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
214 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
215 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
216 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
217 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
218 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
219 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
220 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
221 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
222 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
223 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
224 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
225 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
226 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
227 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
228 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu...
229 XGBoost_Hybrid Sampling (SMOTETomek)_Mean Impu... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 XGBoost 0.887806 NaN Median Imputation
1 XGBoost 0.132296 NaN Median Imputation
2 XGBoost 0.143460 NaN Median Imputation
3 XGBoost 0.137652 NaN Median Imputation
4 XGBoost 0.653358 NaN Median Imputation
5 XGBoost 0.244218 NaN Median Imputation
6 XGBoost 0.306715 NaN Median Imputation
7 XGBoost NaN binary:logistic Median Imputation
8 XGBoost NaN None Median Imputation
9 XGBoost NaN None Median Imputation
10 XGBoost NaN None Median Imputation
11 XGBoost NaN None Median Imputation
12 XGBoost NaN None Median Imputation
13 XGBoost NaN None Median Imputation
14 XGBoost NaN None Median Imputation
15 XGBoost NaN None Median Imputation
16 XGBoost NaN False Median Imputation
17 XGBoost NaN None Median Imputation
18 XGBoost NaN None Median Imputation
19 XGBoost NaN None Median Imputation
20 XGBoost NaN None Median Imputation
21 XGBoost NaN None Median Imputation
22 XGBoost NaN None Median Imputation
23 XGBoost NaN None Median Imputation
24 XGBoost NaN None Median Imputation
25 XGBoost NaN None Median Imputation
26 XGBoost NaN None Median Imputation
27 XGBoost NaN None Median Imputation
28 XGBoost NaN None Median Imputation
29 XGBoost NaN None Median Imputation
30 XGBoost NaN None Median Imputation
31 XGBoost NaN NaN Median Imputation
32 XGBoost NaN None Median Imputation
33 XGBoost NaN None Median Imputation
34 XGBoost NaN None Median Imputation
35 XGBoost NaN None Median Imputation
36 XGBoost NaN None Median Imputation
37 XGBoost NaN None Median Imputation
38 XGBoost NaN None Median Imputation
39 XGBoost NaN None Median Imputation
40 XGBoost NaN None Median Imputation
41 XGBoost NaN None Median Imputation
42 XGBoost NaN None Median Imputation
43 XGBoost NaN None Median Imputation
44 XGBoost NaN None Median Imputation
45 XGBoost NaN None Median Imputation
46 XGBoost 0.905452 NaN Median Imputation
47 XGBoost 0.123552 NaN Median Imputation
48 XGBoost 0.195122 NaN Median Imputation
49 XGBoost 0.151300 NaN Median Imputation
50 XGBoost 0.679774 NaN Median Imputation
51 XGBoost 0.284064 NaN Median Imputation
52 XGBoost 0.359548 NaN Median Imputation
53 XGBoost NaN binary:logistic Median Imputation
54 XGBoost NaN None Median Imputation
55 XGBoost NaN None Median Imputation
56 XGBoost NaN None Median Imputation
57 XGBoost NaN None Median Imputation
58 XGBoost NaN None Median Imputation
59 XGBoost NaN None Median Imputation
60 XGBoost NaN None Median Imputation
61 XGBoost NaN None Median Imputation
62 XGBoost NaN False Median Imputation
63 XGBoost NaN None Median Imputation
64 XGBoost NaN None Median Imputation
65 XGBoost NaN None Median Imputation
66 XGBoost NaN None Median Imputation
67 XGBoost NaN None Median Imputation
68 XGBoost NaN None Median Imputation
69 XGBoost NaN None Median Imputation
70 XGBoost NaN None Median Imputation
71 XGBoost NaN None Median Imputation
72 XGBoost NaN None Median Imputation
73 XGBoost NaN None Median Imputation
74 XGBoost NaN None Median Imputation
75 XGBoost NaN None Median Imputation
76 XGBoost NaN None Median Imputation
77 XGBoost NaN NaN Median Imputation
78 XGBoost NaN None Median Imputation
79 XGBoost NaN None Median Imputation
80 XGBoost NaN None Median Imputation
81 XGBoost NaN None Median Imputation
82 XGBoost NaN None Median Imputation
83 XGBoost NaN None Median Imputation
84 XGBoost NaN None Median Imputation
85 XGBoost NaN None Median Imputation
86 XGBoost NaN None Median Imputation
87 XGBoost NaN None Median Imputation
88 XGBoost NaN None Median Imputation
89 XGBoost NaN None Median Imputation
90 XGBoost NaN None Median Imputation
91 XGBoost NaN None Median Imputation
92 XGBoost 0.895180 NaN Median Imputation
93 XGBoost 0.116364 NaN Median Imputation
94 XGBoost 0.171123 NaN Median Imputation
95 XGBoost 0.138528 NaN Median Imputation
96 XGBoost 0.668646 NaN Median Imputation
97 XGBoost 0.266230 NaN Median Imputation
98 XGBoost 0.337292 NaN Median Imputation
99 XGBoost NaN binary:logistic Median Imputation
100 XGBoost NaN None Median Imputation
101 XGBoost NaN None Median Imputation
102 XGBoost NaN None Median Imputation
103 XGBoost NaN None Median Imputation
104 XGBoost NaN None Median Imputation
105 XGBoost NaN None Median Imputation
106 XGBoost NaN None Median Imputation
107 XGBoost NaN None Median Imputation
108 XGBoost NaN False Median Imputation
109 XGBoost NaN None Median Imputation
110 XGBoost NaN None Median Imputation
111 XGBoost NaN None Median Imputation
112 XGBoost NaN None Median Imputation
113 XGBoost NaN None Median Imputation
114 XGBoost NaN None Median Imputation
115 XGBoost NaN None Median Imputation
116 XGBoost NaN None Median Imputation
117 XGBoost NaN None Median Imputation
118 XGBoost NaN None Median Imputation
119 XGBoost NaN None Median Imputation
120 XGBoost NaN None Median Imputation
121 XGBoost NaN None Median Imputation
122 XGBoost NaN None Median Imputation
123 XGBoost NaN NaN Median Imputation
124 XGBoost NaN None Median Imputation
125 XGBoost NaN None Median Imputation
126 XGBoost NaN None Median Imputation
127 XGBoost NaN None Median Imputation
128 XGBoost NaN None Median Imputation
129 XGBoost NaN None Median Imputation
130 XGBoost NaN None Median Imputation
131 XGBoost NaN None Median Imputation
132 XGBoost NaN None Median Imputation
133 XGBoost NaN None Median Imputation
134 XGBoost NaN None Median Imputation
135 XGBoost NaN None Median Imputation
136 XGBoost NaN None Median Imputation
137 XGBoost NaN None Median Imputation
138 XGBoost 0.894099 NaN Median Imputation
139 XGBoost 0.132143 NaN Median Imputation
140 XGBoost 0.188776 NaN Median Imputation
141 XGBoost 0.155462 NaN Median Imputation
142 XGBoost 0.680699 NaN Median Imputation
143 XGBoost 0.296287 NaN Median Imputation
144 XGBoost 0.361399 NaN Median Imputation
145 XGBoost NaN binary:logistic Median Imputation
146 XGBoost NaN None Median Imputation
147 XGBoost NaN None Median Imputation
148 XGBoost NaN None Median Imputation
149 XGBoost NaN None Median Imputation
150 XGBoost NaN None Median Imputation
151 XGBoost NaN None Median Imputation
152 XGBoost NaN None Median Imputation
153 XGBoost NaN None Median Imputation
154 XGBoost NaN False Median Imputation
155 XGBoost NaN None Median Imputation
156 XGBoost NaN None Median Imputation
157 XGBoost NaN None Median Imputation
158 XGBoost NaN None Median Imputation
159 XGBoost NaN None Median Imputation
160 XGBoost NaN None Median Imputation
161 XGBoost NaN None Median Imputation
162 XGBoost NaN None Median Imputation
163 XGBoost NaN None Median Imputation
164 XGBoost NaN None Median Imputation
165 XGBoost NaN None Median Imputation
166 XGBoost NaN None Median Imputation
167 XGBoost NaN None Median Imputation
168 XGBoost NaN None Median Imputation
169 XGBoost NaN NaN Median Imputation
170 XGBoost NaN None Median Imputation
171 XGBoost NaN None Median Imputation
172 XGBoost NaN None Median Imputation
173 XGBoost NaN None Median Imputation
174 XGBoost NaN None Median Imputation
175 XGBoost NaN None Median Imputation
176 XGBoost NaN None Median Imputation
177 XGBoost NaN None Median Imputation
178 XGBoost NaN None Median Imputation
179 XGBoost NaN None Median Imputation
180 XGBoost NaN None Median Imputation
181 XGBoost NaN None Median Imputation
182 XGBoost NaN None Median Imputation
183 XGBoost NaN None Median Imputation
184 XGBoost 0.894362 NaN Median Imputation
185 XGBoost 0.150943 NaN Median Imputation
186 XGBoost 0.185185 NaN Median Imputation
187 XGBoost 0.166320 NaN Median Imputation
188 XGBoost 0.695182 NaN Median Imputation
189 XGBoost 0.309202 NaN Median Imputation
190 XGBoost 0.390364 NaN Median Imputation
191 XGBoost NaN binary:logistic Median Imputation
192 XGBoost NaN None Median Imputation
193 XGBoost NaN None Median Imputation
194 XGBoost NaN None Median Imputation
195 XGBoost NaN None Median Imputation
196 XGBoost NaN None Median Imputation
197 XGBoost NaN None Median Imputation
198 XGBoost NaN None Median Imputation
199 XGBoost NaN None Median Imputation
200 XGBoost NaN False Median Imputation
201 XGBoost NaN None Median Imputation
202 XGBoost NaN None Median Imputation
203 XGBoost NaN None Median Imputation
204 XGBoost NaN None Median Imputation
205 XGBoost NaN None Median Imputation
206 XGBoost NaN None Median Imputation
207 XGBoost NaN None Median Imputation
208 XGBoost NaN None Median Imputation
209 XGBoost NaN None Median Imputation
210 XGBoost NaN None Median Imputation
211 XGBoost NaN None Median Imputation
212 XGBoost NaN None Median Imputation
213 XGBoost NaN None Median Imputation
214 XGBoost NaN None Median Imputation
215 XGBoost NaN NaN Median Imputation
216 XGBoost NaN None Median Imputation
217 XGBoost NaN None Median Imputation
218 XGBoost NaN None Median Imputation
219 XGBoost NaN None Median Imputation
220 XGBoost NaN None Median Imputation
221 XGBoost NaN None Median Imputation
222 XGBoost NaN None Median Imputation
223 XGBoost NaN None Median Imputation
224 XGBoost NaN None Median Imputation
225 XGBoost NaN None Median Imputation
226 XGBoost NaN None Median Imputation
227 XGBoost NaN None Median Imputation
228 XGBoost NaN None Median Imputation
229 XGBoost NaN None Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
95 Hybrid Sampling (SMOTETomek)
96 Hybrid Sampling (SMOTETomek)
97 Hybrid Sampling (SMOTETomek)
98 Hybrid Sampling (SMOTETomek)
99 Hybrid Sampling (SMOTETomek)
100 Hybrid Sampling (SMOTETomek)
101 Hybrid Sampling (SMOTETomek)
102 Hybrid Sampling (SMOTETomek)
103 Hybrid Sampling (SMOTETomek)
104 Hybrid Sampling (SMOTETomek)
105 Hybrid Sampling (SMOTETomek)
106 Hybrid Sampling (SMOTETomek)
107 Hybrid Sampling (SMOTETomek)
108 Hybrid Sampling (SMOTETomek)
109 Hybrid Sampling (SMOTETomek)
110 Hybrid Sampling (SMOTETomek)
111 Hybrid Sampling (SMOTETomek)
112 Hybrid Sampling (SMOTETomek)
113 Hybrid Sampling (SMOTETomek)
114 Hybrid Sampling (SMOTETomek)
115 Hybrid Sampling (SMOTETomek)
116 Hybrid Sampling (SMOTETomek)
117 Hybrid Sampling (SMOTETomek)
118 Hybrid Sampling (SMOTETomek)
119 Hybrid Sampling (SMOTETomek)
120 Hybrid Sampling (SMOTETomek)
121 Hybrid Sampling (SMOTETomek)
122 Hybrid Sampling (SMOTETomek)
123 Hybrid Sampling (SMOTETomek)
124 Hybrid Sampling (SMOTETomek)
125 Hybrid Sampling (SMOTETomek)
126 Hybrid Sampling (SMOTETomek)
127 Hybrid Sampling (SMOTETomek)
128 Hybrid Sampling (SMOTETomek)
129 Hybrid Sampling (SMOTETomek)
130 Hybrid Sampling (SMOTETomek)
131 Hybrid Sampling (SMOTETomek)
132 Hybrid Sampling (SMOTETomek)
133 Hybrid Sampling (SMOTETomek)
134 Hybrid Sampling (SMOTETomek)
135 Hybrid Sampling (SMOTETomek)
136 Hybrid Sampling (SMOTETomek)
137 Hybrid Sampling (SMOTETomek)
138 Hybrid Sampling (SMOTETomek)
139 Hybrid Sampling (SMOTETomek)
140 Hybrid Sampling (SMOTETomek)
141 Hybrid Sampling (SMOTETomek)
142 Hybrid Sampling (SMOTETomek)
143 Hybrid Sampling (SMOTETomek)
144 Hybrid Sampling (SMOTETomek)
145 Hybrid Sampling (SMOTETomek)
146 Hybrid Sampling (SMOTETomek)
147 Hybrid Sampling (SMOTETomek)
148 Hybrid Sampling (SMOTETomek)
149 Hybrid Sampling (SMOTETomek)
150 Hybrid Sampling (SMOTETomek)
151 Hybrid Sampling (SMOTETomek)
152 Hybrid Sampling (SMOTETomek)
153 Hybrid Sampling (SMOTETomek)
154 Hybrid Sampling (SMOTETomek)
155 Hybrid Sampling (SMOTETomek)
156 Hybrid Sampling (SMOTETomek)
157 Hybrid Sampling (SMOTETomek)
158 Hybrid Sampling (SMOTETomek)
159 Hybrid Sampling (SMOTETomek)
160 Hybrid Sampling (SMOTETomek)
161 Hybrid Sampling (SMOTETomek)
162 Hybrid Sampling (SMOTETomek)
163 Hybrid Sampling (SMOTETomek)
164 Hybrid Sampling (SMOTETomek)
165 Hybrid Sampling (SMOTETomek)
166 Hybrid Sampling (SMOTETomek)
167 Hybrid Sampling (SMOTETomek)
168 Hybrid Sampling (SMOTETomek)
169 Hybrid Sampling (SMOTETomek)
170 Hybrid Sampling (SMOTETomek)
171 Hybrid Sampling (SMOTETomek)
172 Hybrid Sampling (SMOTETomek)
173 Hybrid Sampling (SMOTETomek)
174 Hybrid Sampling (SMOTETomek)
175 Hybrid Sampling (SMOTETomek)
176 Hybrid Sampling (SMOTETomek)
177 Hybrid Sampling (SMOTETomek)
178 Hybrid Sampling (SMOTETomek)
179 Hybrid Sampling (SMOTETomek)
180 Hybrid Sampling (SMOTETomek)
181 Hybrid Sampling (SMOTETomek)
182 Hybrid Sampling (SMOTETomek)
183 Hybrid Sampling (SMOTETomek)
184 Hybrid Sampling (SMOTETomek)
185 Hybrid Sampling (SMOTETomek)
186 Hybrid Sampling (SMOTETomek)
187 Hybrid Sampling (SMOTETomek)
188 Hybrid Sampling (SMOTETomek)
189 Hybrid Sampling (SMOTETomek)
190 Hybrid Sampling (SMOTETomek)
191 Hybrid Sampling (SMOTETomek)
192 Hybrid Sampling (SMOTETomek)
193 Hybrid Sampling (SMOTETomek)
194 Hybrid Sampling (SMOTETomek)
195 Hybrid Sampling (SMOTETomek)
196 Hybrid Sampling (SMOTETomek)
197 Hybrid Sampling (SMOTETomek)
198 Hybrid Sampling (SMOTETomek)
199 Hybrid Sampling (SMOTETomek)
200 Hybrid Sampling (SMOTETomek)
201 Hybrid Sampling (SMOTETomek)
202 Hybrid Sampling (SMOTETomek)
203 Hybrid Sampling (SMOTETomek)
204 Hybrid Sampling (SMOTETomek)
205 Hybrid Sampling (SMOTETomek)
206 Hybrid Sampling (SMOTETomek)
207 Hybrid Sampling (SMOTETomek)
208 Hybrid Sampling (SMOTETomek)
209 Hybrid Sampling (SMOTETomek)
210 Hybrid Sampling (SMOTETomek)
211 Hybrid Sampling (SMOTETomek)
212 Hybrid Sampling (SMOTETomek)
213 Hybrid Sampling (SMOTETomek)
214 Hybrid Sampling (SMOTETomek)
215 Hybrid Sampling (SMOTETomek)
216 Hybrid Sampling (SMOTETomek)
217 Hybrid Sampling (SMOTETomek)
218 Hybrid Sampling (SMOTETomek)
219 Hybrid Sampling (SMOTETomek)
220 Hybrid Sampling (SMOTETomek)
221 Hybrid Sampling (SMOTETomek)
222 Hybrid Sampling (SMOTETomek)
223 Hybrid Sampling (SMOTETomek)
224 Hybrid Sampling (SMOTETomek)
225 Hybrid Sampling (SMOTETomek)
226 Hybrid Sampling (SMOTETomek)
227 Hybrid Sampling (SMOTETomek)
228 Hybrid Sampling (SMOTETomek)
229 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
1 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
2 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
3 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
4 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
5 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
6 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
7 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
8 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
9 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
10 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
11 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
12 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
13 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
14 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
15 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
16 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
17 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
18 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
19 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
20 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
21 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
22 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
23 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
24 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
25 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
26 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
27 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
28 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
29 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
30 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
31 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
32 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
33 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
34 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
35 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
36 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
37 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
38 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
39 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
40 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
41 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
42 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
43 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
44 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
45 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
46 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
47 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
48 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
49 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
50 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
51 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
52 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
53 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
54 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
55 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
56 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
57 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
58 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
59 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
60 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
61 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
62 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
63 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
64 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
65 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
66 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
67 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
68 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
69 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
70 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
71 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
72 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
73 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
74 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
75 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
76 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
77 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
78 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
79 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
80 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
81 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
82 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
83 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
84 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
85 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
86 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
87 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
88 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
89 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
90 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
91 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
92 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
93 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
94 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
95 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
96 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
97 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
98 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
99 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
100 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
101 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
102 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
103 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
104 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
105 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
106 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
107 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
108 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
109 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
110 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
111 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
112 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
113 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
114 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
115 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
116 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
117 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
118 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
119 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
120 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
121 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
122 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
123 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
124 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
125 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
126 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
127 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
128 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
129 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
130 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
131 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
132 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
133 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
134 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
135 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
136 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
137 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
138 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
139 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
140 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
141 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
142 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
143 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
144 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
145 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
146 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
147 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
148 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
149 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
150 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
151 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
152 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
153 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
154 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
155 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
156 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
157 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
158 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
159 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
160 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
161 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
162 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
163 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
164 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
165 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
166 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
167 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
168 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
169 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
170 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
171 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
172 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
173 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
174 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
175 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
176 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
177 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
178 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
179 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
180 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
181 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
182 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
183 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
184 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
185 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
186 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
187 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
188 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
189 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
190 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
191 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
192 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
193 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
194 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
195 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
196 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
197 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
198 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
199 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
200 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
201 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
202 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
203 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
204 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
205 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
206 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
207 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
208 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
209 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
210 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
211 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
212 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
213 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
214 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
215 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
216 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
217 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
218 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
219 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
220 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
221 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
222 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
223 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
224 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
225 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
226 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
227 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
228 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im...
229 XGBoost_Hybrid Sampling (SMOTETomek)_Median Im... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 LightGBM 0.810903 NaN Zero Imputation
1 LightGBM 0.131700 NaN Zero Imputation
2 LightGBM 0.362869 NaN Zero Imputation
3 LightGBM 0.193258 NaN Zero Imputation
4 LightGBM 0.685294 NaN Zero Imputation
5 LightGBM 0.276413 NaN Zero Imputation
6 LightGBM 0.370589 NaN Zero Imputation
7 LightGBM NaN gbdt Zero Imputation
8 LightGBM NaN None Zero Imputation
9 LightGBM NaN 1.0 Zero Imputation
10 LightGBM NaN split Zero Imputation
11 LightGBM NaN 0.1 Zero Imputation
12 LightGBM NaN -1 Zero Imputation
13 LightGBM NaN 20 Zero Imputation
14 LightGBM NaN 0.001 Zero Imputation
15 LightGBM NaN 0.0 Zero Imputation
16 LightGBM NaN 100 Zero Imputation
17 LightGBM NaN None Zero Imputation
18 LightGBM NaN 31 Zero Imputation
19 LightGBM NaN None Zero Imputation
20 LightGBM NaN None Zero Imputation
21 LightGBM NaN 0.0 Zero Imputation
22 LightGBM NaN 0.0 Zero Imputation
23 LightGBM NaN 1.0 Zero Imputation
24 LightGBM NaN 200000 Zero Imputation
25 LightGBM NaN 0 Zero Imputation
26 LightGBM 0.814854 NaN Zero Imputation
27 LightGBM 0.100741 NaN Zero Imputation
28 LightGBM 0.414634 NaN Zero Imputation
29 LightGBM 0.162098 NaN Zero Imputation
30 LightGBM 0.711087 NaN Zero Imputation
31 LightGBM 0.340757 NaN Zero Imputation
32 LightGBM 0.422173 NaN Zero Imputation
33 LightGBM NaN gbdt Zero Imputation
34 LightGBM NaN None Zero Imputation
35 LightGBM NaN 1.0 Zero Imputation
36 LightGBM NaN split Zero Imputation
37 LightGBM NaN 0.1 Zero Imputation
38 LightGBM NaN -1 Zero Imputation
39 LightGBM NaN 20 Zero Imputation
40 LightGBM NaN 0.001 Zero Imputation
41 LightGBM NaN 0.0 Zero Imputation
42 LightGBM NaN 100 Zero Imputation
43 LightGBM NaN None Zero Imputation
44 LightGBM NaN 31 Zero Imputation
45 LightGBM NaN None Zero Imputation
46 LightGBM NaN None Zero Imputation
47 LightGBM NaN 0.0 Zero Imputation
48 LightGBM NaN 0.0 Zero Imputation
49 LightGBM NaN 1.0 Zero Imputation
50 LightGBM NaN 200000 Zero Imputation
51 LightGBM NaN 0 Zero Imputation
52 LightGBM 0.798789 NaN Zero Imputation
53 LightGBM 0.107483 NaN Zero Imputation
54 LightGBM 0.422460 NaN Zero Imputation
55 LightGBM 0.171367 NaN Zero Imputation
56 LightGBM 0.692921 NaN Zero Imputation
57 LightGBM 0.315564 NaN Zero Imputation
58 LightGBM 0.385843 NaN Zero Imputation
59 LightGBM NaN gbdt Zero Imputation
60 LightGBM NaN None Zero Imputation
61 LightGBM NaN 1.0 Zero Imputation
62 LightGBM NaN split Zero Imputation
63 LightGBM NaN 0.1 Zero Imputation
64 LightGBM NaN -1 Zero Imputation
65 LightGBM NaN 20 Zero Imputation
66 LightGBM NaN 0.001 Zero Imputation
67 LightGBM NaN 0.0 Zero Imputation
68 LightGBM NaN 100 Zero Imputation
69 LightGBM NaN None Zero Imputation
70 LightGBM NaN 31 Zero Imputation
71 LightGBM NaN None Zero Imputation
72 LightGBM NaN None Zero Imputation
73 LightGBM NaN 0.0 Zero Imputation
74 LightGBM NaN 0.0 Zero Imputation
75 LightGBM NaN 1.0 Zero Imputation
76 LightGBM NaN 200000 Zero Imputation
77 LightGBM NaN 0 Zero Imputation
78 LightGBM 0.807429 NaN Zero Imputation
79 LightGBM 0.113997 NaN Zero Imputation
80 LightGBM 0.403061 NaN Zero Imputation
81 LightGBM 0.177728 NaN Zero Imputation
82 LightGBM 0.688001 NaN Zero Imputation
83 LightGBM 0.285159 NaN Zero Imputation
84 LightGBM 0.376002 NaN Zero Imputation
85 LightGBM NaN gbdt Zero Imputation
86 LightGBM NaN None Zero Imputation
87 LightGBM NaN 1.0 Zero Imputation
88 LightGBM NaN split Zero Imputation
89 LightGBM NaN 0.1 Zero Imputation
90 LightGBM NaN -1 Zero Imputation
91 LightGBM NaN 20 Zero Imputation
92 LightGBM NaN 0.001 Zero Imputation
93 LightGBM NaN 0.0 Zero Imputation
94 LightGBM NaN 100 Zero Imputation
95 LightGBM NaN None Zero Imputation
96 LightGBM NaN 31 Zero Imputation
97 LightGBM NaN None Zero Imputation
98 LightGBM NaN None Zero Imputation
99 LightGBM NaN 0.0 Zero Imputation
100 LightGBM NaN 0.0 Zero Imputation
101 LightGBM NaN 1.0 Zero Imputation
102 LightGBM NaN 200000 Zero Imputation
103 LightGBM NaN 0 Zero Imputation
104 LightGBM 0.826660 NaN Zero Imputation
105 LightGBM 0.151420 NaN Zero Imputation
106 LightGBM 0.444444 NaN Zero Imputation
107 LightGBM 0.225882 NaN Zero Imputation
108 LightGBM 0.723786 NaN Zero Imputation
109 LightGBM 0.354412 NaN Zero Imputation
110 LightGBM 0.447571 NaN Zero Imputation
111 LightGBM NaN gbdt Zero Imputation
112 LightGBM NaN None Zero Imputation
113 LightGBM NaN 1.0 Zero Imputation
114 LightGBM NaN split Zero Imputation
115 LightGBM NaN 0.1 Zero Imputation
116 LightGBM NaN -1 Zero Imputation
117 LightGBM NaN 20 Zero Imputation
118 LightGBM NaN 0.001 Zero Imputation
119 LightGBM NaN 0.0 Zero Imputation
120 LightGBM NaN 100 Zero Imputation
121 LightGBM NaN None Zero Imputation
122 LightGBM NaN 31 Zero Imputation
123 LightGBM NaN None Zero Imputation
124 LightGBM NaN None Zero Imputation
125 LightGBM NaN 0.0 Zero Imputation
126 LightGBM NaN 0.0 Zero Imputation
127 LightGBM NaN 1.0 Zero Imputation
128 LightGBM NaN 200000 Zero Imputation
129 LightGBM NaN 0 Zero Imputation
Imbalance Class Technique Model Unique Code
0 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
1 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
2 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
3 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
4 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
5 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
6 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
7 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
8 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
9 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
10 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
11 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
12 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
13 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
14 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
15 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
16 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
17 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
18 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
19 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
20 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
21 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
22 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
23 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
24 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
25 Oversampling LightGBM_Oversampling_Zero Imputation_fold_0
26 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
27 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
28 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
29 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
30 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
31 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
32 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
33 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
34 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
35 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
36 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
37 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
38 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
39 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
40 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
41 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
42 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
43 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
44 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
45 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
46 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
47 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
48 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
49 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
50 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
51 Oversampling LightGBM_Oversampling_Zero Imputation_fold_1
52 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
53 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
54 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
55 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
56 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
57 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
58 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
59 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
60 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
61 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
62 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
63 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
64 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
65 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
66 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
67 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
68 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
69 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
70 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
71 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
72 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
73 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
74 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
75 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
76 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
77 Oversampling LightGBM_Oversampling_Zero Imputation_fold_2
78 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
79 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
80 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
81 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
82 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
83 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
84 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
85 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
86 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
87 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
88 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
89 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
90 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
91 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
92 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
93 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
94 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
95 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
96 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
97 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
98 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
99 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
100 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
101 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
102 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
103 Oversampling LightGBM_Oversampling_Zero Imputation_fold_3
104 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
105 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
106 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
107 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
108 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
109 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
110 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
111 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
112 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
113 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
114 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
115 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
116 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
117 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
118 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
119 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
120 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
121 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
122 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
123 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
124 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
125 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
126 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
127 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
128 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4
129 Oversampling LightGBM_Oversampling_Zero Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 LightGBM 0.814854 NaN Mean Imputation
1 LightGBM 0.133648 NaN Mean Imputation
2 LightGBM 0.358650 NaN Mean Imputation
3 LightGBM 0.194731 NaN Mean Imputation
4 LightGBM 0.684344 NaN Mean Imputation
5 LightGBM 0.304198 NaN Mean Imputation
6 LightGBM 0.368689 NaN Mean Imputation
7 LightGBM NaN gbdt Mean Imputation
8 LightGBM NaN None Mean Imputation
9 LightGBM NaN 1.0 Mean Imputation
10 LightGBM NaN split Mean Imputation
11 LightGBM NaN 0.1 Mean Imputation
12 LightGBM NaN -1 Mean Imputation
13 LightGBM NaN 20 Mean Imputation
14 LightGBM NaN 0.001 Mean Imputation
15 LightGBM NaN 0.0 Mean Imputation
16 LightGBM NaN 100 Mean Imputation
17 LightGBM NaN None Mean Imputation
18 LightGBM NaN 31 Mean Imputation
19 LightGBM NaN None Mean Imputation
20 LightGBM NaN None Mean Imputation
21 LightGBM NaN 0.0 Mean Imputation
22 LightGBM NaN 0.0 Mean Imputation
23 LightGBM NaN 1.0 Mean Imputation
24 LightGBM NaN 200000 Mean Imputation
25 LightGBM NaN 0 Mean Imputation
26 LightGBM 0.818804 NaN Mean Imputation
27 LightGBM 0.101824 NaN Mean Imputation
28 LightGBM 0.408537 NaN Mean Imputation
29 LightGBM 0.163017 NaN Mean Imputation
30 LightGBM 0.704826 NaN Mean Imputation
31 LightGBM 0.338201 NaN Mean Imputation
32 LightGBM 0.409653 NaN Mean Imputation
33 LightGBM NaN gbdt Mean Imputation
34 LightGBM NaN None Mean Imputation
35 LightGBM NaN 1.0 Mean Imputation
36 LightGBM NaN split Mean Imputation
37 LightGBM NaN 0.1 Mean Imputation
38 LightGBM NaN -1 Mean Imputation
39 LightGBM NaN 20 Mean Imputation
40 LightGBM NaN 0.001 Mean Imputation
41 LightGBM NaN 0.0 Mean Imputation
42 LightGBM NaN 100 Mean Imputation
43 LightGBM NaN None Mean Imputation
44 LightGBM NaN 31 Mean Imputation
45 LightGBM NaN None Mean Imputation
46 LightGBM NaN None Mean Imputation
47 LightGBM NaN 0.0 Mean Imputation
48 LightGBM NaN 0.0 Mean Imputation
49 LightGBM NaN 1.0 Mean Imputation
50 LightGBM NaN 200000 Mean Imputation
51 LightGBM NaN 0 Mean Imputation
52 LightGBM 0.803266 NaN Mean Imputation
53 LightGBM 0.117486 NaN Mean Imputation
54 LightGBM 0.459893 NaN Mean Imputation
55 LightGBM 0.187160 NaN Mean Imputation
56 LightGBM 0.696535 NaN Mean Imputation
57 LightGBM 0.306396 NaN Mean Imputation
58 LightGBM 0.393070 NaN Mean Imputation
59 LightGBM NaN gbdt Mean Imputation
60 LightGBM NaN None Mean Imputation
61 LightGBM NaN 1.0 Mean Imputation
62 LightGBM NaN split Mean Imputation
63 LightGBM NaN 0.1 Mean Imputation
64 LightGBM NaN -1 Mean Imputation
65 LightGBM NaN 20 Mean Imputation
66 LightGBM NaN 0.001 Mean Imputation
67 LightGBM NaN 0.0 Mean Imputation
68 LightGBM NaN 100 Mean Imputation
69 LightGBM NaN None Mean Imputation
70 LightGBM NaN 31 Mean Imputation
71 LightGBM NaN None Mean Imputation
72 LightGBM NaN None Mean Imputation
73 LightGBM NaN 0.0 Mean Imputation
74 LightGBM NaN 0.0 Mean Imputation
75 LightGBM NaN 1.0 Mean Imputation
76 LightGBM NaN 200000 Mean Imputation
77 LightGBM NaN 0 Mean Imputation
78 LightGBM 0.803477 NaN Mean Imputation
79 LightGBM 0.104885 NaN Mean Imputation
80 LightGBM 0.372449 NaN Mean Imputation
81 LightGBM 0.163677 NaN Mean Imputation
82 LightGBM 0.679731 NaN Mean Imputation
83 LightGBM 0.283696 NaN Mean Imputation
84 LightGBM 0.359461 NaN Mean Imputation
85 LightGBM NaN gbdt Mean Imputation
86 LightGBM NaN None Mean Imputation
87 LightGBM NaN 1.0 Mean Imputation
88 LightGBM NaN split Mean Imputation
89 LightGBM NaN 0.1 Mean Imputation
90 LightGBM NaN -1 Mean Imputation
91 LightGBM NaN 20 Mean Imputation
92 LightGBM NaN 0.001 Mean Imputation
93 LightGBM NaN 0.0 Mean Imputation
94 LightGBM NaN 100 Mean Imputation
95 LightGBM NaN None Mean Imputation
96 LightGBM NaN 31 Mean Imputation
97 LightGBM NaN None Mean Imputation
98 LightGBM NaN None Mean Imputation
99 LightGBM NaN 0.0 Mean Imputation
100 LightGBM NaN 0.0 Mean Imputation
101 LightGBM NaN 1.0 Mean Imputation
102 LightGBM NaN 200000 Mean Imputation
103 LightGBM NaN 0 Mean Imputation
104 LightGBM 0.826660 NaN Mean Imputation
105 LightGBM 0.141234 NaN Mean Imputation
106 LightGBM 0.402778 NaN Mean Imputation
107 LightGBM 0.209135 NaN Mean Imputation
108 LightGBM 0.707874 NaN Mean Imputation
109 LightGBM 0.326117 NaN Mean Imputation
110 LightGBM 0.415748 NaN Mean Imputation
111 LightGBM NaN gbdt Mean Imputation
112 LightGBM NaN None Mean Imputation
113 LightGBM NaN 1.0 Mean Imputation
114 LightGBM NaN split Mean Imputation
115 LightGBM NaN 0.1 Mean Imputation
116 LightGBM NaN -1 Mean Imputation
117 LightGBM NaN 20 Mean Imputation
118 LightGBM NaN 0.001 Mean Imputation
119 LightGBM NaN 0.0 Mean Imputation
120 LightGBM NaN 100 Mean Imputation
121 LightGBM NaN None Mean Imputation
122 LightGBM NaN 31 Mean Imputation
123 LightGBM NaN None Mean Imputation
124 LightGBM NaN None Mean Imputation
125 LightGBM NaN 0.0 Mean Imputation
126 LightGBM NaN 0.0 Mean Imputation
127 LightGBM NaN 1.0 Mean Imputation
128 LightGBM NaN 200000 Mean Imputation
129 LightGBM NaN 0 Mean Imputation
Imbalance Class Technique Model Unique Code
0 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
1 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
2 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
3 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
4 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
5 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
6 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
7 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
8 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
9 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
10 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
11 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
12 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
13 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
14 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
15 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
16 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
17 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
18 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
19 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
20 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
21 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
22 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
23 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
24 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
25 Oversampling LightGBM_Oversampling_Mean Imputation_fold_0
26 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
27 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
28 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
29 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
30 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
31 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
32 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
33 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
34 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
35 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
36 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
37 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
38 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
39 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
40 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
41 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
42 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
43 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
44 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
45 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
46 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
47 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
48 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
49 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
50 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
51 Oversampling LightGBM_Oversampling_Mean Imputation_fold_1
52 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
53 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
54 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
55 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
56 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
57 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
58 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
59 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
60 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
61 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
62 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
63 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
64 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
65 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
66 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
67 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
68 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
69 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
70 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
71 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
72 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
73 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
74 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
75 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
76 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
77 Oversampling LightGBM_Oversampling_Mean Imputation_fold_2
78 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
79 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
80 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
81 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
82 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
83 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
84 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
85 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
86 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
87 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
88 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
89 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
90 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
91 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
92 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
93 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
94 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
95 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
96 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
97 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
98 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
99 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
100 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
101 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
102 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
103 Oversampling LightGBM_Oversampling_Mean Imputation_fold_3
104 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
105 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
106 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
107 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
108 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
109 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
110 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
111 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
112 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
113 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
114 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
115 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
116 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
117 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
118 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
119 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
120 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
121 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
122 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
123 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
124 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
125 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
126 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
127 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
128 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4
129 Oversampling LightGBM_Oversampling_Mean Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 LightGBM 0.819331 NaN Median Imputation
1 LightGBM 0.131363 NaN Median Imputation
2 LightGBM 0.337553 NaN Median Imputation
3 LightGBM 0.189125 NaN Median Imputation
4 LightGBM 0.686851 NaN Median Imputation
5 LightGBM 0.296584 NaN Median Imputation
6 LightGBM 0.373702 NaN Median Imputation
7 LightGBM NaN gbdt Median Imputation
8 LightGBM NaN None Median Imputation
9 LightGBM NaN 1.0 Median Imputation
10 LightGBM NaN split Median Imputation
11 LightGBM NaN 0.1 Median Imputation
12 LightGBM NaN -1 Median Imputation
13 LightGBM NaN 20 Median Imputation
14 LightGBM NaN 0.001 Median Imputation
15 LightGBM NaN 0.0 Median Imputation
16 LightGBM NaN 100 Median Imputation
17 LightGBM NaN None Median Imputation
18 LightGBM NaN 31 Median Imputation
19 LightGBM NaN None Median Imputation
20 LightGBM NaN None Median Imputation
21 LightGBM NaN 0.0 Median Imputation
22 LightGBM NaN 0.0 Median Imputation
23 LightGBM NaN 1.0 Median Imputation
24 LightGBM NaN 200000 Median Imputation
25 LightGBM NaN 0 Median Imputation
26 LightGBM 0.825125 NaN Median Imputation
27 LightGBM 0.094156 NaN Median Imputation
28 LightGBM 0.353659 NaN Median Imputation
29 LightGBM 0.148718 NaN Median Imputation
30 LightGBM 0.700228 NaN Median Imputation
31 LightGBM 0.326580 NaN Median Imputation
32 LightGBM 0.400457 NaN Median Imputation
33 LightGBM NaN gbdt Median Imputation
34 LightGBM NaN None Median Imputation
35 LightGBM NaN 1.0 Median Imputation
36 LightGBM NaN split Median Imputation
37 LightGBM NaN 0.1 Median Imputation
38 LightGBM NaN -1 Median Imputation
39 LightGBM NaN 20 Median Imputation
40 LightGBM NaN 0.001 Median Imputation
41 LightGBM NaN 0.0 Median Imputation
42 LightGBM NaN 100 Median Imputation
43 LightGBM NaN None Median Imputation
44 LightGBM NaN 31 Median Imputation
45 LightGBM NaN None Median Imputation
46 LightGBM NaN None Median Imputation
47 LightGBM NaN 0.0 Median Imputation
48 LightGBM NaN 0.0 Median Imputation
49 LightGBM NaN 1.0 Median Imputation
50 LightGBM NaN 200000 Median Imputation
51 LightGBM NaN 0 Median Imputation
52 LightGBM 0.816434 NaN Median Imputation
53 LightGBM 0.115964 NaN Median Imputation
54 LightGBM 0.411765 NaN Median Imputation
55 LightGBM 0.180964 NaN Median Imputation
56 LightGBM 0.697232 NaN Median Imputation
57 LightGBM 0.314037 NaN Median Imputation
58 LightGBM 0.394464 NaN Median Imputation
59 LightGBM NaN gbdt Median Imputation
60 LightGBM NaN None Median Imputation
61 LightGBM NaN 1.0 Median Imputation
62 LightGBM NaN split Median Imputation
63 LightGBM NaN 0.1 Median Imputation
64 LightGBM NaN -1 Median Imputation
65 LightGBM NaN 20 Median Imputation
66 LightGBM NaN 0.001 Median Imputation
67 LightGBM NaN 0.0 Median Imputation
68 LightGBM NaN 100 Median Imputation
69 LightGBM NaN None Median Imputation
70 LightGBM NaN 31 Median Imputation
71 LightGBM NaN None Median Imputation
72 LightGBM NaN None Median Imputation
73 LightGBM NaN 0.0 Median Imputation
74 LightGBM NaN 0.0 Median Imputation
75 LightGBM NaN 1.0 Median Imputation
76 LightGBM NaN 200000 Median Imputation
77 LightGBM NaN 0 Median Imputation
78 LightGBM 0.814278 NaN Median Imputation
79 LightGBM 0.111450 NaN Median Imputation
80 LightGBM 0.372449 NaN Median Imputation
81 LightGBM 0.171563 NaN Median Imputation
82 LightGBM 0.686265 NaN Median Imputation
83 LightGBM 0.300584 NaN Median Imputation
84 LightGBM 0.372530 NaN Median Imputation
85 LightGBM NaN gbdt Median Imputation
86 LightGBM NaN None Median Imputation
87 LightGBM NaN 1.0 Median Imputation
88 LightGBM NaN split Median Imputation
89 LightGBM NaN 0.1 Median Imputation
90 LightGBM NaN -1 Median Imputation
91 LightGBM NaN 20 Median Imputation
92 LightGBM NaN 0.001 Median Imputation
93 LightGBM NaN 0.0 Median Imputation
94 LightGBM NaN 100 Median Imputation
95 LightGBM NaN None Median Imputation
96 LightGBM NaN 31 Median Imputation
97 LightGBM NaN None Median Imputation
98 LightGBM NaN None Median Imputation
99 LightGBM NaN 0.0 Median Imputation
100 LightGBM NaN 0.0 Median Imputation
101 LightGBM NaN 1.0 Median Imputation
102 LightGBM NaN 200000 Median Imputation
103 LightGBM NaN 0 Median Imputation
104 LightGBM 0.829557 NaN Median Imputation
105 LightGBM 0.137815 NaN Median Imputation
106 LightGBM 0.379630 NaN Median Imputation
107 LightGBM 0.202219 NaN Median Imputation
108 LightGBM 0.708282 NaN Median Imputation
109 LightGBM 0.338816 NaN Median Imputation
110 LightGBM 0.416565 NaN Median Imputation
111 LightGBM NaN gbdt Median Imputation
112 LightGBM NaN None Median Imputation
113 LightGBM NaN 1.0 Median Imputation
114 LightGBM NaN split Median Imputation
115 LightGBM NaN 0.1 Median Imputation
116 LightGBM NaN -1 Median Imputation
117 LightGBM NaN 20 Median Imputation
118 LightGBM NaN 0.001 Median Imputation
119 LightGBM NaN 0.0 Median Imputation
120 LightGBM NaN 100 Median Imputation
121 LightGBM NaN None Median Imputation
122 LightGBM NaN 31 Median Imputation
123 LightGBM NaN None Median Imputation
124 LightGBM NaN None Median Imputation
125 LightGBM NaN 0.0 Median Imputation
126 LightGBM NaN 0.0 Median Imputation
127 LightGBM NaN 1.0 Median Imputation
128 LightGBM NaN 200000 Median Imputation
129 LightGBM NaN 0 Median Imputation
Imbalance Class Technique Model Unique Code
0 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
1 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
2 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
3 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
4 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
5 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
6 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
7 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
8 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
9 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
10 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
11 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
12 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
13 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
14 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
15 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
16 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
17 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
18 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
19 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
20 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
21 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
22 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
23 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
24 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
25 Oversampling LightGBM_Oversampling_Median Imputation_fold_0
26 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
27 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
28 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
29 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
30 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
31 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
32 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
33 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
34 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
35 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
36 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
37 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
38 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
39 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
40 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
41 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
42 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
43 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
44 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
45 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
46 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
47 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
48 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
49 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
50 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
51 Oversampling LightGBM_Oversampling_Median Imputation_fold_1
52 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
53 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
54 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
55 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
56 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
57 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
58 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
59 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
60 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
61 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
62 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
63 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
64 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
65 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
66 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
67 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
68 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
69 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
70 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
71 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
72 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
73 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
74 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
75 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
76 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
77 Oversampling LightGBM_Oversampling_Median Imputation_fold_2
78 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
79 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
80 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
81 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
82 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
83 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
84 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
85 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
86 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
87 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
88 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
89 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
90 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
91 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
92 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
93 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
94 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
95 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
96 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
97 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
98 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
99 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
100 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
101 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
102 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
103 Oversampling LightGBM_Oversampling_Median Imputation_fold_3
104 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
105 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
106 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
107 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
108 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
109 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
110 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
111 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
112 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
113 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
114 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
115 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
116 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
117 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
118 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
119 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
120 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
121 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
122 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
123 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
124 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
125 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
126 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
127 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
128 Oversampling LightGBM_Oversampling_Median Imputation_fold_4
129 Oversampling LightGBM_Oversampling_Median Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 LightGBM 0.651304 NaN Zero Imputation
1 LightGBM 0.109274 NaN Zero Imputation
2 LightGBM 0.641350 NaN Zero Imputation
3 LightGBM 0.186732 NaN Zero Imputation
4 LightGBM 0.683991 NaN Zero Imputation
5 LightGBM 0.316182 NaN Zero Imputation
6 LightGBM 0.367982 NaN Zero Imputation
7 LightGBM NaN gbdt Zero Imputation
8 LightGBM NaN None Zero Imputation
9 LightGBM NaN 1.0 Zero Imputation
10 LightGBM NaN split Zero Imputation
11 LightGBM NaN 0.1 Zero Imputation
12 LightGBM NaN -1 Zero Imputation
13 LightGBM NaN 20 Zero Imputation
14 LightGBM NaN 0.001 Zero Imputation
15 LightGBM NaN 0.0 Zero Imputation
16 LightGBM NaN 100 Zero Imputation
17 LightGBM NaN None Zero Imputation
18 LightGBM NaN 31 Zero Imputation
19 LightGBM NaN None Zero Imputation
20 LightGBM NaN None Zero Imputation
21 LightGBM NaN 0.0 Zero Imputation
22 LightGBM NaN 0.0 Zero Imputation
23 LightGBM NaN 1.0 Zero Imputation
24 LightGBM NaN 200000 Zero Imputation
25 LightGBM NaN 0 Zero Imputation
26 LightGBM 0.608112 NaN Zero Imputation
27 LightGBM 0.075096 NaN Zero Imputation
28 LightGBM 0.713415 NaN Zero Imputation
29 LightGBM 0.135889 NaN Zero Imputation
30 LightGBM 0.698543 NaN Zero Imputation
31 LightGBM 0.325059 NaN Zero Imputation
32 LightGBM 0.397087 NaN Zero Imputation
33 LightGBM NaN gbdt Zero Imputation
34 LightGBM NaN None Zero Imputation
35 LightGBM NaN 1.0 Zero Imputation
36 LightGBM NaN split Zero Imputation
37 LightGBM NaN 0.1 Zero Imputation
38 LightGBM NaN -1 Zero Imputation
39 LightGBM NaN 20 Zero Imputation
40 LightGBM NaN 0.001 Zero Imputation
41 LightGBM NaN 0.0 Zero Imputation
42 LightGBM NaN 100 Zero Imputation
43 LightGBM NaN None Zero Imputation
44 LightGBM NaN 31 Zero Imputation
45 LightGBM NaN None Zero Imputation
46 LightGBM NaN None Zero Imputation
47 LightGBM NaN 0.0 Zero Imputation
48 LightGBM NaN 0.0 Zero Imputation
49 LightGBM NaN 1.0 Zero Imputation
50 LightGBM NaN 200000 Zero Imputation
51 LightGBM NaN 0 Zero Imputation
52 LightGBM 0.621017 NaN Zero Imputation
53 LightGBM 0.082667 NaN Zero Imputation
54 LightGBM 0.663102 NaN Zero Imputation
55 LightGBM 0.147007 NaN Zero Imputation
56 LightGBM 0.681124 NaN Zero Imputation
57 LightGBM 0.304690 NaN Zero Imputation
58 LightGBM 0.362248 NaN Zero Imputation
59 LightGBM NaN gbdt Zero Imputation
60 LightGBM NaN None Zero Imputation
61 LightGBM NaN 1.0 Zero Imputation
62 LightGBM NaN split Zero Imputation
63 LightGBM NaN 0.1 Zero Imputation
64 LightGBM NaN -1 Zero Imputation
65 LightGBM NaN 20 Zero Imputation
66 LightGBM NaN 0.001 Zero Imputation
67 LightGBM NaN 0.0 Zero Imputation
68 LightGBM NaN 100 Zero Imputation
69 LightGBM NaN None Zero Imputation
70 LightGBM NaN 31 Zero Imputation
71 LightGBM NaN None Zero Imputation
72 LightGBM NaN None Zero Imputation
73 LightGBM NaN 0.0 Zero Imputation
74 LightGBM NaN 0.0 Zero Imputation
75 LightGBM NaN 1.0 Zero Imputation
76 LightGBM NaN 200000 Zero Imputation
77 LightGBM NaN 0 Zero Imputation
78 LightGBM 0.635142 NaN Zero Imputation
79 LightGBM 0.083392 NaN Zero Imputation
80 LightGBM 0.607143 NaN Zero Imputation
81 LightGBM 0.146642 NaN Zero Imputation
82 LightGBM 0.661387 NaN Zero Imputation
83 LightGBM 0.278696 NaN Zero Imputation
84 LightGBM 0.322775 NaN Zero Imputation
85 LightGBM NaN gbdt Zero Imputation
86 LightGBM NaN None Zero Imputation
87 LightGBM NaN 1.0 Zero Imputation
88 LightGBM NaN split Zero Imputation
89 LightGBM NaN 0.1 Zero Imputation
90 LightGBM NaN -1 Zero Imputation
91 LightGBM NaN 20 Zero Imputation
92 LightGBM NaN 0.001 Zero Imputation
93 LightGBM NaN 0.0 Zero Imputation
94 LightGBM NaN 100 Zero Imputation
95 LightGBM NaN None Zero Imputation
96 LightGBM NaN 31 Zero Imputation
97 LightGBM NaN None Zero Imputation
98 LightGBM NaN None Zero Imputation
99 LightGBM NaN 0.0 Zero Imputation
100 LightGBM NaN 0.0 Zero Imputation
101 LightGBM NaN 1.0 Zero Imputation
102 LightGBM NaN 200000 Zero Imputation
103 LightGBM NaN 0 Zero Imputation
104 LightGBM 0.637513 NaN Zero Imputation
105 LightGBM 0.093268 NaN Zero Imputation
106 LightGBM 0.615741 NaN Zero Imputation
107 LightGBM 0.161998 NaN Zero Imputation
108 LightGBM 0.676729 NaN Zero Imputation
109 LightGBM 0.272434 NaN Zero Imputation
110 LightGBM 0.353458 NaN Zero Imputation
111 LightGBM NaN gbdt Zero Imputation
112 LightGBM NaN None Zero Imputation
113 LightGBM NaN 1.0 Zero Imputation
114 LightGBM NaN split Zero Imputation
115 LightGBM NaN 0.1 Zero Imputation
116 LightGBM NaN -1 Zero Imputation
117 LightGBM NaN 20 Zero Imputation
118 LightGBM NaN 0.001 Zero Imputation
119 LightGBM NaN 0.0 Zero Imputation
120 LightGBM NaN 100 Zero Imputation
121 LightGBM NaN None Zero Imputation
122 LightGBM NaN 31 Zero Imputation
123 LightGBM NaN None Zero Imputation
124 LightGBM NaN None Zero Imputation
125 LightGBM NaN 0.0 Zero Imputation
126 LightGBM NaN 0.0 Zero Imputation
127 LightGBM NaN 1.0 Zero Imputation
128 LightGBM NaN 200000 Zero Imputation
129 LightGBM NaN 0 Zero Imputation
Imbalance Class Technique Model Unique Code
0 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
1 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
2 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
3 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
4 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
5 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
6 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
7 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
8 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
9 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
10 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
11 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
12 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
13 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
14 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
15 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
16 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
17 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
18 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
19 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
20 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
21 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
22 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
23 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
24 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
25 Undersampling LightGBM_Undersampling_Zero Imputation_fold_0
26 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
27 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
28 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
29 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
30 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
31 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
32 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
33 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
34 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
35 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
36 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
37 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
38 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
39 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
40 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
41 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
42 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
43 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
44 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
45 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
46 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
47 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
48 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
49 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
50 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
51 Undersampling LightGBM_Undersampling_Zero Imputation_fold_1
52 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
53 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
54 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
55 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
56 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
57 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
58 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
59 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
60 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
61 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
62 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
63 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
64 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
65 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
66 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
67 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
68 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
69 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
70 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
71 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
72 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
73 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
74 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
75 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
76 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
77 Undersampling LightGBM_Undersampling_Zero Imputation_fold_2
78 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
79 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
80 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
81 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
82 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
83 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
84 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
85 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
86 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
87 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
88 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
89 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
90 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
91 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
92 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
93 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
94 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
95 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
96 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
97 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
98 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
99 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
100 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
101 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
102 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
103 Undersampling LightGBM_Undersampling_Zero Imputation_fold_3
104 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
105 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
106 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
107 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
108 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
109 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
110 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
111 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
112 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
113 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
114 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
115 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
116 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
117 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
118 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
119 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
120 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
121 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
122 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
123 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
124 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
125 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
126 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
127 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
128 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4
129 Undersampling LightGBM_Undersampling_Zero Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 LightGBM 0.648933 NaN Mean Imputation
1 LightGBM 0.109687 NaN Mean Imputation
2 LightGBM 0.649789 NaN Mean Imputation
3 LightGBM 0.187690 NaN Mean Imputation
4 LightGBM 0.690047 NaN Mean Imputation
5 LightGBM 0.320120 NaN Mean Imputation
6 LightGBM 0.380094 NaN Mean Imputation
7 LightGBM NaN gbdt Mean Imputation
8 LightGBM NaN None Mean Imputation
9 LightGBM NaN 1.0 Mean Imputation
10 LightGBM NaN split Mean Imputation
11 LightGBM NaN 0.1 Mean Imputation
12 LightGBM NaN -1 Mean Imputation
13 LightGBM NaN 20 Mean Imputation
14 LightGBM NaN 0.001 Mean Imputation
15 LightGBM NaN 0.0 Mean Imputation
16 LightGBM NaN 100 Mean Imputation
17 LightGBM NaN None Mean Imputation
18 LightGBM NaN 31 Mean Imputation
19 LightGBM NaN None Mean Imputation
20 LightGBM NaN None Mean Imputation
21 LightGBM NaN 0.0 Mean Imputation
22 LightGBM NaN 0.0 Mean Imputation
23 LightGBM NaN 1.0 Mean Imputation
24 LightGBM NaN 200000 Mean Imputation
25 LightGBM NaN 0 Mean Imputation
26 LightGBM 0.632341 NaN Mean Imputation
27 LightGBM 0.072816 NaN Mean Imputation
28 LightGBM 0.640244 NaN Mean Imputation
29 LightGBM 0.130760 NaN Mean Imputation
30 LightGBM 0.674177 NaN Mean Imputation
31 LightGBM 0.293255 NaN Mean Imputation
32 LightGBM 0.348353 NaN Mean Imputation
33 LightGBM NaN gbdt Mean Imputation
34 LightGBM NaN None Mean Imputation
35 LightGBM NaN 1.0 Mean Imputation
36 LightGBM NaN split Mean Imputation
37 LightGBM NaN 0.1 Mean Imputation
38 LightGBM NaN -1 Mean Imputation
39 LightGBM NaN 20 Mean Imputation
40 LightGBM NaN 0.001 Mean Imputation
41 LightGBM NaN 0.0 Mean Imputation
42 LightGBM NaN 100 Mean Imputation
43 LightGBM NaN None Mean Imputation
44 LightGBM NaN 31 Mean Imputation
45 LightGBM NaN None Mean Imputation
46 LightGBM NaN None Mean Imputation
47 LightGBM NaN 0.0 Mean Imputation
48 LightGBM NaN 0.0 Mean Imputation
49 LightGBM NaN 1.0 Mean Imputation
50 LightGBM NaN 200000 Mean Imputation
51 LightGBM NaN 0 Mean Imputation
52 LightGBM 0.625494 NaN Mean Imputation
53 LightGBM 0.085848 NaN Mean Imputation
54 LightGBM 0.684492 NaN Mean Imputation
55 LightGBM 0.152563 NaN Mean Imputation
56 LightGBM 0.693735 NaN Mean Imputation
57 LightGBM 0.313301 NaN Mean Imputation
58 LightGBM 0.387469 NaN Mean Imputation
59 LightGBM NaN gbdt Mean Imputation
60 LightGBM NaN None Mean Imputation
61 LightGBM NaN 1.0 Mean Imputation
62 LightGBM NaN split Mean Imputation
63 LightGBM NaN 0.1 Mean Imputation
64 LightGBM NaN -1 Mean Imputation
65 LightGBM NaN 20 Mean Imputation
66 LightGBM NaN 0.001 Mean Imputation
67 LightGBM NaN 0.0 Mean Imputation
68 LightGBM NaN 100 Mean Imputation
69 LightGBM NaN None Mean Imputation
70 LightGBM NaN 31 Mean Imputation
71 LightGBM NaN None Mean Imputation
72 LightGBM NaN None Mean Imputation
73 LightGBM NaN 0.0 Mean Imputation
74 LightGBM NaN 0.0 Mean Imputation
75 LightGBM NaN 1.0 Mean Imputation
76 LightGBM NaN 200000 Mean Imputation
77 LightGBM NaN 0 Mean Imputation
78 LightGBM 0.630927 NaN Mean Imputation
79 LightGBM 0.085911 NaN Mean Imputation
80 LightGBM 0.637755 NaN Mean Imputation
81 LightGBM 0.151423 NaN Mean Imputation
82 LightGBM 0.672191 NaN Mean Imputation
83 LightGBM 0.283951 NaN Mean Imputation
84 LightGBM 0.344382 NaN Mean Imputation
85 LightGBM NaN gbdt Mean Imputation
86 LightGBM NaN None Mean Imputation
87 LightGBM NaN 1.0 Mean Imputation
88 LightGBM NaN split Mean Imputation
89 LightGBM NaN 0.1 Mean Imputation
90 LightGBM NaN -1 Mean Imputation
91 LightGBM NaN 20 Mean Imputation
92 LightGBM NaN 0.001 Mean Imputation
93 LightGBM NaN 0.0 Mean Imputation
94 LightGBM NaN 100 Mean Imputation
95 LightGBM NaN None Mean Imputation
96 LightGBM NaN 31 Mean Imputation
97 LightGBM NaN None Mean Imputation
98 LightGBM NaN None Mean Imputation
99 LightGBM NaN 0.0 Mean Imputation
100 LightGBM NaN 0.0 Mean Imputation
101 LightGBM NaN 1.0 Mean Imputation
102 LightGBM NaN 200000 Mean Imputation
103 LightGBM NaN 0 Mean Imputation
104 LightGBM 0.661222 NaN Mean Imputation
105 LightGBM 0.101935 NaN Mean Imputation
106 LightGBM 0.634259 NaN Mean Imputation
107 LightGBM 0.175641 NaN Mean Imputation
108 LightGBM 0.700924 NaN Mean Imputation
109 LightGBM 0.300735 NaN Mean Imputation
110 LightGBM 0.401848 NaN Mean Imputation
111 LightGBM NaN gbdt Mean Imputation
112 LightGBM NaN None Mean Imputation
113 LightGBM NaN 1.0 Mean Imputation
114 LightGBM NaN split Mean Imputation
115 LightGBM NaN 0.1 Mean Imputation
116 LightGBM NaN -1 Mean Imputation
117 LightGBM NaN 20 Mean Imputation
118 LightGBM NaN 0.001 Mean Imputation
119 LightGBM NaN 0.0 Mean Imputation
120 LightGBM NaN 100 Mean Imputation
121 LightGBM NaN None Mean Imputation
122 LightGBM NaN 31 Mean Imputation
123 LightGBM NaN None Mean Imputation
124 LightGBM NaN None Mean Imputation
125 LightGBM NaN 0.0 Mean Imputation
126 LightGBM NaN 0.0 Mean Imputation
127 LightGBM NaN 1.0 Mean Imputation
128 LightGBM NaN 200000 Mean Imputation
129 LightGBM NaN 0 Mean Imputation
Imbalance Class Technique Model Unique Code
0 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
1 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
2 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
3 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
4 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
5 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
6 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
7 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
8 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
9 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
10 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
11 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
12 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
13 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
14 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
15 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
16 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
17 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
18 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
19 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
20 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
21 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
22 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
23 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
24 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
25 Undersampling LightGBM_Undersampling_Mean Imputation_fold_0
26 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
27 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
28 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
29 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
30 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
31 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
32 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
33 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
34 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
35 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
36 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
37 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
38 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
39 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
40 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
41 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
42 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
43 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
44 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
45 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
46 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
47 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
48 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
49 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
50 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
51 Undersampling LightGBM_Undersampling_Mean Imputation_fold_1
52 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
53 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
54 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
55 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
56 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
57 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
58 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
59 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
60 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
61 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
62 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
63 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
64 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
65 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
66 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
67 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
68 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
69 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
70 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
71 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
72 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
73 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
74 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
75 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
76 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
77 Undersampling LightGBM_Undersampling_Mean Imputation_fold_2
78 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
79 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
80 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
81 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
82 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
83 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
84 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
85 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
86 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
87 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
88 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
89 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
90 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
91 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
92 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
93 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
94 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
95 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
96 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
97 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
98 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
99 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
100 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
101 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
102 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
103 Undersampling LightGBM_Undersampling_Mean Imputation_fold_3
104 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
105 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
106 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
107 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
108 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
109 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
110 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
111 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
112 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
113 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
114 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
115 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
116 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
117 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
118 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
119 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
120 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
121 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
122 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
123 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
124 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
125 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
126 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
127 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
128 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4
129 Undersampling LightGBM_Undersampling_Mean Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 LightGBM 0.641559 NaN Median Imputation
1 LightGBM 0.104225 NaN Median Imputation
2 LightGBM 0.624473 NaN Median Imputation
3 LightGBM 0.178636 NaN Median Imputation
4 LightGBM 0.686368 NaN Median Imputation
5 LightGBM 0.291593 NaN Median Imputation
6 LightGBM 0.372735 NaN Median Imputation
7 LightGBM NaN gbdt Median Imputation
8 LightGBM NaN None Median Imputation
9 LightGBM NaN 1.0 Median Imputation
10 LightGBM NaN split Median Imputation
11 LightGBM NaN 0.1 Median Imputation
12 LightGBM NaN -1 Median Imputation
13 LightGBM NaN 20 Median Imputation
14 LightGBM NaN 0.001 Median Imputation
15 LightGBM NaN 0.0 Median Imputation
16 LightGBM NaN 100 Median Imputation
17 LightGBM NaN None Median Imputation
18 LightGBM NaN 31 Median Imputation
19 LightGBM NaN None Median Imputation
20 LightGBM NaN None Median Imputation
21 LightGBM NaN 0.0 Median Imputation
22 LightGBM NaN 0.0 Median Imputation
23 LightGBM NaN 1.0 Median Imputation
24 LightGBM NaN 200000 Median Imputation
25 LightGBM NaN 0 Median Imputation
26 LightGBM 0.632078 NaN Median Imputation
27 LightGBM 0.073356 NaN Median Imputation
28 LightGBM 0.646341 NaN Median Imputation
29 LightGBM 0.131759 NaN Median Imputation
30 LightGBM 0.695265 NaN Median Imputation
31 LightGBM 0.324257 NaN Median Imputation
32 LightGBM 0.390531 NaN Median Imputation
33 LightGBM NaN gbdt Median Imputation
34 LightGBM NaN None Median Imputation
35 LightGBM NaN 1.0 Median Imputation
36 LightGBM NaN split Median Imputation
37 LightGBM NaN 0.1 Median Imputation
38 LightGBM NaN -1 Median Imputation
39 LightGBM NaN 20 Median Imputation
40 LightGBM NaN 0.001 Median Imputation
41 LightGBM NaN 0.0 Median Imputation
42 LightGBM NaN 100 Median Imputation
43 LightGBM NaN None Median Imputation
44 LightGBM NaN 31 Median Imputation
45 LightGBM NaN None Median Imputation
46 LightGBM NaN None Median Imputation
47 LightGBM NaN 0.0 Median Imputation
48 LightGBM NaN 0.0 Median Imputation
49 LightGBM NaN 1.0 Median Imputation
50 LightGBM NaN 200000 Median Imputation
51 LightGBM NaN 0 Median Imputation
52 LightGBM 0.637082 NaN Median Imputation
53 LightGBM 0.080338 NaN Median Imputation
54 LightGBM 0.609626 NaN Median Imputation
55 LightGBM 0.141968 NaN Median Imputation
56 LightGBM 0.662575 NaN Median Imputation
57 LightGBM 0.260352 NaN Median Imputation
58 LightGBM 0.325150 NaN Median Imputation
59 LightGBM NaN gbdt Median Imputation
60 LightGBM NaN None Median Imputation
61 LightGBM NaN 1.0 Median Imputation
62 LightGBM NaN split Median Imputation
63 LightGBM NaN 0.1 Median Imputation
64 LightGBM NaN -1 Median Imputation
65 LightGBM NaN 20 Median Imputation
66 LightGBM NaN 0.001 Median Imputation
67 LightGBM NaN 0.0 Median Imputation
68 LightGBM NaN 100 Median Imputation
69 LightGBM NaN None Median Imputation
70 LightGBM NaN 31 Median Imputation
71 LightGBM NaN None Median Imputation
72 LightGBM NaN None Median Imputation
73 LightGBM NaN 0.0 Median Imputation
74 LightGBM NaN 0.0 Median Imputation
75 LightGBM NaN 1.0 Median Imputation
76 LightGBM NaN 200000 Median Imputation
77 LightGBM NaN 0 Median Imputation
78 LightGBM 0.625132 NaN Median Imputation
79 LightGBM 0.081800 NaN Median Imputation
80 LightGBM 0.612245 NaN Median Imputation
81 LightGBM 0.144317 NaN Median Imputation
82 LightGBM 0.674609 NaN Median Imputation
83 LightGBM 0.277749 NaN Median Imputation
84 LightGBM 0.349218 NaN Median Imputation
85 LightGBM NaN gbdt Median Imputation
86 LightGBM NaN None Median Imputation
87 LightGBM NaN 1.0 Median Imputation
88 LightGBM NaN split Median Imputation
89 LightGBM NaN 0.1 Median Imputation
90 LightGBM NaN -1 Median Imputation
91 LightGBM NaN 20 Median Imputation
92 LightGBM NaN 0.001 Median Imputation
93 LightGBM NaN 0.0 Median Imputation
94 LightGBM NaN 100 Median Imputation
95 LightGBM NaN None Median Imputation
96 LightGBM NaN 31 Median Imputation
97 LightGBM NaN None Median Imputation
98 LightGBM NaN None Median Imputation
99 LightGBM NaN 0.0 Median Imputation
100 LightGBM NaN 0.0 Median Imputation
101 LightGBM NaN 1.0 Median Imputation
102 LightGBM NaN 200000 Median Imputation
103 LightGBM NaN 0 Median Imputation
104 LightGBM 0.639621 NaN Median Imputation
105 LightGBM 0.098886 NaN Median Imputation
106 LightGBM 0.657407 NaN Median Imputation
107 LightGBM 0.171913 NaN Median Imputation
108 LightGBM 0.699186 NaN Median Imputation
109 LightGBM 0.315849 NaN Median Imputation
110 LightGBM 0.398372 NaN Median Imputation
111 LightGBM NaN gbdt Median Imputation
112 LightGBM NaN None Median Imputation
113 LightGBM NaN 1.0 Median Imputation
114 LightGBM NaN split Median Imputation
115 LightGBM NaN 0.1 Median Imputation
116 LightGBM NaN -1 Median Imputation
117 LightGBM NaN 20 Median Imputation
118 LightGBM NaN 0.001 Median Imputation
119 LightGBM NaN 0.0 Median Imputation
120 LightGBM NaN 100 Median Imputation
121 LightGBM NaN None Median Imputation
122 LightGBM NaN 31 Median Imputation
123 LightGBM NaN None Median Imputation
124 LightGBM NaN None Median Imputation
125 LightGBM NaN 0.0 Median Imputation
126 LightGBM NaN 0.0 Median Imputation
127 LightGBM NaN 1.0 Median Imputation
128 LightGBM NaN 200000 Median Imputation
129 LightGBM NaN 0 Median Imputation
Imbalance Class Technique Model Unique Code
0 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
1 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
2 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
3 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
4 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
5 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
6 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
7 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
8 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
9 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
10 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
11 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
12 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
13 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
14 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
15 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
16 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
17 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
18 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
19 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
20 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
21 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
22 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
23 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
24 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
25 Undersampling LightGBM_Undersampling_Median Imputation_fold_0
26 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
27 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
28 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
29 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
30 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
31 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
32 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
33 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
34 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
35 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
36 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
37 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
38 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
39 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
40 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
41 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
42 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
43 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
44 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
45 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
46 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
47 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
48 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
49 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
50 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
51 Undersampling LightGBM_Undersampling_Median Imputation_fold_1
52 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
53 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
54 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
55 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
56 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
57 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
58 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
59 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
60 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
61 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
62 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
63 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
64 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
65 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
66 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
67 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
68 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
69 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
70 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
71 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
72 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
73 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
74 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
75 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
76 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
77 Undersampling LightGBM_Undersampling_Median Imputation_fold_2
78 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
79 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
80 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
81 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
82 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
83 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
84 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
85 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
86 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
87 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
88 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
89 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
90 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
91 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
92 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
93 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
94 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
95 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
96 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
97 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
98 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
99 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
100 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
101 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
102 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
103 Undersampling LightGBM_Undersampling_Median Imputation_fold_3
104 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
105 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
106 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
107 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
108 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
109 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
110 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
111 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
112 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
113 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
114 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
115 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
116 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
117 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
118 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
119 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
120 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
121 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
122 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
123 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
124 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
125 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
126 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
127 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
128 Undersampling LightGBM_Undersampling_Median Imputation_fold_4
129 Undersampling LightGBM_Undersampling_Median Imputation_fold_4 ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 LightGBM 0.877008 NaN Zero Imputation
1 LightGBM 0.147239 NaN Zero Imputation
2 LightGBM 0.202532 NaN Zero Imputation
3 LightGBM 0.170515 NaN Zero Imputation
4 LightGBM 0.684273 NaN Zero Imputation
5 LightGBM 0.297033 NaN Zero Imputation
6 LightGBM 0.368546 NaN Zero Imputation
7 LightGBM NaN gbdt Zero Imputation
8 LightGBM NaN None Zero Imputation
9 LightGBM NaN 1.0 Zero Imputation
10 LightGBM NaN split Zero Imputation
11 LightGBM NaN 0.1 Zero Imputation
12 LightGBM NaN -1 Zero Imputation
13 LightGBM NaN 20 Zero Imputation
14 LightGBM NaN 0.001 Zero Imputation
15 LightGBM NaN 0.0 Zero Imputation
16 LightGBM NaN 100 Zero Imputation
17 LightGBM NaN None Zero Imputation
18 LightGBM NaN 31 Zero Imputation
19 LightGBM NaN None Zero Imputation
20 LightGBM NaN None Zero Imputation
21 LightGBM NaN 0.0 Zero Imputation
22 LightGBM NaN 0.0 Zero Imputation
23 LightGBM NaN 1.0 Zero Imputation
24 LightGBM NaN 200000 Zero Imputation
25 LightGBM NaN 0 Zero Imputation
26 LightGBM 0.890703 NaN Zero Imputation
27 LightGBM 0.111455 NaN Zero Imputation
28 LightGBM 0.219512 NaN Zero Imputation
29 LightGBM 0.147844 NaN Zero Imputation
30 LightGBM 0.709188 NaN Zero Imputation
31 LightGBM 0.342264 NaN Zero Imputation
32 LightGBM 0.418377 NaN Zero Imputation
33 LightGBM NaN gbdt Zero Imputation
34 LightGBM NaN None Zero Imputation
35 LightGBM NaN 1.0 Zero Imputation
36 LightGBM NaN split Zero Imputation
37 LightGBM NaN 0.1 Zero Imputation
38 LightGBM NaN -1 Zero Imputation
39 LightGBM NaN 20 Zero Imputation
40 LightGBM NaN 0.001 Zero Imputation
41 LightGBM NaN 0.0 Zero Imputation
42 LightGBM NaN 100 Zero Imputation
43 LightGBM NaN None Zero Imputation
44 LightGBM NaN 31 Zero Imputation
45 LightGBM NaN None Zero Imputation
46 LightGBM NaN None Zero Imputation
47 LightGBM NaN 0.0 Zero Imputation
48 LightGBM NaN 0.0 Zero Imputation
49 LightGBM NaN 1.0 Zero Imputation
50 LightGBM NaN 200000 Zero Imputation
51 LightGBM NaN 0 Zero Imputation
52 LightGBM 0.885963 NaN Zero Imputation
53 LightGBM 0.105769 NaN Zero Imputation
54 LightGBM 0.176471 NaN Zero Imputation
55 LightGBM 0.132265 NaN Zero Imputation
56 LightGBM 0.676592 NaN Zero Imputation
57 LightGBM 0.265919 NaN Zero Imputation
58 LightGBM 0.353184 NaN Zero Imputation
59 LightGBM NaN gbdt Zero Imputation
60 LightGBM NaN None Zero Imputation
61 LightGBM NaN 1.0 Zero Imputation
62 LightGBM NaN split Zero Imputation
63 LightGBM NaN 0.1 Zero Imputation
64 LightGBM NaN -1 Zero Imputation
65 LightGBM NaN 20 Zero Imputation
66 LightGBM NaN 0.001 Zero Imputation
67 LightGBM NaN 0.0 Zero Imputation
68 LightGBM NaN 100 Zero Imputation
69 LightGBM NaN None Zero Imputation
70 LightGBM NaN 31 Zero Imputation
71 LightGBM NaN None Zero Imputation
72 LightGBM NaN None Zero Imputation
73 LightGBM NaN 0.0 Zero Imputation
74 LightGBM NaN 0.0 Zero Imputation
75 LightGBM NaN 1.0 Zero Imputation
76 LightGBM NaN 200000 Zero Imputation
77 LightGBM NaN 0 Zero Imputation
78 LightGBM 0.880927 NaN Zero Imputation
79 LightGBM 0.127907 NaN Zero Imputation
80 LightGBM 0.224490 NaN Zero Imputation
81 LightGBM 0.162963 NaN Zero Imputation
82 LightGBM 0.673694 NaN Zero Imputation
83 LightGBM 0.276706 NaN Zero Imputation
84 LightGBM 0.347388 NaN Zero Imputation
85 LightGBM NaN gbdt Zero Imputation
86 LightGBM NaN None Zero Imputation
87 LightGBM NaN 1.0 Zero Imputation
88 LightGBM NaN split Zero Imputation
89 LightGBM NaN 0.1 Zero Imputation
90 LightGBM NaN -1 Zero Imputation
91 LightGBM NaN 20 Zero Imputation
92 LightGBM NaN 0.001 Zero Imputation
93 LightGBM NaN 0.0 Zero Imputation
94 LightGBM NaN 100 Zero Imputation
95 LightGBM NaN None Zero Imputation
96 LightGBM NaN 31 Zero Imputation
97 LightGBM NaN None Zero Imputation
98 LightGBM NaN None Zero Imputation
99 LightGBM NaN 0.0 Zero Imputation
100 LightGBM NaN 0.0 Zero Imputation
101 LightGBM NaN 1.0 Zero Imputation
102 LightGBM NaN 200000 Zero Imputation
103 LightGBM NaN 0 Zero Imputation
104 LightGBM 0.885406 NaN Zero Imputation
105 LightGBM 0.136213 NaN Zero Imputation
106 LightGBM 0.189815 NaN Zero Imputation
107 LightGBM 0.158607 NaN Zero Imputation
108 LightGBM 0.696215 NaN Zero Imputation
109 LightGBM 0.294015 NaN Zero Imputation
110 LightGBM 0.392430 NaN Zero Imputation
111 LightGBM NaN gbdt Zero Imputation
112 LightGBM NaN None Zero Imputation
113 LightGBM NaN 1.0 Zero Imputation
114 LightGBM NaN split Zero Imputation
115 LightGBM NaN 0.1 Zero Imputation
116 LightGBM NaN -1 Zero Imputation
117 LightGBM NaN 20 Zero Imputation
118 LightGBM NaN 0.001 Zero Imputation
119 LightGBM NaN 0.0 Zero Imputation
120 LightGBM NaN 100 Zero Imputation
121 LightGBM NaN None Zero Imputation
122 LightGBM NaN 31 Zero Imputation
123 LightGBM NaN None Zero Imputation
124 LightGBM NaN None Zero Imputation
125 LightGBM NaN 0.0 Zero Imputation
126 LightGBM NaN 0.0 Zero Imputation
127 LightGBM NaN 1.0 Zero Imputation
128 LightGBM NaN 200000 Zero Imputation
129 LightGBM NaN 0 Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
95 Hybrid Sampling (SMOTEENN)
96 Hybrid Sampling (SMOTEENN)
97 Hybrid Sampling (SMOTEENN)
98 Hybrid Sampling (SMOTEENN)
99 Hybrid Sampling (SMOTEENN)
100 Hybrid Sampling (SMOTEENN)
101 Hybrid Sampling (SMOTEENN)
102 Hybrid Sampling (SMOTEENN)
103 Hybrid Sampling (SMOTEENN)
104 Hybrid Sampling (SMOTEENN)
105 Hybrid Sampling (SMOTEENN)
106 Hybrid Sampling (SMOTEENN)
107 Hybrid Sampling (SMOTEENN)
108 Hybrid Sampling (SMOTEENN)
109 Hybrid Sampling (SMOTEENN)
110 Hybrid Sampling (SMOTEENN)
111 Hybrid Sampling (SMOTEENN)
112 Hybrid Sampling (SMOTEENN)
113 Hybrid Sampling (SMOTEENN)
114 Hybrid Sampling (SMOTEENN)
115 Hybrid Sampling (SMOTEENN)
116 Hybrid Sampling (SMOTEENN)
117 Hybrid Sampling (SMOTEENN)
118 Hybrid Sampling (SMOTEENN)
119 Hybrid Sampling (SMOTEENN)
120 Hybrid Sampling (SMOTEENN)
121 Hybrid Sampling (SMOTEENN)
122 Hybrid Sampling (SMOTEENN)
123 Hybrid Sampling (SMOTEENN)
124 Hybrid Sampling (SMOTEENN)
125 Hybrid Sampling (SMOTEENN)
126 Hybrid Sampling (SMOTEENN)
127 Hybrid Sampling (SMOTEENN)
128 Hybrid Sampling (SMOTEENN)
129 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
1 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
2 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
3 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
4 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
5 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
6 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
7 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
8 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
9 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
10 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
11 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
12 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
13 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
14 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
15 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
16 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
17 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
18 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
19 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
20 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
21 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
22 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
23 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
24 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
25 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
26 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
27 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
28 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
29 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
30 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
31 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
32 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
33 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
34 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
35 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
36 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
37 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
38 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
39 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
40 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
41 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
42 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
43 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
44 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
45 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
46 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
47 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
48 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
49 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
50 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
51 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
52 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
53 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
54 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
55 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
56 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
57 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
58 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
59 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
60 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
61 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
62 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
63 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
64 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
65 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
66 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
67 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
68 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
69 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
70 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
71 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
72 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
73 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
74 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
75 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
76 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
77 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
78 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
79 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
80 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
81 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
82 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
83 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
84 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
85 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
86 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
87 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
88 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
89 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
90 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
91 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
92 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
93 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
94 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
95 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
96 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
97 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
98 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
99 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
100 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
101 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
102 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
103 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
104 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
105 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
106 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
107 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
108 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
109 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
110 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
111 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
112 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
113 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
114 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
115 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
116 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
117 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
118 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
119 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
120 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
121 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
122 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
123 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
124 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
125 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
126 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
127 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
128 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput...
129 LightGBM_Hybrid Sampling (SMOTEENN)_Zero Imput... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 LightGBM 0.886753 NaN Mean Imputation
1 LightGBM 0.146520 NaN Mean Imputation
2 LightGBM 0.168776 NaN Mean Imputation
3 LightGBM 0.156863 NaN Mean Imputation
4 LightGBM 0.669609 NaN Mean Imputation
5 LightGBM 0.249951 NaN Mean Imputation
6 LightGBM 0.339218 NaN Mean Imputation
7 LightGBM NaN gbdt Mean Imputation
8 LightGBM NaN None Mean Imputation
9 LightGBM NaN 1.0 Mean Imputation
10 LightGBM NaN split Mean Imputation
11 LightGBM NaN 0.1 Mean Imputation
12 LightGBM NaN -1 Mean Imputation
13 LightGBM NaN 20 Mean Imputation
14 LightGBM NaN 0.001 Mean Imputation
15 LightGBM NaN 0.0 Mean Imputation
16 LightGBM NaN 100 Mean Imputation
17 LightGBM NaN None Mean Imputation
18 LightGBM NaN 31 Mean Imputation
19 LightGBM NaN None Mean Imputation
20 LightGBM NaN None Mean Imputation
21 LightGBM NaN 0.0 Mean Imputation
22 LightGBM NaN 0.0 Mean Imputation
23 LightGBM NaN 1.0 Mean Imputation
24 LightGBM NaN 200000 Mean Imputation
25 LightGBM NaN 0 Mean Imputation
26 LightGBM 0.893073 NaN Mean Imputation
27 LightGBM 0.119497 NaN Mean Imputation
28 LightGBM 0.231707 NaN Mean Imputation
29 LightGBM 0.157676 NaN Mean Imputation
30 LightGBM 0.695066 NaN Mean Imputation
31 LightGBM 0.326407 NaN Mean Imputation
32 LightGBM 0.390131 NaN Mean Imputation
33 LightGBM NaN gbdt Mean Imputation
34 LightGBM NaN None Mean Imputation
35 LightGBM NaN 1.0 Mean Imputation
36 LightGBM NaN split Mean Imputation
37 LightGBM NaN 0.1 Mean Imputation
38 LightGBM NaN -1 Mean Imputation
39 LightGBM NaN 20 Mean Imputation
40 LightGBM NaN 0.001 Mean Imputation
41 LightGBM NaN 0.0 Mean Imputation
42 LightGBM NaN 100 Mean Imputation
43 LightGBM NaN None Mean Imputation
44 LightGBM NaN 31 Mean Imputation
45 LightGBM NaN None Mean Imputation
46 LightGBM NaN None Mean Imputation
47 LightGBM NaN 0.0 Mean Imputation
48 LightGBM NaN 0.0 Mean Imputation
49 LightGBM NaN 1.0 Mean Imputation
50 LightGBM NaN 200000 Mean Imputation
51 LightGBM NaN 0 Mean Imputation
52 LightGBM 0.885699 NaN Mean Imputation
53 LightGBM 0.120000 NaN Mean Imputation
54 LightGBM 0.208556 NaN Mean Imputation
55 LightGBM 0.152344 NaN Mean Imputation
56 LightGBM 0.665173 NaN Mean Imputation
57 LightGBM 0.258015 NaN Mean Imputation
58 LightGBM 0.330346 NaN Mean Imputation
59 LightGBM NaN gbdt Mean Imputation
60 LightGBM NaN None Mean Imputation
61 LightGBM NaN 1.0 Mean Imputation
62 LightGBM NaN split Mean Imputation
63 LightGBM NaN 0.1 Mean Imputation
64 LightGBM NaN -1 Mean Imputation
65 LightGBM NaN 20 Mean Imputation
66 LightGBM NaN 0.001 Mean Imputation
67 LightGBM NaN 0.0 Mean Imputation
68 LightGBM NaN 100 Mean Imputation
69 LightGBM NaN None Mean Imputation
70 LightGBM NaN 31 Mean Imputation
71 LightGBM NaN None Mean Imputation
72 LightGBM NaN None Mean Imputation
73 LightGBM NaN 0.0 Mean Imputation
74 LightGBM NaN 0.0 Mean Imputation
75 LightGBM NaN 1.0 Mean Imputation
76 LightGBM NaN 200000 Mean Imputation
77 LightGBM NaN 0 Mean Imputation
78 LightGBM 0.896733 NaN Mean Imputation
79 LightGBM 0.114173 NaN Mean Imputation
80 LightGBM 0.147959 NaN Mean Imputation
81 LightGBM 0.128889 NaN Mean Imputation
82 LightGBM 0.669229 NaN Mean Imputation
83 LightGBM 0.274785 NaN Mean Imputation
84 LightGBM 0.338458 NaN Mean Imputation
85 LightGBM NaN gbdt Mean Imputation
86 LightGBM NaN None Mean Imputation
87 LightGBM NaN 1.0 Mean Imputation
88 LightGBM NaN split Mean Imputation
89 LightGBM NaN 0.1 Mean Imputation
90 LightGBM NaN -1 Mean Imputation
91 LightGBM NaN 20 Mean Imputation
92 LightGBM NaN 0.001 Mean Imputation
93 LightGBM NaN 0.0 Mean Imputation
94 LightGBM NaN 100 Mean Imputation
95 LightGBM NaN None Mean Imputation
96 LightGBM NaN 31 Mean Imputation
97 LightGBM NaN None Mean Imputation
98 LightGBM NaN None Mean Imputation
99 LightGBM NaN 0.0 Mean Imputation
100 LightGBM NaN 0.0 Mean Imputation
101 LightGBM NaN 1.0 Mean Imputation
102 LightGBM NaN 200000 Mean Imputation
103 LightGBM NaN 0 Mean Imputation
104 LightGBM 0.889884 NaN Mean Imputation
105 LightGBM 0.123134 NaN Mean Imputation
106 LightGBM 0.152778 NaN Mean Imputation
107 LightGBM 0.136364 NaN Mean Imputation
108 LightGBM 0.690170 NaN Mean Imputation
109 LightGBM 0.297134 NaN Mean Imputation
110 LightGBM 0.380341 NaN Mean Imputation
111 LightGBM NaN gbdt Mean Imputation
112 LightGBM NaN None Mean Imputation
113 LightGBM NaN 1.0 Mean Imputation
114 LightGBM NaN split Mean Imputation
115 LightGBM NaN 0.1 Mean Imputation
116 LightGBM NaN -1 Mean Imputation
117 LightGBM NaN 20 Mean Imputation
118 LightGBM NaN 0.001 Mean Imputation
119 LightGBM NaN 0.0 Mean Imputation
120 LightGBM NaN 100 Mean Imputation
121 LightGBM NaN None Mean Imputation
122 LightGBM NaN 31 Mean Imputation
123 LightGBM NaN None Mean Imputation
124 LightGBM NaN None Mean Imputation
125 LightGBM NaN 0.0 Mean Imputation
126 LightGBM NaN 0.0 Mean Imputation
127 LightGBM NaN 1.0 Mean Imputation
128 LightGBM NaN 200000 Mean Imputation
129 LightGBM NaN 0 Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
95 Hybrid Sampling (SMOTEENN)
96 Hybrid Sampling (SMOTEENN)
97 Hybrid Sampling (SMOTEENN)
98 Hybrid Sampling (SMOTEENN)
99 Hybrid Sampling (SMOTEENN)
100 Hybrid Sampling (SMOTEENN)
101 Hybrid Sampling (SMOTEENN)
102 Hybrid Sampling (SMOTEENN)
103 Hybrid Sampling (SMOTEENN)
104 Hybrid Sampling (SMOTEENN)
105 Hybrid Sampling (SMOTEENN)
106 Hybrid Sampling (SMOTEENN)
107 Hybrid Sampling (SMOTEENN)
108 Hybrid Sampling (SMOTEENN)
109 Hybrid Sampling (SMOTEENN)
110 Hybrid Sampling (SMOTEENN)
111 Hybrid Sampling (SMOTEENN)
112 Hybrid Sampling (SMOTEENN)
113 Hybrid Sampling (SMOTEENN)
114 Hybrid Sampling (SMOTEENN)
115 Hybrid Sampling (SMOTEENN)
116 Hybrid Sampling (SMOTEENN)
117 Hybrid Sampling (SMOTEENN)
118 Hybrid Sampling (SMOTEENN)
119 Hybrid Sampling (SMOTEENN)
120 Hybrid Sampling (SMOTEENN)
121 Hybrid Sampling (SMOTEENN)
122 Hybrid Sampling (SMOTEENN)
123 Hybrid Sampling (SMOTEENN)
124 Hybrid Sampling (SMOTEENN)
125 Hybrid Sampling (SMOTEENN)
126 Hybrid Sampling (SMOTEENN)
127 Hybrid Sampling (SMOTEENN)
128 Hybrid Sampling (SMOTEENN)
129 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
1 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
2 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
3 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
4 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
5 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
6 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
7 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
8 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
9 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
10 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
11 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
12 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
13 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
14 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
15 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
16 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
17 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
18 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
19 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
20 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
21 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
22 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
23 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
24 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
25 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
26 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
27 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
28 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
29 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
30 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
31 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
32 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
33 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
34 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
35 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
36 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
37 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
38 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
39 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
40 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
41 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
42 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
43 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
44 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
45 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
46 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
47 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
48 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
49 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
50 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
51 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
52 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
53 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
54 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
55 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
56 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
57 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
58 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
59 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
60 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
61 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
62 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
63 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
64 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
65 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
66 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
67 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
68 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
69 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
70 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
71 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
72 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
73 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
74 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
75 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
76 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
77 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
78 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
79 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
80 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
81 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
82 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
83 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
84 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
85 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
86 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
87 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
88 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
89 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
90 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
91 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
92 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
93 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
94 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
95 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
96 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
97 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
98 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
99 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
100 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
101 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
102 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
103 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
104 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
105 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
106 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
107 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
108 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
109 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
110 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
111 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
112 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
113 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
114 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
115 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
116 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
117 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
118 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
119 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
120 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
121 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
122 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
123 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
124 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
125 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
126 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
127 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
128 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput...
129 LightGBM_Hybrid Sampling (SMOTEENN)_Mean Imput... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 LightGBM 0.877798 NaN Median Imputation
1 LightGBM 0.130293 NaN Median Imputation
2 LightGBM 0.168776 NaN Median Imputation
3 LightGBM 0.147059 NaN Median Imputation
4 LightGBM 0.673820 NaN Median Imputation
5 LightGBM 0.269272 NaN Median Imputation
6 LightGBM 0.347640 NaN Median Imputation
7 LightGBM NaN gbdt Median Imputation
8 LightGBM NaN None Median Imputation
9 LightGBM NaN 1.0 Median Imputation
10 LightGBM NaN split Median Imputation
11 LightGBM NaN 0.1 Median Imputation
12 LightGBM NaN -1 Median Imputation
13 LightGBM NaN 20 Median Imputation
14 LightGBM NaN 0.001 Median Imputation
15 LightGBM NaN 0.0 Median Imputation
16 LightGBM NaN 100 Median Imputation
17 LightGBM NaN None Median Imputation
18 LightGBM NaN 31 Median Imputation
19 LightGBM NaN None Median Imputation
20 LightGBM NaN None Median Imputation
21 LightGBM NaN 0.0 Median Imputation
22 LightGBM NaN 0.0 Median Imputation
23 LightGBM NaN 1.0 Median Imputation
24 LightGBM NaN 200000 Median Imputation
25 LightGBM NaN 0 Median Imputation
26 LightGBM 0.894390 NaN Median Imputation
27 LightGBM 0.106312 NaN Median Imputation
28 LightGBM 0.195122 NaN Median Imputation
29 LightGBM 0.137634 NaN Median Imputation
30 LightGBM 0.696201 NaN Median Imputation
31 LightGBM 0.313278 NaN Median Imputation
32 LightGBM 0.392402 NaN Median Imputation
33 LightGBM NaN gbdt Median Imputation
34 LightGBM NaN None Median Imputation
35 LightGBM NaN 1.0 Median Imputation
36 LightGBM NaN split Median Imputation
37 LightGBM NaN 0.1 Median Imputation
38 LightGBM NaN -1 Median Imputation
39 LightGBM NaN 20 Median Imputation
40 LightGBM NaN 0.001 Median Imputation
41 LightGBM NaN 0.0 Median Imputation
42 LightGBM NaN 100 Median Imputation
43 LightGBM NaN None Median Imputation
44 LightGBM NaN 31 Median Imputation
45 LightGBM NaN None Median Imputation
46 LightGBM NaN None Median Imputation
47 LightGBM NaN 0.0 Median Imputation
48 LightGBM NaN 0.0 Median Imputation
49 LightGBM NaN 1.0 Median Imputation
50 LightGBM NaN 200000 Median Imputation
51 LightGBM NaN 0 Median Imputation
52 LightGBM 0.892283 NaN Median Imputation
53 LightGBM 0.114583 NaN Median Imputation
54 LightGBM 0.176471 NaN Median Imputation
55 LightGBM 0.138947 NaN Median Imputation
56 LightGBM 0.668333 NaN Median Imputation
57 LightGBM 0.267837 NaN Median Imputation
58 LightGBM 0.336666 NaN Median Imputation
59 LightGBM NaN gbdt Median Imputation
60 LightGBM NaN None Median Imputation
61 LightGBM NaN 1.0 Median Imputation
62 LightGBM NaN split Median Imputation
63 LightGBM NaN 0.1 Median Imputation
64 LightGBM NaN -1 Median Imputation
65 LightGBM NaN 20 Median Imputation
66 LightGBM NaN 0.001 Median Imputation
67 LightGBM NaN 0.0 Median Imputation
68 LightGBM NaN 100 Median Imputation
69 LightGBM NaN None Median Imputation
70 LightGBM NaN 31 Median Imputation
71 LightGBM NaN None Median Imputation
72 LightGBM NaN None Median Imputation
73 LightGBM NaN 0.0 Median Imputation
74 LightGBM NaN 0.0 Median Imputation
75 LightGBM NaN 1.0 Median Imputation
76 LightGBM NaN 200000 Median Imputation
77 LightGBM NaN 0 Median Imputation
78 LightGBM 0.890148 NaN Median Imputation
79 LightGBM 0.132890 NaN Median Imputation
80 LightGBM 0.204082 NaN Median Imputation
81 LightGBM 0.160966 NaN Median Imputation
82 LightGBM 0.659871 NaN Median Imputation
83 LightGBM 0.239592 NaN Median Imputation
84 LightGBM 0.319742 NaN Median Imputation
85 LightGBM NaN gbdt Median Imputation
86 LightGBM NaN None Median Imputation
87 LightGBM NaN 1.0 Median Imputation
88 LightGBM NaN split Median Imputation
89 LightGBM NaN 0.1 Median Imputation
90 LightGBM NaN -1 Median Imputation
91 LightGBM NaN 20 Median Imputation
92 LightGBM NaN 0.001 Median Imputation
93 LightGBM NaN 0.0 Median Imputation
94 LightGBM NaN 100 Median Imputation
95 LightGBM NaN None Median Imputation
96 LightGBM NaN 31 Median Imputation
97 LightGBM NaN None Median Imputation
98 LightGBM NaN None Median Imputation
99 LightGBM NaN 0.0 Median Imputation
100 LightGBM NaN 0.0 Median Imputation
101 LightGBM NaN 1.0 Median Imputation
102 LightGBM NaN 200000 Median Imputation
103 LightGBM NaN 0 Median Imputation
104 LightGBM 0.893309 NaN Median Imputation
105 LightGBM 0.153846 NaN Median Imputation
106 LightGBM 0.194444 NaN Median Imputation
107 LightGBM 0.171779 NaN Median Imputation
108 LightGBM 0.688048 NaN Median Imputation
109 LightGBM 0.305783 NaN Median Imputation
110 LightGBM 0.376097 NaN Median Imputation
111 LightGBM NaN gbdt Median Imputation
112 LightGBM NaN None Median Imputation
113 LightGBM NaN 1.0 Median Imputation
114 LightGBM NaN split Median Imputation
115 LightGBM NaN 0.1 Median Imputation
116 LightGBM NaN -1 Median Imputation
117 LightGBM NaN 20 Median Imputation
118 LightGBM NaN 0.001 Median Imputation
119 LightGBM NaN 0.0 Median Imputation
120 LightGBM NaN 100 Median Imputation
121 LightGBM NaN None Median Imputation
122 LightGBM NaN 31 Median Imputation
123 LightGBM NaN None Median Imputation
124 LightGBM NaN None Median Imputation
125 LightGBM NaN 0.0 Median Imputation
126 LightGBM NaN 0.0 Median Imputation
127 LightGBM NaN 1.0 Median Imputation
128 LightGBM NaN 200000 Median Imputation
129 LightGBM NaN 0 Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTEENN)
1 Hybrid Sampling (SMOTEENN)
2 Hybrid Sampling (SMOTEENN)
3 Hybrid Sampling (SMOTEENN)
4 Hybrid Sampling (SMOTEENN)
5 Hybrid Sampling (SMOTEENN)
6 Hybrid Sampling (SMOTEENN)
7 Hybrid Sampling (SMOTEENN)
8 Hybrid Sampling (SMOTEENN)
9 Hybrid Sampling (SMOTEENN)
10 Hybrid Sampling (SMOTEENN)
11 Hybrid Sampling (SMOTEENN)
12 Hybrid Sampling (SMOTEENN)
13 Hybrid Sampling (SMOTEENN)
14 Hybrid Sampling (SMOTEENN)
15 Hybrid Sampling (SMOTEENN)
16 Hybrid Sampling (SMOTEENN)
17 Hybrid Sampling (SMOTEENN)
18 Hybrid Sampling (SMOTEENN)
19 Hybrid Sampling (SMOTEENN)
20 Hybrid Sampling (SMOTEENN)
21 Hybrid Sampling (SMOTEENN)
22 Hybrid Sampling (SMOTEENN)
23 Hybrid Sampling (SMOTEENN)
24 Hybrid Sampling (SMOTEENN)
25 Hybrid Sampling (SMOTEENN)
26 Hybrid Sampling (SMOTEENN)
27 Hybrid Sampling (SMOTEENN)
28 Hybrid Sampling (SMOTEENN)
29 Hybrid Sampling (SMOTEENN)
30 Hybrid Sampling (SMOTEENN)
31 Hybrid Sampling (SMOTEENN)
32 Hybrid Sampling (SMOTEENN)
33 Hybrid Sampling (SMOTEENN)
34 Hybrid Sampling (SMOTEENN)
35 Hybrid Sampling (SMOTEENN)
36 Hybrid Sampling (SMOTEENN)
37 Hybrid Sampling (SMOTEENN)
38 Hybrid Sampling (SMOTEENN)
39 Hybrid Sampling (SMOTEENN)
40 Hybrid Sampling (SMOTEENN)
41 Hybrid Sampling (SMOTEENN)
42 Hybrid Sampling (SMOTEENN)
43 Hybrid Sampling (SMOTEENN)
44 Hybrid Sampling (SMOTEENN)
45 Hybrid Sampling (SMOTEENN)
46 Hybrid Sampling (SMOTEENN)
47 Hybrid Sampling (SMOTEENN)
48 Hybrid Sampling (SMOTEENN)
49 Hybrid Sampling (SMOTEENN)
50 Hybrid Sampling (SMOTEENN)
51 Hybrid Sampling (SMOTEENN)
52 Hybrid Sampling (SMOTEENN)
53 Hybrid Sampling (SMOTEENN)
54 Hybrid Sampling (SMOTEENN)
55 Hybrid Sampling (SMOTEENN)
56 Hybrid Sampling (SMOTEENN)
57 Hybrid Sampling (SMOTEENN)
58 Hybrid Sampling (SMOTEENN)
59 Hybrid Sampling (SMOTEENN)
60 Hybrid Sampling (SMOTEENN)
61 Hybrid Sampling (SMOTEENN)
62 Hybrid Sampling (SMOTEENN)
63 Hybrid Sampling (SMOTEENN)
64 Hybrid Sampling (SMOTEENN)
65 Hybrid Sampling (SMOTEENN)
66 Hybrid Sampling (SMOTEENN)
67 Hybrid Sampling (SMOTEENN)
68 Hybrid Sampling (SMOTEENN)
69 Hybrid Sampling (SMOTEENN)
70 Hybrid Sampling (SMOTEENN)
71 Hybrid Sampling (SMOTEENN)
72 Hybrid Sampling (SMOTEENN)
73 Hybrid Sampling (SMOTEENN)
74 Hybrid Sampling (SMOTEENN)
75 Hybrid Sampling (SMOTEENN)
76 Hybrid Sampling (SMOTEENN)
77 Hybrid Sampling (SMOTEENN)
78 Hybrid Sampling (SMOTEENN)
79 Hybrid Sampling (SMOTEENN)
80 Hybrid Sampling (SMOTEENN)
81 Hybrid Sampling (SMOTEENN)
82 Hybrid Sampling (SMOTEENN)
83 Hybrid Sampling (SMOTEENN)
84 Hybrid Sampling (SMOTEENN)
85 Hybrid Sampling (SMOTEENN)
86 Hybrid Sampling (SMOTEENN)
87 Hybrid Sampling (SMOTEENN)
88 Hybrid Sampling (SMOTEENN)
89 Hybrid Sampling (SMOTEENN)
90 Hybrid Sampling (SMOTEENN)
91 Hybrid Sampling (SMOTEENN)
92 Hybrid Sampling (SMOTEENN)
93 Hybrid Sampling (SMOTEENN)
94 Hybrid Sampling (SMOTEENN)
95 Hybrid Sampling (SMOTEENN)
96 Hybrid Sampling (SMOTEENN)
97 Hybrid Sampling (SMOTEENN)
98 Hybrid Sampling (SMOTEENN)
99 Hybrid Sampling (SMOTEENN)
100 Hybrid Sampling (SMOTEENN)
101 Hybrid Sampling (SMOTEENN)
102 Hybrid Sampling (SMOTEENN)
103 Hybrid Sampling (SMOTEENN)
104 Hybrid Sampling (SMOTEENN)
105 Hybrid Sampling (SMOTEENN)
106 Hybrid Sampling (SMOTEENN)
107 Hybrid Sampling (SMOTEENN)
108 Hybrid Sampling (SMOTEENN)
109 Hybrid Sampling (SMOTEENN)
110 Hybrid Sampling (SMOTEENN)
111 Hybrid Sampling (SMOTEENN)
112 Hybrid Sampling (SMOTEENN)
113 Hybrid Sampling (SMOTEENN)
114 Hybrid Sampling (SMOTEENN)
115 Hybrid Sampling (SMOTEENN)
116 Hybrid Sampling (SMOTEENN)
117 Hybrid Sampling (SMOTEENN)
118 Hybrid Sampling (SMOTEENN)
119 Hybrid Sampling (SMOTEENN)
120 Hybrid Sampling (SMOTEENN)
121 Hybrid Sampling (SMOTEENN)
122 Hybrid Sampling (SMOTEENN)
123 Hybrid Sampling (SMOTEENN)
124 Hybrid Sampling (SMOTEENN)
125 Hybrid Sampling (SMOTEENN)
126 Hybrid Sampling (SMOTEENN)
127 Hybrid Sampling (SMOTEENN)
128 Hybrid Sampling (SMOTEENN)
129 Hybrid Sampling (SMOTEENN)
Model Unique Code
0 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
1 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
2 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
3 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
4 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
5 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
6 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
7 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
8 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
9 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
10 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
11 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
12 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
13 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
14 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
15 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
16 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
17 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
18 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
19 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
20 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
21 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
22 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
23 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
24 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
25 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
26 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
27 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
28 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
29 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
30 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
31 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
32 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
33 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
34 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
35 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
36 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
37 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
38 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
39 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
40 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
41 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
42 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
43 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
44 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
45 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
46 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
47 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
48 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
49 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
50 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
51 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
52 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
53 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
54 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
55 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
56 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
57 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
58 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
59 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
60 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
61 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
62 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
63 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
64 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
65 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
66 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
67 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
68 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
69 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
70 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
71 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
72 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
73 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
74 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
75 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
76 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
77 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
78 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
79 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
80 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
81 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
82 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
83 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
84 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
85 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
86 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
87 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
88 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
89 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
90 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
91 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
92 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
93 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
94 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
95 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
96 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
97 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
98 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
99 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
100 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
101 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
102 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
103 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
104 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
105 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
106 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
107 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
108 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
109 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
110 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
111 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
112 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
113 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
114 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
115 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
116 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
117 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
118 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
119 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
120 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
121 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
122 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
123 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
124 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
125 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
126 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
127 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
128 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp...
129 LightGBM_Hybrid Sampling (SMOTEENN)_Median Imp... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 LightGBM 0.878062 NaN Zero Imputation
1 LightGBM 0.133117 NaN Zero Imputation
2 LightGBM 0.172996 NaN Zero Imputation
3 LightGBM 0.150459 NaN Zero Imputation
4 LightGBM 0.669074 NaN Zero Imputation
5 LightGBM 0.293829 NaN Zero Imputation
6 LightGBM 0.338148 NaN Zero Imputation
7 LightGBM NaN gbdt Zero Imputation
8 LightGBM NaN None Zero Imputation
9 LightGBM NaN 1.0 Zero Imputation
10 LightGBM NaN split Zero Imputation
11 LightGBM NaN 0.1 Zero Imputation
12 LightGBM NaN -1 Zero Imputation
13 LightGBM NaN 20 Zero Imputation
14 LightGBM NaN 0.001 Zero Imputation
15 LightGBM NaN 0.0 Zero Imputation
16 LightGBM NaN 100 Zero Imputation
17 LightGBM NaN None Zero Imputation
18 LightGBM NaN 31 Zero Imputation
19 LightGBM NaN None Zero Imputation
20 LightGBM NaN None Zero Imputation
21 LightGBM NaN 0.0 Zero Imputation
22 LightGBM NaN 0.0 Zero Imputation
23 LightGBM NaN 1.0 Zero Imputation
24 LightGBM NaN 200000 Zero Imputation
25 LightGBM NaN 0 Zero Imputation
26 LightGBM 0.900711 NaN Zero Imputation
27 LightGBM 0.120996 NaN Zero Imputation
28 LightGBM 0.207317 NaN Zero Imputation
29 LightGBM 0.152809 NaN Zero Imputation
30 LightGBM 0.698845 NaN Zero Imputation
31 LightGBM 0.317066 NaN Zero Imputation
32 LightGBM 0.397689 NaN Zero Imputation
33 LightGBM NaN gbdt Zero Imputation
34 LightGBM NaN None Zero Imputation
35 LightGBM NaN 1.0 Zero Imputation
36 LightGBM NaN split Zero Imputation
37 LightGBM NaN 0.1 Zero Imputation
38 LightGBM NaN -1 Zero Imputation
39 LightGBM NaN 20 Zero Imputation
40 LightGBM NaN 0.001 Zero Imputation
41 LightGBM NaN 0.0 Zero Imputation
42 LightGBM NaN 100 Zero Imputation
43 LightGBM NaN None Zero Imputation
44 LightGBM NaN 31 Zero Imputation
45 LightGBM NaN None Zero Imputation
46 LightGBM NaN None Zero Imputation
47 LightGBM NaN 0.0 Zero Imputation
48 LightGBM NaN 0.0 Zero Imputation
49 LightGBM NaN 1.0 Zero Imputation
50 LightGBM NaN 200000 Zero Imputation
51 LightGBM NaN 0 Zero Imputation
52 LightGBM 0.894917 NaN Zero Imputation
53 LightGBM 0.131944 NaN Zero Imputation
54 LightGBM 0.203209 NaN Zero Imputation
55 LightGBM 0.160000 NaN Zero Imputation
56 LightGBM 0.686604 NaN Zero Imputation
57 LightGBM 0.286658 NaN Zero Imputation
58 LightGBM 0.373209 NaN Zero Imputation
59 LightGBM NaN gbdt Zero Imputation
60 LightGBM NaN None Zero Imputation
61 LightGBM NaN 1.0 Zero Imputation
62 LightGBM NaN split Zero Imputation
63 LightGBM NaN 0.1 Zero Imputation
64 LightGBM NaN -1 Zero Imputation
65 LightGBM NaN 20 Zero Imputation
66 LightGBM NaN 0.001 Zero Imputation
67 LightGBM NaN 0.0 Zero Imputation
68 LightGBM NaN 100 Zero Imputation
69 LightGBM NaN None Zero Imputation
70 LightGBM NaN 31 Zero Imputation
71 LightGBM NaN None Zero Imputation
72 LightGBM NaN None Zero Imputation
73 LightGBM NaN 0.0 Zero Imputation
74 LightGBM NaN 0.0 Zero Imputation
75 LightGBM NaN 1.0 Zero Imputation
76 LightGBM NaN 200000 Zero Imputation
77 LightGBM NaN 0 Zero Imputation
78 LightGBM 0.890411 NaN Zero Imputation
79 LightGBM 0.128378 NaN Zero Imputation
80 LightGBM 0.193878 NaN Zero Imputation
81 LightGBM 0.154472 NaN Zero Imputation
82 LightGBM 0.679689 NaN Zero Imputation
83 LightGBM 0.269558 NaN Zero Imputation
84 LightGBM 0.359378 NaN Zero Imputation
85 LightGBM NaN gbdt Zero Imputation
86 LightGBM NaN None Zero Imputation
87 LightGBM NaN 1.0 Zero Imputation
88 LightGBM NaN split Zero Imputation
89 LightGBM NaN 0.1 Zero Imputation
90 LightGBM NaN -1 Zero Imputation
91 LightGBM NaN 20 Zero Imputation
92 LightGBM NaN 0.001 Zero Imputation
93 LightGBM NaN 0.0 Zero Imputation
94 LightGBM NaN 100 Zero Imputation
95 LightGBM NaN None Zero Imputation
96 LightGBM NaN 31 Zero Imputation
97 LightGBM NaN None Zero Imputation
98 LightGBM NaN None Zero Imputation
99 LightGBM NaN 0.0 Zero Imputation
100 LightGBM NaN 0.0 Zero Imputation
101 LightGBM NaN 1.0 Zero Imputation
102 LightGBM NaN 200000 Zero Imputation
103 LightGBM NaN 0 Zero Imputation
104 LightGBM 0.890148 NaN Zero Imputation
105 LightGBM 0.144876 NaN Zero Imputation
106 LightGBM 0.189815 NaN Zero Imputation
107 LightGBM 0.164329 NaN Zero Imputation
108 LightGBM 0.701465 NaN Zero Imputation
109 LightGBM 0.325719 NaN Zero Imputation
110 LightGBM 0.402929 NaN Zero Imputation
111 LightGBM NaN gbdt Zero Imputation
112 LightGBM NaN None Zero Imputation
113 LightGBM NaN 1.0 Zero Imputation
114 LightGBM NaN split Zero Imputation
115 LightGBM NaN 0.1 Zero Imputation
116 LightGBM NaN -1 Zero Imputation
117 LightGBM NaN 20 Zero Imputation
118 LightGBM NaN 0.001 Zero Imputation
119 LightGBM NaN 0.0 Zero Imputation
120 LightGBM NaN 100 Zero Imputation
121 LightGBM NaN None Zero Imputation
122 LightGBM NaN 31 Zero Imputation
123 LightGBM NaN None Zero Imputation
124 LightGBM NaN None Zero Imputation
125 LightGBM NaN 0.0 Zero Imputation
126 LightGBM NaN 0.0 Zero Imputation
127 LightGBM NaN 1.0 Zero Imputation
128 LightGBM NaN 200000 Zero Imputation
129 LightGBM NaN 0 Zero Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
95 Hybrid Sampling (SMOTETomek)
96 Hybrid Sampling (SMOTETomek)
97 Hybrid Sampling (SMOTETomek)
98 Hybrid Sampling (SMOTETomek)
99 Hybrid Sampling (SMOTETomek)
100 Hybrid Sampling (SMOTETomek)
101 Hybrid Sampling (SMOTETomek)
102 Hybrid Sampling (SMOTETomek)
103 Hybrid Sampling (SMOTETomek)
104 Hybrid Sampling (SMOTETomek)
105 Hybrid Sampling (SMOTETomek)
106 Hybrid Sampling (SMOTETomek)
107 Hybrid Sampling (SMOTETomek)
108 Hybrid Sampling (SMOTETomek)
109 Hybrid Sampling (SMOTETomek)
110 Hybrid Sampling (SMOTETomek)
111 Hybrid Sampling (SMOTETomek)
112 Hybrid Sampling (SMOTETomek)
113 Hybrid Sampling (SMOTETomek)
114 Hybrid Sampling (SMOTETomek)
115 Hybrid Sampling (SMOTETomek)
116 Hybrid Sampling (SMOTETomek)
117 Hybrid Sampling (SMOTETomek)
118 Hybrid Sampling (SMOTETomek)
119 Hybrid Sampling (SMOTETomek)
120 Hybrid Sampling (SMOTETomek)
121 Hybrid Sampling (SMOTETomek)
122 Hybrid Sampling (SMOTETomek)
123 Hybrid Sampling (SMOTETomek)
124 Hybrid Sampling (SMOTETomek)
125 Hybrid Sampling (SMOTETomek)
126 Hybrid Sampling (SMOTETomek)
127 Hybrid Sampling (SMOTETomek)
128 Hybrid Sampling (SMOTETomek)
129 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
1 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
2 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
3 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
4 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
5 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
6 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
7 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
8 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
9 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
10 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
11 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
12 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
13 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
14 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
15 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
16 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
17 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
18 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
19 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
20 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
21 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
22 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
23 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
24 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
25 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
26 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
27 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
28 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
29 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
30 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
31 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
32 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
33 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
34 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
35 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
36 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
37 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
38 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
39 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
40 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
41 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
42 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
43 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
44 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
45 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
46 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
47 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
48 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
49 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
50 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
51 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
52 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
53 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
54 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
55 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
56 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
57 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
58 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
59 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
60 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
61 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
62 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
63 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
64 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
65 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
66 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
67 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
68 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
69 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
70 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
71 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
72 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
73 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
74 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
75 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
76 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
77 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
78 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
79 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
80 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
81 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
82 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
83 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
84 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
85 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
86 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
87 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
88 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
89 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
90 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
91 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
92 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
93 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
94 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
95 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
96 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
97 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
98 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
99 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
100 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
101 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
102 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
103 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
104 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
105 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
106 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
107 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
108 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
109 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
110 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
111 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
112 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
113 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
114 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
115 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
116 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
117 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
118 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
119 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
120 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
121 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
122 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
123 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
124 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
125 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
126 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
127 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
128 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp...
129 LightGBM_Hybrid Sampling (SMOTETomek)_Zero Imp... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 LightGBM 0.883066 NaN Mean Imputation
1 LightGBM 0.144330 NaN Mean Imputation
2 LightGBM 0.177215 NaN Mean Imputation
3 LightGBM 0.159091 NaN Mean Imputation
4 LightGBM 0.673649 NaN Mean Imputation
5 LightGBM 0.279163 NaN Mean Imputation
6 LightGBM 0.347299 NaN Mean Imputation
7 LightGBM NaN gbdt Mean Imputation
8 LightGBM NaN None Mean Imputation
9 LightGBM NaN 1.0 Mean Imputation
10 LightGBM NaN split Mean Imputation
11 LightGBM NaN 0.1 Mean Imputation
12 LightGBM NaN -1 Mean Imputation
13 LightGBM NaN 20 Mean Imputation
14 LightGBM NaN 0.001 Mean Imputation
15 LightGBM NaN 0.0 Mean Imputation
16 LightGBM NaN 100 Mean Imputation
17 LightGBM NaN None Mean Imputation
18 LightGBM NaN 31 Mean Imputation
19 LightGBM NaN None Mean Imputation
20 LightGBM NaN None Mean Imputation
21 LightGBM NaN 0.0 Mean Imputation
22 LightGBM NaN 0.0 Mean Imputation
23 LightGBM NaN 1.0 Mean Imputation
24 LightGBM NaN 200000 Mean Imputation
25 LightGBM NaN 0 Mean Imputation
26 LightGBM 0.901765 NaN Mean Imputation
27 LightGBM 0.114391 NaN Mean Imputation
28 LightGBM 0.189024 NaN Mean Imputation
29 LightGBM 0.142529 NaN Mean Imputation
30 LightGBM 0.675542 NaN Mean Imputation
31 LightGBM 0.270763 NaN Mean Imputation
32 LightGBM 0.351084 NaN Mean Imputation
33 LightGBM NaN gbdt Mean Imputation
34 LightGBM NaN None Mean Imputation
35 LightGBM NaN 1.0 Mean Imputation
36 LightGBM NaN split Mean Imputation
37 LightGBM NaN 0.1 Mean Imputation
38 LightGBM NaN -1 Mean Imputation
39 LightGBM NaN 20 Mean Imputation
40 LightGBM NaN 0.001 Mean Imputation
41 LightGBM NaN 0.0 Mean Imputation
42 LightGBM NaN 100 Mean Imputation
43 LightGBM NaN None Mean Imputation
44 LightGBM NaN 31 Mean Imputation
45 LightGBM NaN None Mean Imputation
46 LightGBM NaN None Mean Imputation
47 LightGBM NaN 0.0 Mean Imputation
48 LightGBM NaN 0.0 Mean Imputation
49 LightGBM NaN 1.0 Mean Imputation
50 LightGBM NaN 200000 Mean Imputation
51 LightGBM NaN 0 Mean Imputation
52 LightGBM 0.883329 NaN Mean Imputation
53 LightGBM 0.127907 NaN Mean Imputation
54 LightGBM 0.235294 NaN Mean Imputation
55 LightGBM 0.165725 NaN Mean Imputation
56 LightGBM 0.686167 NaN Mean Imputation
57 LightGBM 0.310858 NaN Mean Imputation
58 LightGBM 0.372333 NaN Mean Imputation
59 LightGBM NaN gbdt Mean Imputation
60 LightGBM NaN None Mean Imputation
61 LightGBM NaN 1.0 Mean Imputation
62 LightGBM NaN split Mean Imputation
63 LightGBM NaN 0.1 Mean Imputation
64 LightGBM NaN -1 Mean Imputation
65 LightGBM NaN 20 Mean Imputation
66 LightGBM NaN 0.001 Mean Imputation
67 LightGBM NaN 0.0 Mean Imputation
68 LightGBM NaN 100 Mean Imputation
69 LightGBM NaN None Mean Imputation
70 LightGBM NaN 31 Mean Imputation
71 LightGBM NaN None Mean Imputation
72 LightGBM NaN None Mean Imputation
73 LightGBM NaN 0.0 Mean Imputation
74 LightGBM NaN 0.0 Mean Imputation
75 LightGBM NaN 1.0 Mean Imputation
76 LightGBM NaN 200000 Mean Imputation
77 LightGBM NaN 0 Mean Imputation
78 LightGBM 0.890148 NaN Mean Imputation
79 LightGBM 0.122867 NaN Mean Imputation
80 LightGBM 0.183673 NaN Mean Imputation
81 LightGBM 0.147239 NaN Mean Imputation
82 LightGBM 0.683311 NaN Mean Imputation
83 LightGBM 0.310091 NaN Mean Imputation
84 LightGBM 0.366621 NaN Mean Imputation
85 LightGBM NaN gbdt Mean Imputation
86 LightGBM NaN None Mean Imputation
87 LightGBM NaN 1.0 Mean Imputation
88 LightGBM NaN split Mean Imputation
89 LightGBM NaN 0.1 Mean Imputation
90 LightGBM NaN -1 Mean Imputation
91 LightGBM NaN 20 Mean Imputation
92 LightGBM NaN 0.001 Mean Imputation
93 LightGBM NaN 0.0 Mean Imputation
94 LightGBM NaN 100 Mean Imputation
95 LightGBM NaN None Mean Imputation
96 LightGBM NaN 31 Mean Imputation
97 LightGBM NaN None Mean Imputation
98 LightGBM NaN None Mean Imputation
99 LightGBM NaN 0.0 Mean Imputation
100 LightGBM NaN 0.0 Mean Imputation
101 LightGBM NaN 1.0 Mean Imputation
102 LightGBM NaN 200000 Mean Imputation
103 LightGBM NaN 0 Mean Imputation
104 LightGBM 0.892518 NaN Mean Imputation
105 LightGBM 0.154676 NaN Mean Imputation
106 LightGBM 0.199074 NaN Mean Imputation
107 LightGBM 0.174089 NaN Mean Imputation
108 LightGBM 0.699898 NaN Mean Imputation
109 LightGBM 0.331523 NaN Mean Imputation
110 LightGBM 0.399797 NaN Mean Imputation
111 LightGBM NaN gbdt Mean Imputation
112 LightGBM NaN None Mean Imputation
113 LightGBM NaN 1.0 Mean Imputation
114 LightGBM NaN split Mean Imputation
115 LightGBM NaN 0.1 Mean Imputation
116 LightGBM NaN -1 Mean Imputation
117 LightGBM NaN 20 Mean Imputation
118 LightGBM NaN 0.001 Mean Imputation
119 LightGBM NaN 0.0 Mean Imputation
120 LightGBM NaN 100 Mean Imputation
121 LightGBM NaN None Mean Imputation
122 LightGBM NaN 31 Mean Imputation
123 LightGBM NaN None Mean Imputation
124 LightGBM NaN None Mean Imputation
125 LightGBM NaN 0.0 Mean Imputation
126 LightGBM NaN 0.0 Mean Imputation
127 LightGBM NaN 1.0 Mean Imputation
128 LightGBM NaN 200000 Mean Imputation
129 LightGBM NaN 0 Mean Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
5 Hybrid Sampling (SMOTETomek)
6 Hybrid Sampling (SMOTETomek)
7 Hybrid Sampling (SMOTETomek)
8 Hybrid Sampling (SMOTETomek)
9 Hybrid Sampling (SMOTETomek)
10 Hybrid Sampling (SMOTETomek)
11 Hybrid Sampling (SMOTETomek)
12 Hybrid Sampling (SMOTETomek)
13 Hybrid Sampling (SMOTETomek)
14 Hybrid Sampling (SMOTETomek)
15 Hybrid Sampling (SMOTETomek)
16 Hybrid Sampling (SMOTETomek)
17 Hybrid Sampling (SMOTETomek)
18 Hybrid Sampling (SMOTETomek)
19 Hybrid Sampling (SMOTETomek)
20 Hybrid Sampling (SMOTETomek)
21 Hybrid Sampling (SMOTETomek)
22 Hybrid Sampling (SMOTETomek)
23 Hybrid Sampling (SMOTETomek)
24 Hybrid Sampling (SMOTETomek)
25 Hybrid Sampling (SMOTETomek)
26 Hybrid Sampling (SMOTETomek)
27 Hybrid Sampling (SMOTETomek)
28 Hybrid Sampling (SMOTETomek)
29 Hybrid Sampling (SMOTETomek)
30 Hybrid Sampling (SMOTETomek)
31 Hybrid Sampling (SMOTETomek)
32 Hybrid Sampling (SMOTETomek)
33 Hybrid Sampling (SMOTETomek)
34 Hybrid Sampling (SMOTETomek)
35 Hybrid Sampling (SMOTETomek)
36 Hybrid Sampling (SMOTETomek)
37 Hybrid Sampling (SMOTETomek)
38 Hybrid Sampling (SMOTETomek)
39 Hybrid Sampling (SMOTETomek)
40 Hybrid Sampling (SMOTETomek)
41 Hybrid Sampling (SMOTETomek)
42 Hybrid Sampling (SMOTETomek)
43 Hybrid Sampling (SMOTETomek)
44 Hybrid Sampling (SMOTETomek)
45 Hybrid Sampling (SMOTETomek)
46 Hybrid Sampling (SMOTETomek)
47 Hybrid Sampling (SMOTETomek)
48 Hybrid Sampling (SMOTETomek)
49 Hybrid Sampling (SMOTETomek)
50 Hybrid Sampling (SMOTETomek)
51 Hybrid Sampling (SMOTETomek)
52 Hybrid Sampling (SMOTETomek)
53 Hybrid Sampling (SMOTETomek)
54 Hybrid Sampling (SMOTETomek)
55 Hybrid Sampling (SMOTETomek)
56 Hybrid Sampling (SMOTETomek)
57 Hybrid Sampling (SMOTETomek)
58 Hybrid Sampling (SMOTETomek)
59 Hybrid Sampling (SMOTETomek)
60 Hybrid Sampling (SMOTETomek)
61 Hybrid Sampling (SMOTETomek)
62 Hybrid Sampling (SMOTETomek)
63 Hybrid Sampling (SMOTETomek)
64 Hybrid Sampling (SMOTETomek)
65 Hybrid Sampling (SMOTETomek)
66 Hybrid Sampling (SMOTETomek)
67 Hybrid Sampling (SMOTETomek)
68 Hybrid Sampling (SMOTETomek)
69 Hybrid Sampling (SMOTETomek)
70 Hybrid Sampling (SMOTETomek)
71 Hybrid Sampling (SMOTETomek)
72 Hybrid Sampling (SMOTETomek)
73 Hybrid Sampling (SMOTETomek)
74 Hybrid Sampling (SMOTETomek)
75 Hybrid Sampling (SMOTETomek)
76 Hybrid Sampling (SMOTETomek)
77 Hybrid Sampling (SMOTETomek)
78 Hybrid Sampling (SMOTETomek)
79 Hybrid Sampling (SMOTETomek)
80 Hybrid Sampling (SMOTETomek)
81 Hybrid Sampling (SMOTETomek)
82 Hybrid Sampling (SMOTETomek)
83 Hybrid Sampling (SMOTETomek)
84 Hybrid Sampling (SMOTETomek)
85 Hybrid Sampling (SMOTETomek)
86 Hybrid Sampling (SMOTETomek)
87 Hybrid Sampling (SMOTETomek)
88 Hybrid Sampling (SMOTETomek)
89 Hybrid Sampling (SMOTETomek)
90 Hybrid Sampling (SMOTETomek)
91 Hybrid Sampling (SMOTETomek)
92 Hybrid Sampling (SMOTETomek)
93 Hybrid Sampling (SMOTETomek)
94 Hybrid Sampling (SMOTETomek)
95 Hybrid Sampling (SMOTETomek)
96 Hybrid Sampling (SMOTETomek)
97 Hybrid Sampling (SMOTETomek)
98 Hybrid Sampling (SMOTETomek)
99 Hybrid Sampling (SMOTETomek)
100 Hybrid Sampling (SMOTETomek)
101 Hybrid Sampling (SMOTETomek)
102 Hybrid Sampling (SMOTETomek)
103 Hybrid Sampling (SMOTETomek)
104 Hybrid Sampling (SMOTETomek)
105 Hybrid Sampling (SMOTETomek)
106 Hybrid Sampling (SMOTETomek)
107 Hybrid Sampling (SMOTETomek)
108 Hybrid Sampling (SMOTETomek)
109 Hybrid Sampling (SMOTETomek)
110 Hybrid Sampling (SMOTETomek)
111 Hybrid Sampling (SMOTETomek)
112 Hybrid Sampling (SMOTETomek)
113 Hybrid Sampling (SMOTETomek)
114 Hybrid Sampling (SMOTETomek)
115 Hybrid Sampling (SMOTETomek)
116 Hybrid Sampling (SMOTETomek)
117 Hybrid Sampling (SMOTETomek)
118 Hybrid Sampling (SMOTETomek)
119 Hybrid Sampling (SMOTETomek)
120 Hybrid Sampling (SMOTETomek)
121 Hybrid Sampling (SMOTETomek)
122 Hybrid Sampling (SMOTETomek)
123 Hybrid Sampling (SMOTETomek)
124 Hybrid Sampling (SMOTETomek)
125 Hybrid Sampling (SMOTETomek)
126 Hybrid Sampling (SMOTETomek)
127 Hybrid Sampling (SMOTETomek)
128 Hybrid Sampling (SMOTETomek)
129 Hybrid Sampling (SMOTETomek)
Model Unique Code
0 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
1 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
2 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
3 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
4 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
5 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
6 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
7 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
8 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
9 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
10 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
11 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
12 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
13 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
14 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
15 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
16 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
17 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
18 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
19 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
20 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
21 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
22 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
23 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
24 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
25 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
26 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
27 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
28 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
29 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
30 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
31 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
32 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
33 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
34 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
35 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
36 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
37 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
38 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
39 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
40 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
41 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
42 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
43 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
44 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
45 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
46 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
47 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
48 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
49 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
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70 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
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78 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
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80 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
81 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
82 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
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84 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
85 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
86 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
87 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
88 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
89 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
90 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
91 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
92 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
93 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
94 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
95 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
96 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
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98 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
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100 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
101 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
102 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
103 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
104 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
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106 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
107 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
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110 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
111 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
112 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
113 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
114 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
115 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
116 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
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118 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
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120 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
121 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp...
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129 LightGBM_Hybrid Sampling (SMOTETomek)_Mean Imp... ,
Algorithm Metrics Hyperparameters Imputation Technique \
0 LightGBM 0.882012 NaN Median Imputation
1 LightGBM 0.129825 NaN Median Imputation
2 LightGBM 0.156118 NaN Median Imputation
3 LightGBM 0.141762 NaN Median Imputation
4 LightGBM 0.672112 NaN Median Imputation
5 LightGBM 0.271770 NaN Median Imputation
6 LightGBM 0.344224 NaN Median Imputation
7 LightGBM NaN gbdt Median Imputation
8 LightGBM NaN None Median Imputation
9 LightGBM NaN 1.0 Median Imputation
10 LightGBM NaN split Median Imputation
11 LightGBM NaN 0.1 Median Imputation
12 LightGBM NaN -1 Median Imputation
13 LightGBM NaN 20 Median Imputation
14 LightGBM NaN 0.001 Median Imputation
15 LightGBM NaN 0.0 Median Imputation
16 LightGBM NaN 100 Median Imputation
17 LightGBM NaN None Median Imputation
18 LightGBM NaN 31 Median Imputation
19 LightGBM NaN None Median Imputation
20 LightGBM NaN None Median Imputation
21 LightGBM NaN 0.0 Median Imputation
22 LightGBM NaN 0.0 Median Imputation
23 LightGBM NaN 1.0 Median Imputation
24 LightGBM NaN 200000 Median Imputation
25 LightGBM NaN 0 Median Imputation
26 LightGBM 0.904398 NaN Median Imputation
27 LightGBM 0.112840 NaN Median Imputation
28 LightGBM 0.176829 NaN Median Imputation
29 LightGBM 0.137767 NaN Median Imputation
30 LightGBM 0.677074 NaN Median Imputation
31 LightGBM 0.279128 NaN Median Imputation
32 LightGBM 0.354149 NaN Median Imputation
33 LightGBM NaN gbdt Median Imputation
34 LightGBM NaN None Median Imputation
35 LightGBM NaN 1.0 Median Imputation
36 LightGBM NaN split Median Imputation
37 LightGBM NaN 0.1 Median Imputation
38 LightGBM NaN -1 Median Imputation
39 LightGBM NaN 20 Median Imputation
40 LightGBM NaN 0.001 Median Imputation
41 LightGBM NaN 0.0 Median Imputation
42 LightGBM NaN 100 Median Imputation
43 LightGBM NaN None Median Imputation
44 LightGBM NaN 31 Median Imputation
45 LightGBM NaN None Median Imputation
46 LightGBM NaN None Median Imputation
47 LightGBM NaN 0.0 Median Imputation
48 LightGBM NaN 0.0 Median Imputation
49 LightGBM NaN 1.0 Median Imputation
50 LightGBM NaN 200000 Median Imputation
51 LightGBM NaN 0 Median Imputation
52 LightGBM 0.891493 NaN Median Imputation
53 LightGBM 0.133550 NaN Median Imputation
54 LightGBM 0.219251 NaN Median Imputation
55 LightGBM 0.165992 NaN Median Imputation
56 LightGBM 0.690328 NaN Median Imputation
57 LightGBM 0.289697 NaN Median Imputation
58 LightGBM 0.380657 NaN Median Imputation
59 LightGBM NaN gbdt Median Imputation
60 LightGBM NaN None Median Imputation
61 LightGBM NaN 1.0 Median Imputation
62 LightGBM NaN split Median Imputation
63 LightGBM NaN 0.1 Median Imputation
64 LightGBM NaN -1 Median Imputation
65 LightGBM NaN 20 Median Imputation
66 LightGBM NaN 0.001 Median Imputation
67 LightGBM NaN 0.0 Median Imputation
68 LightGBM NaN 100 Median Imputation
69 LightGBM NaN None Median Imputation
70 LightGBM NaN 31 Median Imputation
71 LightGBM NaN None Median Imputation
72 LightGBM NaN None Median Imputation
73 LightGBM NaN 0.0 Median Imputation
74 LightGBM NaN 0.0 Median Imputation
75 LightGBM NaN 1.0 Median Imputation
76 LightGBM NaN 200000 Median Imputation
77 LightGBM NaN 0 Median Imputation
78 LightGBM 0.891465 NaN Median Imputation
79 LightGBM 0.119718 NaN Median Imputation
80 LightGBM 0.173469 NaN Median Imputation
81 LightGBM 0.141667 NaN Median Imputation
82 LightGBM 0.682028 NaN Median Imputation
83 LightGBM 0.282222 NaN Median Imputation
84 LightGBM 0.364056 NaN Median Imputation
85 LightGBM NaN gbdt Median Imputation
86 LightGBM NaN None Median Imputation
87 LightGBM NaN 1.0 Median Imputation
88 LightGBM NaN split Median Imputation
89 LightGBM NaN 0.1 Median Imputation
90 LightGBM NaN -1 Median Imputation
91 LightGBM NaN 20 Median Imputation
92 LightGBM NaN 0.001 Median Imputation
93 LightGBM NaN 0.0 Median Imputation
94 LightGBM NaN 100 Median Imputation
95 LightGBM NaN None Median Imputation
96 LightGBM NaN 31 Median Imputation
97 LightGBM NaN None Median Imputation
98 LightGBM NaN None Median Imputation
99 LightGBM NaN 0.0 Median Imputation
100 LightGBM NaN 0.0 Median Imputation
101 LightGBM NaN 1.0 Median Imputation
102 LightGBM NaN 200000 Median Imputation
103 LightGBM NaN 0 Median Imputation
104 LightGBM 0.893309 NaN Median Imputation
105 LightGBM 0.151292 NaN Median Imputation
106 LightGBM 0.189815 NaN Median Imputation
107 LightGBM 0.168378 NaN Median Imputation
108 LightGBM 0.692653 NaN Median Imputation
109 LightGBM 0.329428 NaN Median Imputation
110 LightGBM 0.385305 NaN Median Imputation
111 LightGBM NaN gbdt Median Imputation
112 LightGBM NaN None Median Imputation
113 LightGBM NaN 1.0 Median Imputation
114 LightGBM NaN split Median Imputation
115 LightGBM NaN 0.1 Median Imputation
116 LightGBM NaN -1 Median Imputation
117 LightGBM NaN 20 Median Imputation
118 LightGBM NaN 0.001 Median Imputation
119 LightGBM NaN 0.0 Median Imputation
120 LightGBM NaN 100 Median Imputation
121 LightGBM NaN None Median Imputation
122 LightGBM NaN 31 Median Imputation
123 LightGBM NaN None Median Imputation
124 LightGBM NaN None Median Imputation
125 LightGBM NaN 0.0 Median Imputation
126 LightGBM NaN 0.0 Median Imputation
127 LightGBM NaN 1.0 Median Imputation
128 LightGBM NaN 200000 Median Imputation
129 LightGBM NaN 0 Median Imputation
Imbalance Class Technique \
0 Hybrid Sampling (SMOTETomek)
1 Hybrid Sampling (SMOTETomek)
2 Hybrid Sampling (SMOTETomek)
3 Hybrid Sampling (SMOTETomek)
4 Hybrid Sampling (SMOTETomek)
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Model Unique Code
0 LightGBM_Hybrid Sampling (SMOTETomek)_Median I...
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2 LightGBM_Hybrid Sampling (SMOTETomek)_Median I...
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129 LightGBM_Hybrid Sampling (SMOTETomek)_Median I... ]
import pandas as pd
import numpy as np
from sklearn.metrics import confusion_matrix, precision_recall_curve, roc_curve, auc, matthews_corrcoef, cohen_kappa_score, accuracy_score, f1_score, precision_score, recall_score, balanced_accuracy_score
def calculate_classification_metrics_one_class(y_true, y_pred_prob, target_class=1):
# Calculate binary predictions
y_pred = (y_pred_prob >= 0.5).astype(int)
# Confusion Matrix
cm = confusion_matrix(y_true, y_pred)
# True Positives, True Negatives, False Positives, False Negatives
TP, TN, FP, FN = cm.ravel()
# Sensitivity (True Positive Rate)
sensitivity = TP / (TP + FN)
# Specificity (True Negative Rate)
specificity = TN / (TN + FP)
# Area Under the ROC Curve (AUC-ROC)
fpr, tpr, _ = roc_curve(y_true, y_pred_prob)
auc_roc = auc(fpr, tpr)
# Area Under the Precision-Recall Curve (AUC-PRC)
precision, recall, _ = precision_recall_curve(y_true, y_pred_prob)
auc_prc = auc(recall, precision)
# Gini Coefficient
gini = 2 * auc_roc - 1
# Kolmogorov-Smirnov (KS) statistic
ks = np.max(tpr - fpr)
# Matthews Correlation Coefficient (MCC)
mcc = matthews_corrcoef(y_true, y_pred)
# Cohen's Kappa
kappa = cohen_kappa_score(y_true, y_pred)
# Accuracy
accuracy = accuracy_score(y_true, y_pred)
# Balanced Accuracy
balanced_accuracy = balanced_accuracy_score(y_true, y_pred)
# F1 Score
f1 = f1_score(y_true, y_pred)
# Weighted F1 Score
f1_weighted = f1_score(y_true, y_pred, average='weighted')
# Precision
precision = precision_score(y_true, y_pred)
# Recall
recall = recall_score(y_true, y_pred)
# Create DataFrame
metrics_df = pd.DataFrame({
'Class': [f'Class {target_class}'],
'TP': [TP],
'TN': [TN],
'FP': [FP],
'FN': [FN],
'Sensitivity_TPR': [sensitivity],
'Specificity': [specificity],
'AUC_ROC': [auc_roc],
'AUC_PRC': [auc_prc],
'Gini': [gini],
'KS': [ks],
'MCC': [mcc],
'Kappa': [kappa],
'Accuracy': [accuracy],
'Balanced_Accuracy': [balanced_accuracy],
'F1': [f1],
'F1_Weighted': [f1_weighted],
'Precision': [precision],
'Recall': [recall]
})
# Reset index
metrics_df = metrics_df.reset_index(drop=True)
return metrics_df
imputation_technique
import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from imblearn.over_sampling import RandomOverSampler, SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.combine import SMOTEENN, SMOTETomek
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc, make_scorer
from sklearn.impute import SimpleImputer
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistGradientBoostingClassifier
#from catboost import CatBoostClassifier
import os
import json
# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.columns[1:479]
target = df['label']
# Define the imbalanced data handling techniques
sampling_techniques = {
'None': None,
'Oversampling': RandomOverSampler(),
'Undersampling': RandomUnderSampler(),
}
# Define the imputation techniques
imputation_techniques = {
'None': None,
'Zero Imputation': SimpleImputer(strategy='constant', fill_value=0),
'Mean Imputation': SimpleImputer(strategy='mean'),
'Median Imputation': SimpleImputer(strategy='median')
}
# Create a list to store the results
results_2 = []
# Define k-fold cross-validation
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)
# Iterate through each model
for model_name, model in [
# ('Logistic Regression', LogisticRegression()),
('k-Nearest Neighbors (k-NN)', KNeighborsClassifier()),
# ('Support Vector Machines (SVM)', SVC(probability=True)),
# ('Neural Networks', MLPClassifier()),
# ('Naive Bayes', GaussianNB()),
# ('Decision Trees', DecisionTreeClassifier()),
# ('Random Forest', RandomForestClassifier()),
# ('Gradient Boosting Machines (GBM)', GradientBoostingClassifier()),
# ('XGBoost', XGBClassifier()),
# ('LightGBM', LGBMClassifier()),
# ('CatBoost', CatBoostClassifier()),
# ('HistGradientBoostingClassifier', HistGradientBoostingClassifier())
]:
# Modify hyperparameters for the current model to include both 'None' and 'balanced'
models_to_try_both_hyperparams = ['Logistic Regression', 'Support Vector Machines (SVM)', 'Naive Bayes', 'Decision Trees', 'Random Forest', 'HistGradientBoostingClassifier']
hyperparameters[model_name] = {'class_weight': [None, 'balanced']} if model_name in models_to_try_both_hyperparams else {}
# Iterate through each imbalanced data handling technique
for sampling_name, sampling_technique in sampling_techniques.items():
# Iterate through each hyperparameter
for hyperparameter in hyperparameters[model_name].get('class_weight', [None]):
# Iterate through each imputation technique
for imputation_name, imputation_technique in imputation_techniques.items():
# Create a new dataframe to store the results for this model, technique combination
df_result_2 = pd.DataFrame(columns=['Algorithm', 'Metrics', 'Hyperparameters', 'Imputation Technique', 'Imbalance Class Technique', 'Model Unique Code'])
# Perform k-fold cross-validation
for fold, (train_index, test_index) in enumerate(k_fold.split(df[features], target)):
X_train, X_test = df[features].iloc[train_index], df[features].iloc[test_index]
y_train, y_test = target.iloc[train_index], target.iloc[test_index]
# Apply imputation technique on the train and test sets, if imputation_name is not None
if imputation_name is not None and imputation_technique is not None:
print(imputation_name)
X_train_imputed = imputation_technique.fit_transform(X_train)
X_test_imputed = imputation_technique.transform(X_test)
else:
print(imputation_name)
if model_name in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
X_train_imputed, X_test_imputed = X_train, X_test
else:
break
# Apply the imbalanced data handling technique on the train set
if sampling_technique is not None:
X_train_resampled, y_train_resampled = sampling_technique.fit_resample(X_train_imputed, y_train)
else:
X_train_resampled, y_train_resampled = X_train_imputed, y_train
# Train the model on the resampled train set
model.fit(X_train_resampled, y_train_resampled)
# Make predictions on the test set
y_pred = model.predict(X_test_imputed)
y_prob = model.predict_proba(X_test_imputed)[:, 1]
# Calculate the evaluation metrics
metrics_df = calculate_classification_metrics_one_class(y_test, y_prob)
# Set 'Imputation Technique' based on the specified conditions
if imputation_name in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
imputation_name = 'None'
else:
imputation_name = imputation_name
#imputation_name = None if imputation_name not in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier'] else imputation_name
# Create a new DataFrame for each iteration
df_result_2 = pd.DataFrame({
'Algorithm': model_name,
'Hyperparameters': [model.get_params()],
'Imputation Technique': imputation_name,
'Imbalance Class Technique': sampling_name,
'Model Unique Code': f"{model_name}_{sampling_name}_{imputation_name}_fold_{fold}",
**metrics_df # Unpack the metrics_df columns
})
# Append the result to the list
results_2.append(df_result_2)
# Combine all the results DataFrames into one final DataFrame
df_results_final_2 = pd.concat(results_2, ignore_index=True)
# Save each model in a separate file with a name corresponding to the model unique code
for model_unique_code in df_results_final_2['Model Unique Code'].unique():
df_model = df_results_final_2[df_results_final_2['Model Unique Code'] == model_unique_code]
df_model.to_csv(f"{model_unique_code}_2.csv", index=False)
# Display the final DataFrame
display(df_results_final_2)
None Zero Imputation Zero Imputation Zero Imputation Zero Imputation Zero Imputation Mean Imputation Mean Imputation Mean Imputation Mean Imputation Mean Imputation Median Imputation Median Imputation Median Imputation Median Imputation Median Imputation None Zero Imputation Zero Imputation Zero Imputation Zero Imputation Zero Imputation Mean Imputation Mean Imputation Mean Imputation Mean Imputation Mean Imputation Median Imputation Median Imputation Median Imputation Median Imputation Median Imputation None Zero Imputation Zero Imputation Zero Imputation Zero Imputation Zero Imputation Mean Imputation Mean Imputation Mean Imputation Mean Imputation Mean Imputation Median Imputation Median Imputation Median Imputation Median Imputation Median Imputation
| Algorithm | Hyperparameters | Imputation Technique | Imbalance Class Technique | Model Unique Code | Class | TP | TN | FP | FN | Sensitivity_TPR | Specificity | AUC_ROC | AUC_PRC | Gini | KS | MCC | Kappa | Accuracy | Balanced_Accuracy | F1 | F1_Weighted | Precision | Recall | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Zero Imputation | None | k-Nearest Neighbors (k-NN)_None_Zero Imputation_fold_0 | Class 1 | 3550 | 10 | 237 | 0 | 1.000000 | 0.040486 | 0.561344 | 0.089385 | 0.122688 | 0.117866 | -0.013259 | -0.005080 | 0.934949 | 0.498596 | 0.000000 | 0.906062 | 0.000000 | 0.000000 |
| 1 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Zero Imputation | None | k-Nearest Neighbors (k-NN)_None_Zero Imputation_fold_1 | Class 1 | 3620 | 13 | 163 | 1 | 0.999724 | 0.073864 | 0.552757 | 0.066008 | 0.105515 | 0.098012 | 0.008450 | 0.004472 | 0.953648 | 0.501260 | 0.011236 | 0.934586 | 0.071429 | 0.006098 |
| 2 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Zero Imputation | None | k-Nearest Neighbors (k-NN)_None_Zero Imputation_fold_2 | Class 1 | 3600 | 10 | 187 | 0 | 1.000000 | 0.050761 | 0.540575 | 0.062454 | 0.081150 | 0.077690 | -0.011696 | -0.005025 | 0.948117 | 0.498615 | 0.000000 | 0.925430 | 0.000000 | 0.000000 |
| 3 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Zero Imputation | None | k-Nearest Neighbors (k-NN)_None_Zero Imputation_fold_3 | Class 1 | 3589 | 11 | 194 | 2 | 0.999443 | 0.053659 | 0.539393 | 0.069418 | 0.078787 | 0.073492 | 0.027077 | 0.012798 | 0.945996 | 0.503574 | 0.019139 | 0.923022 | 0.153846 | 0.010204 |
| 4 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Zero Imputation | None | k-Nearest Neighbors (k-NN)_None_Zero Imputation_fold_4 | Class 1 | 3575 | 5 | 214 | 2 | 0.999441 | 0.022831 | 0.574602 | 0.102078 | 0.149205 | 0.142753 | 0.042455 | 0.014416 | 0.942308 | 0.503931 | 0.017937 | 0.916091 | 0.285714 | 0.009259 |
| 5 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Mean Imputation | None | k-Nearest Neighbors (k-NN)_None_Mean Imputation_fold_0 | Class 1 | 3549 | 11 | 236 | 1 | 0.999718 | 0.044534 | 0.550183 | 0.086808 | 0.100366 | 0.097887 | 0.004868 | 0.002028 | 0.934949 | 0.500565 | 0.008032 | 0.906554 | 0.083333 | 0.004219 |
| 6 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Mean Imputation | None | k-Nearest Neighbors (k-NN)_None_Mean Imputation_fold_1 | Class 1 | 3624 | 9 | 163 | 1 | 0.999724 | 0.052326 | 0.563737 | 0.070277 | 0.127473 | 0.115713 | 0.014360 | 0.006562 | 0.954701 | 0.501810 | 0.011494 | 0.935125 | 0.100000 | 0.006098 |
| 7 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Mean Imputation | None | k-Nearest Neighbors (k-NN)_None_Mean Imputation_fold_2 | Class 1 | 3596 | 14 | 186 | 1 | 0.999722 | 0.070000 | 0.561800 | 0.070924 | 0.123599 | 0.118447 | 0.005069 | 0.002606 | 0.947327 | 0.500735 | 0.009901 | 0.925514 | 0.066667 | 0.005348 |
| 8 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Mean Imputation | None | k-Nearest Neighbors (k-NN)_None_Mean Imputation_fold_3 | Class 1 | 3580 | 20 | 192 | 4 | 0.998884 | 0.094340 | 0.546001 | 0.081556 | 0.092003 | 0.086752 | 0.041466 | 0.025384 | 0.944152 | 0.507426 | 0.036364 | 0.922972 | 0.166667 | 0.020408 |
| 9 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Mean Imputation | None | k-Nearest Neighbors (k-NN)_None_Mean Imputation_fold_4 | Class 1 | 3566 | 14 | 213 | 3 | 0.999159 | 0.061674 | 0.584009 | 0.094202 | 0.168018 | 0.162270 | 0.034619 | 0.017594 | 0.940200 | 0.504989 | 0.025751 | 0.915472 | 0.176471 | 0.013889 |
| 10 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Median Imputation | None | k-Nearest Neighbors (k-NN)_None_Median Imputation_fold_0 | Class 1 | 3551 | 9 | 237 | 0 | 1.000000 | 0.036585 | 0.560442 | 0.090550 | 0.120884 | 0.114489 | -0.012577 | -0.004588 | 0.935212 | 0.498736 | 0.000000 | 0.906193 | 0.000000 | 0.000000 |
| 11 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Median Imputation | None | k-Nearest Neighbors (k-NN)_None_Median Imputation_fold_1 | Class 1 | 3613 | 20 | 163 | 1 | 0.999723 | 0.109290 | 0.562621 | 0.063424 | 0.125243 | 0.118232 | 0.001624 | 0.001015 | 0.951804 | 0.500296 | 0.010811 | 0.933642 | 0.047619 | 0.006098 |
| 12 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Median Imputation | None | k-Nearest Neighbors (k-NN)_None_Median Imputation_fold_2 | Class 1 | 3594 | 16 | 186 | 1 | 0.999722 | 0.079208 | 0.544127 | 0.067827 | 0.088253 | 0.080460 | 0.002967 | 0.001609 | 0.946800 | 0.500458 | 0.009804 | 0.925245 | 0.058824 | 0.005348 |
| 13 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Median Imputation | None | k-Nearest Neighbors (k-NN)_None_Median Imputation_fold_3 | Class 1 | 3582 | 18 | 195 | 1 | 0.999721 | 0.084507 | 0.549494 | 0.071411 | 0.098988 | 0.090743 | 0.000320 | 0.000178 | 0.943888 | 0.500051 | 0.009302 | 0.921464 | 0.052632 | 0.005102 |
| 14 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Median Imputation | None | k-Nearest Neighbors (k-NN)_None_Median Imputation_fold_4 | Class 1 | 3561 | 19 | 215 | 1 | 0.999719 | 0.081197 | 0.582975 | 0.091624 | 0.165950 | 0.158876 | -0.002168 | -0.001181 | 0.938356 | 0.499661 | 0.008475 | 0.913580 | 0.050000 | 0.004630 |
| 15 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Zero Imputation | Oversampling | k-Nearest Neighbors (k-NN)_Oversampling_Zero Imputation_fold_0 | Class 1 | 3149 | 411 | 193 | 44 | 0.986220 | 0.680464 | 0.559812 | 0.120083 | 0.119624 | 0.119271 | 0.052294 | 0.049118 | 0.840927 | 0.535102 | 0.127168 | 0.863471 | 0.096703 | 0.185654 |
| 16 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Zero Imputation | Oversampling | k-Nearest Neighbors (k-NN)_Oversampling_Zero Imputation_fold_1 | Class 1 | 3166 | 467 | 134 | 30 | 0.990613 | 0.777038 | 0.545952 | 0.088694 | 0.091903 | 0.093566 | 0.032778 | 0.027614 | 0.841717 | 0.527191 | 0.090772 | 0.877786 | 0.060362 | 0.182927 |
| 17 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Zero Imputation | Oversampling | k-Nearest Neighbors (k-NN)_Oversampling_Zero Imputation_fold_2 | Class 1 | 3162 | 448 | 164 | 23 | 0.992779 | 0.732026 | 0.530319 | 0.076261 | 0.060638 | 0.071788 | -0.000725 | -0.000642 | 0.838820 | 0.499447 | 0.069909 | 0.870304 | 0.048832 | 0.122995 |
| 18 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Zero Imputation | Oversampling | k-Nearest Neighbors (k-NN)_Oversampling_Zero Imputation_fold_3 | Class 1 | 3165 | 435 | 167 | 29 | 0.990920 | 0.722591 | 0.533114 | 0.092357 | 0.066229 | 0.068946 | 0.018325 | 0.016475 | 0.841412 | 0.513563 | 0.087879 | 0.870545 | 0.062500 | 0.147959 |
| 19 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Zero Imputation | Oversampling | k-Nearest Neighbors (k-NN)_Oversampling_Zero Imputation_fold_4 | Class 1 | 3146 | 434 | 173 | 43 | 0.986516 | 0.714992 | 0.565178 | 0.115438 | 0.130355 | 0.131378 | 0.054405 | 0.049655 | 0.840095 | 0.538923 | 0.124098 | 0.867182 | 0.090147 | 0.199074 |
| 20 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Mean Imputation | Oversampling | k-Nearest Neighbors (k-NN)_Oversampling_Mean Imputation_fold_0 | Class 1 | 3156 | 404 | 207 | 30 | 0.990584 | 0.661211 | 0.545113 | 0.100545 | 0.090225 | 0.099853 | 0.009959 | 0.009438 | 0.839083 | 0.506550 | 0.089419 | 0.860416 | 0.069124 | 0.126582 |
| 21 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Mean Imputation | Oversampling | k-Nearest Neighbors (k-NN)_Oversampling_Mean Imputation_fold_1 | Class 1 | 3190 | 443 | 132 | 32 | 0.990068 | 0.770435 | 0.552373 | 0.081366 | 0.104746 | 0.104619 | 0.044970 | 0.038409 | 0.848565 | 0.536592 | 0.100156 | 0.882030 | 0.067368 | 0.195122 |
| 22 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Mean Imputation | Oversampling | k-Nearest Neighbors (k-NN)_Oversampling_Mean Imputation_fold_2 | Class 1 | 3145 | 465 | 156 | 31 | 0.990239 | 0.748792 | 0.558970 | 0.091710 | 0.117940 | 0.127480 | 0.023737 | 0.020728 | 0.836450 | 0.518483 | 0.090776 | 0.869790 | 0.062500 | 0.165775 |
| 23 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Mean Imputation | Oversampling | k-Nearest Neighbors (k-NN)_Oversampling_Mean Imputation_fold_3 | Class 1 | 3167 | 433 | 167 | 29 | 0.990926 | 0.721667 | 0.539427 | 0.078303 | 0.078853 | 0.085261 | 0.018735 | 0.016862 | 0.841939 | 0.513841 | 0.088146 | 0.870856 | 0.062771 | 0.147959 |
| 24 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Mean Imputation | Oversampling | k-Nearest Neighbors (k-NN)_Oversampling_Mean Imputation_fold_4 | Class 1 | 3148 | 432 | 174 | 42 | 0.986834 | 0.712871 | 0.576148 | 0.110522 | 0.152297 | 0.158199 | 0.051699 | 0.047255 | 0.840358 | 0.536887 | 0.121739 | 0.867221 | 0.088608 | 0.194444 |
| 25 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Median Imputation | Oversampling | k-Nearest Neighbors (k-NN)_Oversampling_Median Imputation_fold_0 | Class 1 | 3156 | 404 | 198 | 39 | 0.987793 | 0.671096 | 0.554961 | 0.111374 | 0.109923 | 0.111955 | 0.038487 | 0.036334 | 0.841454 | 0.525537 | 0.114706 | 0.863107 | 0.088036 | 0.164557 |
| 26 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Median Imputation | Oversampling | k-Nearest Neighbors (k-NN)_Oversampling_Median Imputation_fold_1 | Class 1 | 3166 | 467 | 131 | 33 | 0.989684 | 0.780936 | 0.551633 | 0.080609 | 0.103266 | 0.107138 | 0.043692 | 0.036739 | 0.842507 | 0.536338 | 0.099398 | 0.878537 | 0.066000 | 0.201220 |
| 27 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Median Imputation | Oversampling | k-Nearest Neighbors (k-NN)_Oversampling_Median Imputation_fold_2 | Class 1 | 3159 | 451 | 159 | 28 | 0.991214 | 0.739344 | 0.541171 | 0.087225 | 0.082343 | 0.090324 | 0.016164 | 0.014251 | 0.839347 | 0.512401 | 0.084084 | 0.871180 | 0.058455 | 0.149733 |
| 28 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Median Imputation | Oversampling | k-Nearest Neighbors (k-NN)_Oversampling_Median Imputation_fold_3 | Class 1 | 3172 | 428 | 172 | 24 | 0.992491 | 0.713333 | 0.539729 | 0.090781 | 0.079459 | 0.088141 | 0.002432 | 0.002201 | 0.841939 | 0.501780 | 0.074074 | 0.870247 | 0.053097 | 0.122449 |
| 29 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Median Imputation | Oversampling | k-Nearest Neighbors (k-NN)_Oversampling_Median Imputation_fold_4 | Class 1 | 3158 | 422 | 170 | 46 | 0.985643 | 0.712838 | 0.576763 | 0.116750 | 0.153527 | 0.155364 | 0.066999 | 0.061420 | 0.844046 | 0.547543 | 0.134503 | 0.869930 | 0.098291 | 0.212963 |
| 30 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Zero Imputation | Undersampling | k-Nearest Neighbors (k-NN)_Undersampling_Zero Imputation_fold_0 | Class 1 | 2172 | 1388 | 114 | 123 | 0.946405 | 0.924101 | 0.598085 | 0.114938 | 0.196169 | 0.135390 | 0.063805 | 0.036793 | 0.604425 | 0.564550 | 0.140732 | 0.705476 | 0.081403 | 0.518987 |
| 31 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Zero Imputation | Undersampling | k-Nearest Neighbors (k-NN)_Undersampling_Zero Imputation_fold_1 | Class 1 | 2251 | 1382 | 71 | 93 | 0.960324 | 0.951136 | 0.631722 | 0.113075 | 0.263444 | 0.186671 | 0.077858 | 0.038756 | 0.617329 | 0.593336 | 0.113484 | 0.728252 | 0.063051 | 0.567073 |
| 32 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Zero Imputation | Undersampling | k-Nearest Neighbors (k-NN)_Undersampling_Zero Imputation_fold_2 | Class 1 | 2175 | 1435 | 80 | 107 | 0.953111 | 0.947195 | 0.608780 | 0.141824 | 0.217560 | 0.174686 | 0.076969 | 0.039385 | 0.601001 | 0.587343 | 0.123771 | 0.711256 | 0.069390 | 0.572193 |
| 33 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Zero Imputation | Undersampling | k-Nearest Neighbors (k-NN)_Undersampling_Zero Imputation_fold_3 | Class 1 | 2288 | 1312 | 96 | 100 | 0.958124 | 0.931818 | 0.588710 | 0.118650 | 0.177421 | 0.145760 | 0.066734 | 0.037059 | 0.629083 | 0.572880 | 0.124378 | 0.731644 | 0.070822 | 0.510204 |
| 34 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Zero Imputation | Undersampling | k-Nearest Neighbors (k-NN)_Undersampling_Zero Imputation_fold_4 | Class 1 | 2160 | 1420 | 100 | 116 | 0.949033 | 0.934211 | 0.602570 | 0.111931 | 0.205139 | 0.168731 | 0.066260 | 0.036265 | 0.599579 | 0.570194 | 0.132420 | 0.705169 | 0.075521 | 0.537037 |
| 35 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Mean Imputation | Undersampling | k-Nearest Neighbors (k-NN)_Undersampling_Mean Imputation_fold_0 | Class 1 | 2181 | 1379 | 108 | 129 | 0.944156 | 0.927371 | 0.602571 | 0.124466 | 0.205143 | 0.160987 | 0.077593 | 0.044804 | 0.608375 | 0.578472 | 0.147851 | 0.708448 | 0.085544 | 0.544304 |
| 36 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Mean Imputation | Undersampling | k-Nearest Neighbors (k-NN)_Undersampling_Mean Imputation_fold_1 | Class 1 | 2258 | 1375 | 81 | 83 | 0.964545 | 0.944368 | 0.609237 | 0.118706 | 0.218475 | 0.162051 | 0.053344 | 0.026772 | 0.616539 | 0.563811 | 0.102343 | 0.727954 | 0.056927 | 0.506098 |
| 37 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Mean Imputation | Undersampling | k-Nearest Neighbors (k-NN)_Undersampling_Mean Imputation_fold_2 | Class 1 | 2179 | 1431 | 76 | 111 | 0.951528 | 0.949569 | 0.607938 | 0.110273 | 0.215875 | 0.197184 | 0.086882 | 0.044458 | 0.603108 | 0.598592 | 0.128398 | 0.712781 | 0.071984 | 0.593583 |
| 38 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Mean Imputation | Undersampling | k-Nearest Neighbors (k-NN)_Undersampling_Mean Imputation_fold_3 | Class 1 | 2285 | 1315 | 93 | 103 | 0.956868 | 0.933949 | 0.595391 | 0.113706 | 0.190782 | 0.160232 | 0.073297 | 0.040590 | 0.629083 | 0.580116 | 0.127633 | 0.731588 | 0.072638 | 0.525510 |
| 39 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Mean Imputation | Undersampling | k-Nearest Neighbors (k-NN)_Undersampling_Mean Imputation_fold_4 | Class 1 | 2185 | 1395 | 92 | 124 | 0.946297 | 0.938130 | 0.605689 | 0.128707 | 0.211378 | 0.184409 | 0.087195 | 0.048096 | 0.608272 | 0.592205 | 0.142939 | 0.711794 | 0.081633 | 0.574074 |
| 40 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Median Imputation | Undersampling | k-Nearest Neighbors (k-NN)_Undersampling_Median Imputation_fold_0 | Class 1 | 2141 | 1419 | 104 | 133 | 0.941513 | 0.931714 | 0.601548 | 0.112933 | 0.203096 | 0.162586 | 0.080007 | 0.045294 | 0.598894 | 0.581293 | 0.148686 | 0.700879 | 0.085696 | 0.561181 |
| 41 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Median Imputation | Undersampling | k-Nearest Neighbors (k-NN)_Undersampling_Median Imputation_fold_1 | Class 1 | 2241 | 1392 | 72 | 92 | 0.960566 | 0.950820 | 0.632310 | 0.104149 | 0.264620 | 0.207762 | 0.074086 | 0.036719 | 0.614432 | 0.588911 | 0.111650 | 0.726049 | 0.061995 | 0.560976 |
| 42 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Median Imputation | Undersampling | k-Nearest Neighbors (k-NN)_Undersampling_Median Imputation_fold_2 | Class 1 | 2198 | 1412 | 78 | 109 | 0.952752 | 0.947651 | 0.612385 | 0.129117 | 0.224771 | 0.191752 | 0.084677 | 0.043758 | 0.607585 | 0.595876 | 0.127635 | 0.716361 | 0.071663 | 0.582888 |
| 43 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Median Imputation | Undersampling | k-Nearest Neighbors (k-NN)_Undersampling_Median Imputation_fold_3 | Class 1 | 2343 | 1257 | 94 | 102 | 0.958282 | 0.930422 | 0.610182 | 0.119809 | 0.220364 | 0.171241 | 0.079041 | 0.045001 | 0.644099 | 0.585621 | 0.131190 | 0.742909 | 0.075055 | 0.520408 |
| 44 | k-Nearest Neighbors (k-NN) | {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': N... | Median Imputation | Undersampling | k-Nearest Neighbors (k-NN)_Undersampling_Median Imputation_fold_4 | Class 1 | 2142 | 1438 | 84 | 132 | 0.941953 | 0.944809 | 0.614230 | 0.115921 | 0.228461 | 0.209435 | 0.098516 | 0.053087 | 0.599052 | 0.604718 | 0.147816 | 0.704283 | 0.084076 | 0.611111 |
model
XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=None, n_jobs=None,
num_parallel_tree=None, random_state=None, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=None, n_jobs=None,
num_parallel_tree=None, random_state=None, ...)import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB, ComplementNB, MultinomialNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from imblearn.over_sampling import RandomOverSampler, SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.combine import SMOTEENN, SMOTETomek
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc, make_scorer
from sklearn.impute import SimpleImputer
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistGradientBoostingClassifier
#from catboost import CatBoostClassifier
import os
import json
# Redefine function without print
def end_timer():
end_time = time.time()
execution_time = end_time - start_time
execution_time = round(execution_time, 2)
return execution_time
# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.columns[1:479]
target = df['label']
# Define the imbalanced data handling techniques
sampling_techniques = {
'None': None,
'Oversampling': RandomOverSampler(),
'Undersampling': RandomUnderSampler(),
}
# Define the imputation techniques
imputation_techniques = {
'None': None,
'Zero Imputation': SimpleImputer(strategy='constant', fill_value=0),
'Mean Imputation': SimpleImputer(strategy='mean'),
'Median Imputation': SimpleImputer(strategy='median')
}
# Create a list to store the results
results_2 = []
# Define k-fold cross-validation
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)
# Iterate through each model
for model_name, model in [
('Logistic Regression', LogisticRegression(hyperparameter)),
('k-Nearest Neighbors (k-NN)', KNeighborsClassifier()),
# ('Support Vector Machines (SVM)', SVC(hyperparameters, probability=True)),
('Neural Networks', MLPClassifier()),
('Naive Bayes',GaussianNB()),
('Decision Trees', DecisionTreeClassifier()),
('Random Forest', RandomForestClassifier()),
('Gradient Boosting Machines (GBM)', GradientBoostingClassifier()),
('XGBoost', XGBClassifier()),
('LightGBM', LGBMClassifier()),
# ('CatBoost', CatBoostClassifier()),
('HistGradientBoostingClassifier', HistGradientBoostingClassifier())
]:
# Modify hyperparameters for the current model to include both 'None' and 'balanced'
models_to_try_both_hyperparams = ['Logistic Regression', 'Support Vector Machines (SVM)', 'Decision Trees', 'Random Forest', 'HistGradientBoostingClassifier']
hyperparameters = {'class_weight': [None, 'balanced']} if model_name in models_to_try_both_hyperparams else {}
# Iterate through each imbalanced data handling technique
for sampling_name, sampling_technique in sampling_techniques.items():
# Iterate through each hyperparameter
for hyperparameter in hyperparameters.get('class_weight', [None]):
if model_name in models_to_try_both_hyperparams:
# Set hyperparameters for the model
model.set_params(**{'class_weight': hyperparameter})
else:
pass
# Iterate through each imputation technique
for imputation_name, imputation_technique in imputation_techniques.items():
# Create a new dataframe to store the results for this model, technique combination
df_result_2 = pd.DataFrame(columns=['Algorithm', 'Metrics', 'Hyperparameters', 'Imputation Technique', 'Imbalance Class Technique', 'Model Unique Code', 'Execution Time', 'Shape_X0_0', 'Shape_X0_1', 'Shape_X1_0', 'Shape_X1_1'])
# Perform k-fold cross-validation
for fold, (train_index, test_index) in enumerate(k_fold.split(df[features], target)):
Init_Num_Class_0, Init_Num_Class_1, Res_Num_Class_0, Res_Num_Class_1, X_train_resampled, y_train_resampled, X_train, y_train = 0, 0, 0, 0, 0, 0, 0, 0
X_train, X_test = df[features].iloc[train_index], df[features].iloc[test_index]
y_train, y_test = target.iloc[train_index], target.iloc[test_index]
# Start timer
start_timer()
# Apply imputation technique on the train and test sets, if imputation_name is not None
if imputation_name is not None and imputation_technique is not None:
X_train_imputed = imputation_technique.fit_transform(X_train)
X_test_imputed = imputation_technique.transform(X_test)
else:
if model_name in ['Decision Trees', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
X_train_imputed, X_test_imputed = X_train, X_test
else:
break
# Apply the imbalanced data handling technique on the train set
if sampling_technique is not None:
X_train_resampled, y_train_resampled = sampling_technique.fit_resample(X_train_imputed, y_train)
else:
X_train_resampled, y_train_resampled = X_train_imputed, y_train
# Stop timer
execution_time = end_timer()
# Train the model on the resampled train set
model.fit(X_train_resampled, y_train_resampled)
# Make predictions on the test set
y_pred = model.predict(X_test_imputed)
y_prob = model.predict_proba(X_test_imputed)[:, 1]
# Calculate the evaluation metrics
metrics_df = calculate_classification_metrics_one_class(y_test, y_prob)
# Set 'Imputation Technique' based on the specified conditions
if imputation_name in ['Decision Trees', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
imputation_name = 'None'
else:
imputation_name = imputation_name
# Count samples and features for each label before resampling
Init_Num_Class_0 = X_train[y_train == 0].shape[0] # Number of samples with label 0
Init_Num_Class_1 = X_train[y_train == 1].shape[0] # Number of samples with label 1
Res_Num_Class_0 = X_train_resampled[y_train_resampled == 0].shape[0] # Number of features with label 0 after resampling
Res_Num_Class_1 = X_train_resampled[y_train_resampled == 1].shape[0] # Number of features with label 1 after resampling
# Append the result to the list
df_result_2 = pd.DataFrame({
'Algorithm': model_name,
'Hyperparameters': [model.get_params()],
'Imputation Technique': imputation_name,
'Imbalance Class Technique': sampling_name,
'Model Unique Code': f"{model_name}_{sampling_name}_{imputation_name}_{hyperparameter}_fold_{fold}",
'Execution Time': execution_time,
'Init_Num_Class_0': Init_Num_Class_0,
'Init_Num_Class_1': Init_Num_Class_1,
'Res_Num_Class_0': Res_Num_Class_0,
'Res_Num_Class_1': Res_Num_Class_1,
'execution_time_seg': execution_time,
**metrics_df # Unpack the metrics_df columns
})
# Append the result to the list
results_2.append(df_result_2)
# Combine all the results DataFrames into one final DataFrame
df_results_final_2 = pd.concat(results_2, ignore_index=True)
# Save each model in a separate file with a name corresponding to the model unique code
for model_unique_code in df_results_final_2['Model Unique Code'].unique():
df_model = df_results_final_2[df_results_final_2['Model Unique Code'] == model_unique_code]
df_model.to_csv(f"{model_unique_code}_2.csv", index=False)
# Display the final DataFrame
display(df_results_final_2)
[LightGBM] [Info] Number of positive: 763, number of negative: 14423 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.028803 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60337 [LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.050244 -> initscore=-2.939321 [LightGBM] [Info] Start training from score -2.939321 [LightGBM] [Info] Number of positive: 836, number of negative: 14350 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035796 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60388 [LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.055051 -> initscore=-2.842877 [LightGBM] [Info] Start training from score -2.842877 [LightGBM] [Info] Number of positive: 813, number of negative: 14373 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036476 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60401 [LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053536 -> initscore=-2.872376 [LightGBM] [Info] Start training from score -2.872376 [LightGBM] [Info] Number of positive: 804, number of negative: 14383 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035324 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60578 [LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052940 -> initscore=-2.884203 [LightGBM] [Info] Start training from score -2.884203 [LightGBM] [Info] Number of positive: 784, number of negative: 14403 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032897 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60304 [LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051623 -> initscore=-2.910783 [LightGBM] [Info] Start training from score -2.910783 [LightGBM] [Info] Number of positive: 763, number of negative: 14423 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.020700 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 59971 [LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.050244 -> initscore=-2.939321 [LightGBM] [Info] Start training from score -2.939321 [LightGBM] [Info] Number of positive: 836, number of negative: 14350 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029280 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60014 [LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 457 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.055051 -> initscore=-2.842877 [LightGBM] [Info] Start training from score -2.842877 [LightGBM] [Info] Number of positive: 813, number of negative: 14373 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.021057 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60026 [LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053536 -> initscore=-2.872376 [LightGBM] [Info] Start training from score -2.872376 [LightGBM] [Info] Number of positive: 804, number of negative: 14383 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.032641 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 60211 [LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052940 -> initscore=-2.884203 [LightGBM] [Info] Start training from score -2.884203 [LightGBM] [Info] Number of positive: 784, number of negative: 14403 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.018172 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 59920 [LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 457 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051623 -> initscore=-2.910783 [LightGBM] [Info] Start training from score -2.910783 [LightGBM] [Info] Number of positive: 763, number of negative: 14423 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034402 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62019 [LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.050244 -> initscore=-2.939321 [LightGBM] [Info] Start training from score -2.939321 [LightGBM] [Info] Number of positive: 836, number of negative: 14350 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.025857 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62314 [LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.055051 -> initscore=-2.842877 [LightGBM] [Info] Start training from score -2.842877 [LightGBM] [Info] Number of positive: 813, number of negative: 14373 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.030355 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61847 [LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053536 -> initscore=-2.872376 [LightGBM] [Info] Start training from score -2.872376 [LightGBM] [Info] Number of positive: 804, number of negative: 14383 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036096 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62515 [LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052940 -> initscore=-2.884203 [LightGBM] [Info] Start training from score -2.884203 [LightGBM] [Info] Number of positive: 784, number of negative: 14403 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034071 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62093 [LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051623 -> initscore=-2.910783 [LightGBM] [Info] Start training from score -2.910783 [LightGBM] [Info] Number of positive: 763, number of negative: 14423 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.020499 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 59911 [LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.050244 -> initscore=-2.939321 [LightGBM] [Info] Start training from score -2.939321 [LightGBM] [Info] Number of positive: 836, number of negative: 14350 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.021777 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 59950 [LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 457 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.055051 -> initscore=-2.842877 [LightGBM] [Info] Start training from score -2.842877 [LightGBM] [Info] Number of positive: 813, number of negative: 14373 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.018270 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 59972 [LightGBM] [Info] Number of data points in the train set: 15186, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053536 -> initscore=-2.872376 [LightGBM] [Info] Start training from score -2.872376 [LightGBM] [Info] Number of positive: 804, number of negative: 14383 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.020605 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60146 [LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052940 -> initscore=-2.884203 [LightGBM] [Info] Start training from score -2.884203 [LightGBM] [Info] Number of positive: 784, number of negative: 14403 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.019120 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 59870 [LightGBM] [Info] Number of data points in the train set: 15187, number of used features: 457 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051623 -> initscore=-2.910783 [LightGBM] [Info] Start training from score -2.910783 [LightGBM] [Info] Number of positive: 14423, number of negative: 14423 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.048959 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61462 [LightGBM] [Info] Number of data points in the train set: 28846, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14350, number of negative: 14350 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.054894 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61473 [LightGBM] [Info] Number of data points in the train set: 28700, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14373, number of negative: 14373 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.058655 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61372 [LightGBM] [Info] Number of data points in the train set: 28746, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14383, number of negative: 14383 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.053134 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61680 [LightGBM] [Info] Number of data points in the train set: 28766, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14403, number of negative: 14403 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042964 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61379 [LightGBM] [Info] Number of data points in the train set: 28806, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14423, number of negative: 14423 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.030900 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61103 [LightGBM] [Info] Number of data points in the train set: 28846, number of used features: 461 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14350, number of negative: 14350 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029946 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61139 [LightGBM] [Info] Number of data points in the train set: 28700, number of used features: 461 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14373, number of negative: 14373 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.025467 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61121 [LightGBM] [Info] Number of data points in the train set: 28746, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14383, number of negative: 14383 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.025948 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61321 [LightGBM] [Info] Number of data points in the train set: 28766, number of used features: 461 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14403, number of negative: 14403 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.030841 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61054 [LightGBM] [Info] Number of data points in the train set: 28806, number of used features: 461 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14423, number of negative: 14423 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.053725 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60769 [LightGBM] [Info] Number of data points in the train set: 28846, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14350, number of negative: 14350 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.050554 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60848 [LightGBM] [Info] Number of data points in the train set: 28700, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14373, number of negative: 14373 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.054027 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60552 [LightGBM] [Info] Number of data points in the train set: 28746, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14383, number of negative: 14383 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041971 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61150 [LightGBM] [Info] Number of data points in the train set: 28766, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14403, number of negative: 14403 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041682 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60682 [LightGBM] [Info] Number of data points in the train set: 28806, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14423, number of negative: 14423 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036337 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60880 [LightGBM] [Info] Number of data points in the train set: 28846, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14350, number of negative: 14350 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.027610 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60975 [LightGBM] [Info] Number of data points in the train set: 28700, number of used features: 461 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14373, number of negative: 14373 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032489 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60915 [LightGBM] [Info] Number of data points in the train set: 28746, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14383, number of negative: 14383 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029968 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61130 [LightGBM] [Info] Number of data points in the train set: 28766, number of used features: 461 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 14403, number of negative: 14403 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.028908 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 60858 [LightGBM] [Info] Number of data points in the train set: 28806, number of used features: 461 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 763, number of negative: 763 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008496 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 26520 [LightGBM] [Info] Number of data points in the train set: 1526, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 836, number of negative: 836 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009695 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 27953 [LightGBM] [Info] Number of data points in the train set: 1672, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 813, number of negative: 813 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008214 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 27552 [LightGBM] [Info] Number of data points in the train set: 1626, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 804, number of negative: 804 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007327 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 27531 [LightGBM] [Info] Number of data points in the train set: 1608, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 784, number of negative: 784 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008330 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 26861 [LightGBM] [Info] Number of data points in the train set: 1568, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 763, number of negative: 763 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004378 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 25547 [LightGBM] [Info] Number of data points in the train set: 1526, number of used features: 370 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 836, number of negative: 836 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005798 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 26754 [LightGBM] [Info] Number of data points in the train set: 1672, number of used features: 368 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 813, number of negative: 813 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004938 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 27339 [LightGBM] [Info] Number of data points in the train set: 1626, number of used features: 372 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 804, number of negative: 804 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004427 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 26352 [LightGBM] [Info] Number of data points in the train set: 1608, number of used features: 358 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 784, number of negative: 784 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005507 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 26046 [LightGBM] [Info] Number of data points in the train set: 1568, number of used features: 368 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 763, number of negative: 763 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007831 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 31496 [LightGBM] [Info] Number of data points in the train set: 1526, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 836, number of negative: 836 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010165 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 32977 [LightGBM] [Info] Number of data points in the train set: 1672, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 813, number of negative: 813 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009539 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 32893 [LightGBM] [Info] Number of data points in the train set: 1626, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 804, number of negative: 804 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009862 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 33481 [LightGBM] [Info] Number of data points in the train set: 1608, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 784, number of negative: 784 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007440 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 32304 [LightGBM] [Info] Number of data points in the train set: 1568, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 763, number of negative: 763 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007070 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28686 [LightGBM] [Info] Number of data points in the train set: 1526, number of used features: 358 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 836, number of negative: 836 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005490 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 30197 [LightGBM] [Info] Number of data points in the train set: 1672, number of used features: 362 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 813, number of negative: 813 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005271 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 30877 [LightGBM] [Info] Number of data points in the train set: 1626, number of used features: 374 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 804, number of negative: 804 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006307 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29923 [LightGBM] [Info] Number of data points in the train set: 1608, number of used features: 370 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 784, number of negative: 784 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005836 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29815 [LightGBM] [Info] Number of data points in the train set: 1568, number of used features: 366 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
| Algorithm | Hyperparameters | Imputation Technique | Imbalance Class Technique | Model Unique Code | Execution Time | Init_Num_Class_0 | Init_Num_Class_1 | Res_Num_Class_0 | Res_Num_Class_1 | execution_time_seg | Class | TP | TN | FP | FN | Sensitivity_TPR | Specificity | AUC_ROC | AUC_PRC | Gini | KS | MCC | Kappa | Accuracy | Balanced_Accuracy | F1 | F1_Weighted | Precision | Recall | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Logistic Regression | {'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, '... | Zero Imputation | None | Logistic Regression_None_Zero Imputation_None_fold_0 | 0.24 | 14423 | 763 | 14423 | 763 | 0.24 | Class 1 | 3556 | 4 | 236 | 1 | 0.999719 | 0.016667 | 0.599933 | 0.083530 | 0.199866 | 0.206333 | 0.020652 | 0.005700 | 0.936792 | 0.501548 | 0.008264 | 0.907492 | 0.200000 | 0.004219 |
| 1 | Logistic Regression | {'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, '... | Zero Imputation | None | Logistic Regression_None_Zero Imputation_None_fold_1 | 0.31 | 14350 | 836 | 14350 | 836 | 0.31 | Class 1 | 3628 | 5 | 163 | 1 | 0.999724 | 0.029762 | 0.563974 | 0.050731 | 0.127948 | 0.174778 | 0.024164 | 0.008743 | 0.955755 | 0.502361 | 0.011765 | 0.935664 | 0.166667 | 0.006098 |
| 2 | Logistic Regression | {'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, '... | Zero Imputation | None | Logistic Regression_None_Zero Imputation_None_fold_2 | 0.25 | 14373 | 813 | 14373 | 813 | 0.25 | Class 1 | 3605 | 5 | 187 | 0 | 1.000000 | 0.026042 | 0.552269 | 0.053489 | 0.104537 | 0.165550 | -0.008265 | -0.002572 | 0.949434 | 0.499307 | 0.000000 | 0.926089 | 0.000000 | 0.000000 |
| 3 | Logistic Regression | {'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, '... | Zero Imputation | None | Logistic Regression_None_Zero Imputation_None_fold_3 | 0.24 | 14383 | 804 | 14383 | 804 | 0.24 | Class 1 | 3594 | 6 | 196 | 0 | 1.000000 | 0.029703 | 0.549608 | 0.060222 | 0.099216 | 0.173520 | -0.009284 | -0.003077 | 0.946786 | 0.499167 | 0.000000 | 0.922444 | 0.000000 | 0.000000 |
| 4 | Logistic Regression | {'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, '... | Zero Imputation | None | Logistic Regression_None_Zero Imputation_None_fold_4 | 0.24 | 14403 | 784 | 14403 | 784 | 0.24 | Class 1 | 3578 | 2 | 214 | 2 | 0.999441 | 0.009259 | 0.550503 | 0.070592 | 0.101006 | 0.157107 | 0.062123 | 0.016146 | 0.943098 | 0.504350 | 0.018182 | 0.916500 | 0.500000 | 0.009259 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 715 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': 'balanced', 'early_stopping': 'auto', 'interactio... | Median Imputation | Undersampling | HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced_fold_0 | 0.73 | 14423 | 763 | 763 | 763 | 0.73 | Class 1 | 2231 | 1329 | 84 | 153 | 0.935822 | 0.940552 | 0.689108 | 0.120529 | 0.378217 | 0.296507 | 0.135013 | 0.078875 | 0.627864 | 0.636128 | 0.178010 | 0.723195 | 0.103239 | 0.645570 |
| 716 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': 'balanced', 'early_stopping': 'auto', 'interactio... | Median Imputation | Undersampling | HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced_fold_1 | 0.73 | 14350 | 836 | 836 | 836 | 0.73 | Class 1 | 2318 | 1315 | 61 | 103 | 0.957456 | 0.955669 | 0.687482 | 0.086227 | 0.374964 | 0.292018 | 0.111827 | 0.057216 | 0.637609 | 0.633044 | 0.130215 | 0.743442 | 0.072638 | 0.628049 |
| 717 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': 'balanced', 'early_stopping': 'auto', 'interactio... | Median Imputation | Undersampling | HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced_fold_2 | 0.75 | 14373 | 813 | 813 | 813 | 0.75 | Class 1 | 2293 | 1317 | 69 | 118 | 0.951058 | 0.950216 | 0.666724 | 0.086319 | 0.333448 | 0.269436 | 0.118798 | 0.063927 | 0.634975 | 0.633098 | 0.145499 | 0.737263 | 0.082230 | 0.631016 |
| 718 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': 'balanced', 'early_stopping': 'auto', 'interactio... | Median Imputation | Undersampling | HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced_fold_3 | 0.77 | 14383 | 804 | 804 | 804 | 0.77 | Class 1 | 2245 | 1355 | 69 | 127 | 0.946459 | 0.951545 | 0.681509 | 0.096901 | 0.363017 | 0.293169 | 0.123184 | 0.066204 | 0.624868 | 0.635785 | 0.151371 | 0.727830 | 0.085695 | 0.647959 |
| 719 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': 'balanced', 'early_stopping': 'auto', 'interactio... | Median Imputation | Undersampling | HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced_fold_4 | 0.76 | 14403 | 784 | 784 | 784 | 0.76 | Class 1 | 2326 | 1254 | 74 | 142 | 0.942464 | 0.944277 | 0.711052 | 0.130003 | 0.422105 | 0.325528 | 0.147550 | 0.086110 | 0.650158 | 0.653564 | 0.176179 | 0.743686 | 0.101719 | 0.657407 |
720 rows × 30 columns
# Order df_results_final_2 by 'Execution Time'
df_results_final_2 = df_results_final_2.sort_values(by=['Execution Time'], ascending=False)
display(df_results_final_2)
| Algorithm | Hyperparameters | Imputation Technique | Imbalance Class Technique | Model Unique Code | Execution Time | Init_Num_Class_0 | Init_Num_Class_1 | Res_Num_Class_0 | Res_Num_Class_1 | execution_time_seg | Class | TP | TN | FP | FN | Sensitivity_TPR | Specificity | AUC_ROC | AUC_PRC | Gini | KS | MCC | Kappa | Accuracy | Balanced_Accuracy | F1 | F1_Weighted | Precision | Recall | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 559 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | Median Imputation | None | LightGBM_None_Median Imputation_None_fold_4 | 0.97 | 14403 | 784 | 14403 | 784 | 0.97 | Class 1 | 3579 | 1 | 215 | 1 | 0.999721 | 0.004630 | 0.723510 | 0.136000 | 0.447020 | 0.353011 | 0.043916 | 0.008139 | 0.943098 | 0.502175 | 0.009174 | 0.915995 | 0.500000 | 0.004630 |
| 579 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | Median Imputation | Oversampling | LightGBM_Oversampling_Median Imputation_None_fold_4 | 0.97 | 14403 | 784 | 14403 | 14403 | 0.97 | Class 1 | 3075 | 505 | 133 | 83 | 0.973718 | 0.791536 | 0.707663 | 0.137440 | 0.415326 | 0.323081 | 0.155712 | 0.134426 | 0.831928 | 0.621599 | 0.206468 | 0.866205 | 0.141156 | 0.384259 |
| 578 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | Median Imputation | Oversampling | LightGBM_Oversampling_Median Imputation_None_fold_3 | 0.94 | 14383 | 804 | 14383 | 14383 | 0.94 | Class 1 | 3015 | 585 | 122 | 74 | 0.976044 | 0.827440 | 0.690359 | 0.103601 | 0.380717 | 0.298929 | 0.125638 | 0.101592 | 0.813751 | 0.607526 | 0.173099 | 0.857780 | 0.112291 | 0.377551 |
| 577 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | Median Imputation | Oversampling | LightGBM_Oversampling_Median Imputation_None_fold_2 | 0.94 | 14373 | 813 | 14373 | 14373 | 0.94 | Class 1 | 2990 | 620 | 107 | 80 | 0.973941 | 0.852820 | 0.694404 | 0.102240 | 0.388807 | 0.292502 | 0.142890 | 0.111302 | 0.808533 | 0.628031 | 0.180383 | 0.856578 | 0.114286 | 0.427807 |
| 576 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | Median Imputation | Oversampling | LightGBM_Oversampling_Median Imputation_None_fold_1 | 0.94 | 14350 | 836 | 14350 | 14350 | 0.94 | Class 1 | 3073 | 560 | 105 | 59 | 0.981162 | 0.842105 | 0.688934 | 0.090925 | 0.377868 | 0.319675 | 0.113158 | 0.088452 | 0.824862 | 0.602807 | 0.150702 | 0.869898 | 0.095315 | 0.359756 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 483 | XGBoost | {'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsam... | None | None | XGBoost_None_None_None_fold_3 | 0.00 | 14383 | 804 | 14383 | 804 | 0.00 | Class 1 | 3594 | 6 | 193 | 3 | 0.999166 | 0.030151 | 0.685021 | 0.120106 | 0.370041 | 0.304195 | 0.062059 | 0.024847 | 0.947576 | 0.506820 | 0.029268 | 0.924330 | 0.333333 | 0.015306 |
| 484 | XGBoost | {'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsam... | None | None | XGBoost_None_None_None_fold_4 | 0.00 | 14403 | 784 | 14403 | 784 | 0.00 | Class 1 | 3577 | 3 | 214 | 2 | 0.999441 | 0.013825 | 0.712983 | 0.129065 | 0.425966 | 0.352933 | 0.053788 | 0.015565 | 0.942835 | 0.504211 | 0.018100 | 0.916363 | 0.400000 | 0.009259 |
| 540 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | None | None | LightGBM_None_None_None_fold_0 | 0.00 | 14423 | 763 | 14423 | 763 | 0.00 | Class 1 | 3557 | 3 | 235 | 2 | 0.999438 | 0.012605 | 0.689955 | 0.132990 | 0.379909 | 0.292415 | 0.050672 | 0.013986 | 0.937319 | 0.503798 | 0.016529 | 0.908262 | 0.400000 | 0.008439 |
| 542 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | None | None | LightGBM_None_None_None_fold_2 | 0.00 | 14373 | 813 | 14373 | 813 | 0.00 | Class 1 | 3604 | 6 | 186 | 1 | 0.999723 | 0.031250 | 0.703826 | 0.112467 | 0.407651 | 0.314831 | 0.018591 | 0.006779 | 0.949434 | 0.501843 | 0.010309 | 0.926590 | 0.142857 | 0.005348 |
| 481 | XGBoost | {'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsam... | None | None | XGBoost_None_None_None_fold_1 | 0.00 | 14350 | 836 | 14350 | 836 | 0.00 | Class 1 | 3626 | 7 | 163 | 1 | 0.999724 | 0.041176 | 0.683174 | 0.091616 | 0.366347 | 0.300800 | 0.018491 | 0.007641 | 0.955228 | 0.502085 | 0.011628 | 0.935395 | 0.125000 | 0.006098 |
720 rows × 30 columns
# Sum 'Execution Time' by 'Algorithm' and divide by count of entries of that Algorithm
df_exec_time = (df_results_final_2.groupby('Algorithm')['Execution Time'].sum() / df_results_final_2.groupby('Algorithm')['Execution Time'].count())*df_results_final_2.groupby('Algorithm')['Execution Time'].count().sum()
df_exec_time = df_exec_time.sort_values(ascending=False)
df_exec_time
Algorithm Logistic Regression 342.00 k-Nearest Neighbors (k-NN) 341.76 Naive Bayes 340.64 Neural Networks 337.28 Gradient Boosting Machines (GBM) 332.16 Random Forest 331.76 LightGBM 301.32 XGBoost 272.76 Decision Trees 269.16 HistGradientBoostingClassifier 268.44 Name: Execution Time, dtype: float64
df_results_final_2.groupby('Algorithm')['Execution Time'].count().sum()
720
import seaborn as sns
import matplotlib.pyplot as plt
# Extract relevant data for plotting
data_to_plot = df_results_final_2[['Algorithm', 'Imbalance Class Technique', 'Imputation Technique', 'AUC_ROC', 'AUC_PRC', 'Gini', 'KS', 'Balanced_Accuracy', 'F1_Weighted', 'Model Unique Code', 'Execution Time']]
# Extract fold number from 'Model Unique Code'
data_to_plot['Fold'] = data_to_plot['Model Unique Code'].str.extract(r'fold_(\d+)').astype(int)
# Exclude the text _fold_ and the fold number from 'Model Unique Code'
data_to_plot['Model Code'] = data_to_plot['Model Unique Code'].str.extract(r'^(.*?)_fold_\d+').astype(str)
# Melt the dataframe to long format for easier plotting
melted_data = pd.melt(data_to_plot, id_vars=['Model Code', 'Imbalance Class Technique', 'Imputation Technique', 'Model Unique Code', 'Fold'],
value_vars=['AUC_ROC', 'AUC_PRC', 'KS', 'Balanced_Accuracy', 'F1_Weighted', 'Execution Time'],
var_name='Metrics', value_name='Metric Value')
# Create separate boxplots for each metric
metrics = melted_data['Metrics'].unique()
for metric in metrics:
plt.figure(figsize=(21, 7))
sns.set(style='whitegrid')
sns.boxplot(x='Model Code', y='Metric Value', hue='Imbalance Class Technique',
data=melted_data[melted_data['Metrics'] == metric], palette='Set3', showfliers=False, dodge=True)
plt.title(f'{metric} Comparison by Algorithm, Imbalance Technique, Imputation Technique, and Class Weight')
plt.xlabel('Model Code')
plt.ylabel('Metric Value')
plt.xticks(rotation=90, fontsize=8)
plt.legend(title='Imbalance Class Technique')
plt.show()
import plotly.express as px
# Create a function to generate boxplots for each metric
def plot_metric_boxplots(data, metric):
fig = px.box(data, x='Model Code', y='Metric Value', color='Imbalance Class Technique',
category_orders={'Imbalance Class Technique': ['None', 'Over-sampling', 'Under-sampling']},
points=False,
title=f'{metric} Comparison by Algorithm, Imbalance Technique, Imputation Technique, and Class Weight',
labels={'Metric Value': metric, 'Model Code': 'Model Code'},
width=1200, height=600)
# Customize the layout
fig.update_layout(xaxis=dict(tickangle=90, showticklabels=False), legend_title_text='Imbalance Class Technique')
#fig.update_layout(xaxis=dict(tickangle=90), legend_title_text='Imbalance Class Technique')
# Show the plot
fig.show()
# Melt the dataframe to long format for easier plotting
melted_data = pd.melt(data_to_plot, id_vars=['Model Code', 'Imbalance Class Technique', 'Imputation Technique', 'Model Unique Code', 'Fold'],
value_vars=['AUC_ROC', 'AUC_PRC', 'KS', 'Balanced_Accuracy', 'F1_Weighted', 'Execution Time'],
var_name='Metrics', value_name='Metric Value')
# Create boxplots for each metric
for metric in melted_data['Metrics'].unique():
# Filter relevant columns for hover_data
relevant_columns = ['Model Code', 'Imbalance Class Technique', 'Imputation Technique', 'Model Unique Code', 'Fold', 'Metrics', 'Metric Value']
plot_metric_boxplots(melted_data[melted_data['Metrics'] == metric][relevant_columns], metric)
# List 'Algorithm' with AUC_ROC > 0.65
list_AUC_ROC = df_results_final_2[df_results_final_2['AUC_ROC'] > 0.65]['Algorithm'].unique()
# List 'Algorithm' with KS > 0.3
list_KS = df_results_final_2[df_results_final_2['KS'] > 0.35]['Algorithm'].unique()
# Lis 'Algorithm' with Balanced_Accuracy > 0.60
List_Bal_Acc = df_results_final_2[df_results_final_2['Balanced_Accuracy'] > 0.65]['Algorithm'].unique()
# Get the intersection of the above three lists
list_intersection = (list(set(list_AUC_ROC).intersection(list_KS).intersection(List_Bal_Acc)))
print(list_AUC_ROC, list_KS, List_Bal_Acc, sep='\n')
print(list_intersection)
['LightGBM' 'XGBoost' 'HistGradientBoostingClassifier' 'Gradient Boosting Machines (GBM)' 'Logistic Regression' 'Random Forest' 'Neural Networks' 'k-Nearest Neighbors (k-NN)'] ['LightGBM' 'Gradient Boosting Machines (GBM)' 'HistGradientBoostingClassifier' 'Random Forest' 'XGBoost'] ['Gradient Boosting Machines (GBM)' 'HistGradientBoostingClassifier' 'XGBoost' 'Random Forest' 'LightGBM'] ['LightGBM', 'XGBoost', 'HistGradientBoostingClassifier', 'Gradient Boosting Machines (GBM)', 'Random Forest']
data_to_plot
| Algorithm | Imbalance Class Technique | Imputation Technique | AUC_ROC | AUC_PRC | Gini | KS | Balanced_Accuracy | F1_Weighted | Model Unique Code | Execution Time | Fold | Model Code | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Logistic Regression | None | Zero Imputation | 0.599933 | 0.083530 | 0.199866 | 0.206333 | 0.501548 | 0.907492 | Logistic Regression_None_Zero Imputation_None_fold_0 | 0.24 | 0 | Logistic Regression_None_Zero Imputation_None |
| 1 | Logistic Regression | None | Zero Imputation | 0.563974 | 0.050731 | 0.127948 | 0.174778 | 0.502361 | 0.935664 | Logistic Regression_None_Zero Imputation_None_fold_1 | 0.31 | 1 | Logistic Regression_None_Zero Imputation_None |
| 2 | Logistic Regression | None | Zero Imputation | 0.552269 | 0.053489 | 0.104537 | 0.165550 | 0.499307 | 0.926089 | Logistic Regression_None_Zero Imputation_None_fold_2 | 0.25 | 2 | Logistic Regression_None_Zero Imputation_None |
| 3 | Logistic Regression | None | Zero Imputation | 0.549608 | 0.060222 | 0.099216 | 0.173520 | 0.499167 | 0.922444 | Logistic Regression_None_Zero Imputation_None_fold_3 | 0.24 | 3 | Logistic Regression_None_Zero Imputation_None |
| 4 | Logistic Regression | None | Zero Imputation | 0.550503 | 0.070592 | 0.101006 | 0.157107 | 0.504350 | 0.916500 | Logistic Regression_None_Zero Imputation_None_fold_4 | 0.24 | 4 | Logistic Regression_None_Zero Imputation_None |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 715 | HistGradientBoostingClassifier | Undersampling | Median Imputation | 0.689108 | 0.120529 | 0.378217 | 0.296507 | 0.636128 | 0.723195 | HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced_fold_0 | 0.73 | 0 | HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced |
| 716 | HistGradientBoostingClassifier | Undersampling | Median Imputation | 0.687482 | 0.086227 | 0.374964 | 0.292018 | 0.633044 | 0.743442 | HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced_fold_1 | 0.73 | 1 | HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced |
| 717 | HistGradientBoostingClassifier | Undersampling | Median Imputation | 0.666724 | 0.086319 | 0.333448 | 0.269436 | 0.633098 | 0.737263 | HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced_fold_2 | 0.75 | 2 | HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced |
| 718 | HistGradientBoostingClassifier | Undersampling | Median Imputation | 0.681509 | 0.096901 | 0.363017 | 0.293169 | 0.635785 | 0.727830 | HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced_fold_3 | 0.77 | 3 | HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced |
| 719 | HistGradientBoostingClassifier | Undersampling | Median Imputation | 0.711052 | 0.130003 | 0.422105 | 0.325528 | 0.653564 | 0.743686 | HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced_fold_4 | 0.76 | 4 | HistGradientBoostingClassifier_Undersampling_Median Imputation_balanced |
720 rows × 13 columns
df_results_final_2.columns
Index(['Algorithm', 'Hyperparameters', 'Imputation Technique',
'Imbalance Class Technique', 'Model Unique Code', 'Execution Time',
'Init_Num_Class_0', 'Init_Num_Class_1', 'Res_Num_Class_0',
'Res_Num_Class_1', 'execution_time', 'Class', 'TP', 'TN', 'FP', 'FN',
'Sensitivity_TPR', 'Specificity', 'AUC_ROC', 'AUC_PRC', 'Gini', 'KS',
'MCC', 'Kappa', 'Accuracy', 'Balanced_Accuracy', 'F1', 'F1_Weighted',
'Precision', 'Recall'],
dtype='object')
metrics_df
| Class | TP | TN | FP | FN | Sensitivity_TPR | Specificity | AUC_ROC | AUC_PRC | Gini | KS | MCC | Kappa | Accuracy | Balanced_Accuracy | F1 | F1_Weighted | Precision | Recall | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Class 1 | 2321 | 1259 | 81 | 135 | 0.945033 | 0.939552 | 0.6749 | 0.102557 | 0.349801 | 0.276397 | 0.131349 | 0.076726 | 0.646997 | 0.636662 | 0.167702 | 0.741382 | 0.096844 | 0.625 |
# Set the display character limit to 100 and max number of columns to 100
pd.set_option('display.max_columns', 100)
pd.set_option('display.max_colwidth', 100)
display(df_results_final_2)
| Algorithm | Hyperparameters | Imputation Technique | Imbalance Class Technique | Model Unique Code | Class | TP | TN | FP | FN | Sensitivity_TPR | Specificity | AUC_ROC | AUC_PRC | Gini | KS | MCC | Kappa | Accuracy | Balanced_Accuracy | F1 | F1_Weighted | Precision | Recall | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Neural Networks | {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e... | Zero Imputation | Oversampling | Neural Networks_Oversampling_Zero Imputation_fold_0 | Class 1 | 2721 | 839 | 136 | 101 | 0.964210 | 0.860513 | 0.618631 | 0.111923 | 0.237261 | 0.219866 | 0.106769 | 0.079889 | 0.743218 | 0.595243 | 0.171623 | 0.805838 | 0.107447 | 0.426160 |
| 1 | Neural Networks | {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e... | Zero Imputation | Oversampling | Neural Networks_Oversampling_Zero Imputation_fold_1 | Class 1 | 2847 | 786 | 97 | 67 | 0.977008 | 0.890147 | 0.653543 | 0.083387 | 0.307085 | 0.265538 | 0.093613 | 0.063939 | 0.767448 | 0.596093 | 0.131760 | 0.834042 | 0.078546 | 0.408537 |
| 2 | Neural Networks | {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e... | Zero Imputation | Oversampling | Neural Networks_Oversampling_Zero Imputation_fold_2 | Class 1 | 2911 | 699 | 105 | 82 | 0.972603 | 0.869403 | 0.646090 | 0.094143 | 0.292180 | 0.255255 | 0.131092 | 0.097716 | 0.788254 | 0.622437 | 0.169421 | 0.843730 | 0.104994 | 0.438503 |
| 3 | Neural Networks | {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e... | Zero Imputation | Oversampling | Neural Networks_Oversampling_Zero Imputation_fold_3 | Class 1 | 2735 | 865 | 100 | 96 | 0.966090 | 0.896373 | 0.641382 | 0.101850 | 0.282764 | 0.266633 | 0.126982 | 0.087695 | 0.745785 | 0.624759 | 0.165946 | 0.814717 | 0.099896 | 0.489796 |
| 4 | Neural Networks | {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e... | Zero Imputation | Oversampling | Neural Networks_Oversampling_Zero Imputation_fold_4 | Class 1 | 2812 | 768 | 125 | 91 | 0.968653 | 0.860022 | 0.650405 | 0.108391 | 0.300811 | 0.272589 | 0.114475 | 0.086204 | 0.764752 | 0.603386 | 0.169302 | 0.823502 | 0.105937 | 0.421296 |
| 5 | Neural Networks | {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e... | Zero Imputation | Undersampling | Neural Networks_Undersampling_Zero Imputation_fold_0 | Class 1 | 2315 | 1245 | 110 | 127 | 0.947993 | 0.918819 | 0.605889 | 0.174622 | 0.211778 | 0.228541 | 0.093739 | 0.057539 | 0.643139 | 0.593073 | 0.157862 | 0.735168 | 0.092566 | 0.535865 |
| 6 | Neural Networks | {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e... | Zero Imputation | Undersampling | Neural Networks_Undersampling_Zero Imputation_fold_1 | Class 1 | 2164 | 1469 | 62 | 102 | 0.954987 | 0.959504 | 0.588771 | 0.109206 | 0.177543 | 0.221773 | 0.089819 | 0.042701 | 0.596787 | 0.608801 | 0.117579 | 0.711865 | 0.064927 | 0.621951 |
| 7 | Neural Networks | {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e... | Zero Imputation | Undersampling | Neural Networks_Undersampling_Zero Imputation_fold_2 | Class 1 | 2475 | 1135 | 124 | 63 | 0.975177 | 0.901509 | 0.596698 | 0.142273 | 0.193396 | 0.229597 | 0.010474 | 0.006313 | 0.668422 | 0.511247 | 0.090975 | 0.762447 | 0.052588 | 0.336898 |
| 8 | Neural Networks | {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e... | Zero Imputation | Undersampling | Neural Networks_Undersampling_Zero Imputation_fold_3 | Class 1 | 2531 | 1069 | 114 | 82 | 0.968618 | 0.903635 | 0.610656 | 0.163082 | 0.221312 | 0.227211 | 0.058456 | 0.036755 | 0.688356 | 0.560711 | 0.121752 | 0.775003 | 0.071242 | 0.418367 |
| 9 | Neural Networks | {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'e... | Zero Imputation | Undersampling | Neural Networks_Undersampling_Zero Imputation_fold_4 | Class 1 | 1747 | 1833 | 71 | 145 | 0.923362 | 0.962710 | 0.560347 | 0.177950 | 0.120695 | 0.195143 | 0.073864 | 0.032960 | 0.498419 | 0.579643 | 0.132179 | 0.617967 | 0.073306 | 0.671296 |
import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, HistGradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from imblearn.over_sampling import RandomOverSampler
from imblearn.under_sampling import RandomUnderSampler
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.experimental import enable_hist_gradient_boosting
from imblearn.over_sampling import RandomOverSampler
from imblearn.under_sampling import RandomUnderSampler
from imblearn.combine import SMOTEENN, SMOTETomek
import os
import json
import time
# Redefine function without print
def end_timer():
end_time = time.time()
execution_time = end_time - start_time
execution_time = round(execution_time, 2)
return execution_time
# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.columns[1:479]
target = df['label']
# Define the imbalanced data handling techniques
sampling_techniques = {
'None': None,
'Oversampling': RandomOverSampler(),
'Undersampling': RandomUnderSampler(),
}
# Define the imputation techniques
imputation_techniques = {
'None': None,
'Zero Imputation': SimpleImputer(strategy='constant', fill_value=0),
}
# Create a list to store the results
results_3 = []
# Define k-fold cross-validation
k_fold = KFold(n_splits=10, shuffle=True, random_state=42)
# Iterate through each model
for model_name, model in [
('Random Forest', RandomForestClassifier()),
('Gradient Boosting Machines (GBM)', GradientBoostingClassifier()),
('XGBoost', XGBClassifier()),
('LightGBM', LGBMClassifier()),
('HistGradientBoostingClassifier', HistGradientBoostingClassifier())
]:
# Filter only specified algorithms
if model_name not in ['LightGBM', 'XGBoost', 'HistGradientBoostingClassifier', 'Gradient Boosting Machines (GBM)', 'Random Forest']:
continue
# Define the normalization and scaling techniques
normalization_scalers = {
'MinMaxScaler [0, 1]': MinMaxScaler(feature_range=(0, 1)),
'MinMaxScaler [1, 2]': MinMaxScaler(feature_range=(1, 2)),
'StandardScaler': StandardScaler(),
'StandardScaler MinMaxScaler [0, 1]': [StandardScaler(), MinMaxScaler(feature_range=(0, 1))]
}
# Iterate through each normalization and scaling technique
for scaler_name, scaler in normalization_scalers.items():
# Iterate through each imbalanced data handling technique
for sampling_name, sampling_technique in sampling_techniques.items():
# Iterate through each imputation technique
for imputation_name, imputation_technique in imputation_techniques.items():
# Create a new dataframe to store the results for this model, technique combination
df_result_3 = pd.DataFrame(columns=['Algorithm', 'Metrics', 'Hyperparameters', 'Imputation Technique', 'Imbalance Class Technique', 'Model Unique Code', 'Execution Time', 'Shape_X0_0', 'Shape_X0_1', 'Shape_X1_0', 'Shape_X1_1'])
# Perform k-fold cross-validation
for fold, (train_index, test_index) in enumerate(k_fold.split(df[features], target)):
Init_Num_Class_0, Init_Num_Class_1, Res_Num_Class_0, Res_Num_Class_1, X_train_resampled, y_train_resampled, X_train, y_train = 0, 0, 0, 0, 0, 0, 0, 0
X_train, X_test = df[features].iloc[train_index], df[features].iloc[test_index]
y_train, y_test = target.iloc[train_index], target.iloc[test_index]
# Apply imputation technique on the train and test sets, if imputation_name is not None
if imputation_name is not None and imputation_technique is not None:
X_train_imputed = imputation_technique.fit_transform(X_train)
X_test_imputed = imputation_technique.transform(X_test)
else:
if model_name in ['Decision Trees', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
X_train_imputed, X_test_imputed = X_train, X_test
else:
break
# Apply normalization and scaling technique on the train and test sets
if isinstance(scaler, list):
# For multiple scalers, apply them sequentially
for s in scaler:
X_train_imputed = s.fit_transform(X_train_imputed)
X_test_imputed = s.transform(X_test_imputed)
else:
X_train_imputed = scaler.fit_transform(X_train_imputed)
X_test_imputed = scaler.transform(X_test_imputed)
# Apply the imbalanced data handling technique on the train set
if sampling_technique is not None:
X_train_resampled, y_train_resampled = sampling_technique.fit_resample(X_train_imputed, y_train)
else:
X_train_resampled, y_train_resampled = X_train_imputed, y_train
# Start timer
start_time = time.time()
# Train the model on the resampled train set
model.fit(X_train_resampled, y_train_resampled)
# Stop timer
execution_time = end_timer()
# Make predictions on the test set
y_pred = model.predict(X_test_imputed)
y_prob = model.predict_proba(X_test_imputed)[:, 1]
# Calculate the evaluation metrics
metrics_df = calculate_classification_metrics_one_class(y_test, y_prob)
# Set 'Imputation Technique' based on the specified conditions
if imputation_name in ['Decision Trees', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
imputation_name = 'None'
else:
imputation_name = imputation_name
# Count samples and features for each label before resampling
Init_Num_Class_0 = X_train[y_train == 0].shape[0] # Number of samples with label 0
Init_Num_Class_1 = X_train[y_train == 1].shape[0] # Number of samples with label 1
Res_Num_Class_0 = X_train_resampled[y_train_resampled == 0].shape[0] # Number of features with label 0 after resampling
Res_Num_Class_1 = X_train_resampled[y_train_resampled == 1].shape[0] # Number of features with label 1 after resampling
# Append the result to the list
df_result_3 = pd.DataFrame({
'Algorithm': model_name,
'Hyperparameters': [model.get_params()],
'Imputation Technique': imputation_name,
'Imbalance Class Technique': sampling_name,
'Model Unique Code': f"{model_name}_{sampling_name}_{imputation_name}_{scaler_name}_fold_{fold}",
'Execution Time': execution_time,
'Init_Num_Class_0': Init_Num_Class_0,
'Init_Num_Class_1': Init_Num_Class_1,
'Res_Num_Class_0': Res_Num_Class_0,
'Res_Num_Class_1': Res_Num_Class_1,
'execution_time_seg': execution_time,
**metrics_df # Unpack the metrics_df columns
})
# Append the result to the list
results_3.append(df_result_3)
# Combine all the results DataFrames into one final DataFrame
df_results_final_3 = pd.concat(results_3, ignore_index=True)
# Save each model in a separate file with a name corresponding to the model unique code
for model_unique_code in df_results_final_3['Model Unique Code'].unique():
df_model = df_results_final_3[df_results_final_3['Model Unique Code'] == model_unique_code]
df_model.to_csv(f"{model_unique_code}_3.csv", index=False)
# Display the final DataFrame
display(df_results_final_3)
[LightGBM] [Info] Number of positive: 876, number of negative: 16208 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037260 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61413 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894 [LightGBM] [Info] Start training from score -2.917894 [LightGBM] [Info] Number of positive: 887, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041253 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61402 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736 [LightGBM] [Info] Start training from score -2.904736 [LightGBM] [Info] Number of positive: 919, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036153 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61361 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318 [LightGBM] [Info] Start training from score -2.867318 [LightGBM] [Info] Number of positive: 917, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037679 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61498 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682 [LightGBM] [Info] Start training from score -2.869682 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039870 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61484 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 909, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036642 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61459 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939 [LightGBM] [Info] Start training from score -2.878939 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036338 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61537 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 900, number of negative: 16185 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029741 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61551 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445 [LightGBM] [Info] Start training from score -2.889445 [LightGBM] [Info] Number of positive: 879, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037739 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61470 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352 [LightGBM] [Info] Start training from score -2.914352 [LightGBM] [Info] Number of positive: 905, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037534 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61416 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596 [LightGBM] [Info] Start training from score -2.883596 [LightGBM] [Info] Number of positive: 876, number of negative: 16208 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.022168 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61009 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894 [LightGBM] [Info] Start training from score -2.917894 [LightGBM] [Info] Number of positive: 887, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.023689 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61052 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736 [LightGBM] [Info] Start training from score -2.904736 [LightGBM] [Info] Number of positive: 919, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.027205 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61002 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318 [LightGBM] [Info] Start training from score -2.867318 [LightGBM] [Info] Number of positive: 917, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.023541 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61149 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682 [LightGBM] [Info] Start training from score -2.869682 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.024981 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61131 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 909, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.022967 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61103 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939 [LightGBM] [Info] Start training from score -2.878939 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.023707 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61133 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 900, number of negative: 16185 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.028832 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61201 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445 [LightGBM] [Info] Start training from score -2.889445 [LightGBM] [Info] Number of positive: 879, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.024577 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61117 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352 [LightGBM] [Info] Start training from score -2.914352 [LightGBM] [Info] Number of positive: 905, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.020534 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61061 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596 [LightGBM] [Info] Start training from score -2.883596 [LightGBM] [Info] Number of positive: 16208, number of negative: 16208 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071376 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62418 [LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16197, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.067002 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62525 [LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16165, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.072405 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62378 [LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16168, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.066744 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62547 [LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.021989 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 62513 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16176, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070971 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62544 [LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.070296 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62604 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16185, number of negative: 16185 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071644 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62515 [LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16206, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073837 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62561 [LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16180, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.069411 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62426 [LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16208, number of negative: 16208 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042013 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62119 [LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16197, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035043 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62169 [LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16165, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038075 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62029 [LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16168, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039667 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62119 [LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033802 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62276 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16176, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031823 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62317 [LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032411 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62127 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 461 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16185, number of negative: 16185 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035819 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62184 [LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16206, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037010 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62247 [LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16180, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.028524 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62080 [LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 876, number of negative: 876 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008440 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28428 [LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 887, number of negative: 887 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009921 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28797 [LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 919, number of negative: 919 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009483 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29183 [LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 917, number of negative: 917 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009668 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29270 [LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008482 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29396 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 909, number of negative: 909 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008302 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29369 [LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009506 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28731 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 900, number of negative: 900 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009127 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29482 [LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 879, number of negative: 879 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008003 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28640 [LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 905, number of negative: 905 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007911 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28887 [LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 876, number of negative: 876 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004915 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28634 [LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 372 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 887, number of negative: 887 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005662 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29181 [LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 374 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 919, number of negative: 919 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005035 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29865 [LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 374 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 917, number of negative: 917 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005011 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29560 [LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 376 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006201 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29777 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 374 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 909, number of negative: 909 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004915 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29186 [LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 374 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005580 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29720 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 376 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 900, number of negative: 900 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005379 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29646 [LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 380 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 879, number of negative: 879 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005102 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29204 [LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 374 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 905, number of negative: 905 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005777 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29582 [LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 368 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 876, number of negative: 16208 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047285 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63435 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894 [LightGBM] [Info] Start training from score -2.917894 [LightGBM] [Info] Number of positive: 887, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.054277 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63640 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736 [LightGBM] [Info] Start training from score -2.904736 [LightGBM] [Info] Number of positive: 919, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.049583 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63346 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318 [LightGBM] [Info] Start training from score -2.867318 [LightGBM] [Info] Number of positive: 917, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.053588 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63532 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682 [LightGBM] [Info] Start training from score -2.869682 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047432 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63518 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 909, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.056939 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63541 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939 [LightGBM] [Info] Start training from score -2.878939 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.048629 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63672 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 900, number of negative: 16185 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.050535 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63705 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445 [LightGBM] [Info] Start training from score -2.889445 [LightGBM] [Info] Number of positive: 879, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.049595 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63644 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352 [LightGBM] [Info] Start training from score -2.914352 [LightGBM] [Info] Number of positive: 905, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034629 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63324 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596 [LightGBM] [Info] Start training from score -2.883596 [LightGBM] [Info] Number of positive: 876, number of negative: 16208 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.018472 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63032 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894 [LightGBM] [Info] Start training from score -2.917894 [LightGBM] [Info] Number of positive: 887, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.027051 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63155 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736 [LightGBM] [Info] Start training from score -2.904736 [LightGBM] [Info] Number of positive: 919, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.028255 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62892 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318 [LightGBM] [Info] Start training from score -2.867318 [LightGBM] [Info] Number of positive: 917, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.028327 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63131 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682 [LightGBM] [Info] Start training from score -2.869682 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.027020 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63113 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 909, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.023368 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63138 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939 [LightGBM] [Info] Start training from score -2.878939 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.025159 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63265 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 900, number of negative: 16185 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.025989 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63158 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445 [LightGBM] [Info] Start training from score -2.889445 [LightGBM] [Info] Number of positive: 879, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.028992 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 63247 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352 [LightGBM] [Info] Start training from score -2.914352 [LightGBM] [Info] Number of positive: 905, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.018496 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62925 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596 [LightGBM] [Info] Start training from score -2.883596 [LightGBM] [Info] Number of positive: 16208, number of negative: 16208 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.052828 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62398 [LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16197, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046696 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62234 [LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16165, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.051230 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62242 [LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16168, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042586 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62292 [LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.048814 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62290 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16176, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044105 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62216 [LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.043414 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62498 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16185, number of negative: 16185 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044390 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62473 [LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16206, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042116 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62360 [LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16180, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.052986 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62145 [LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16208, number of negative: 16208 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041107 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61860 [LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16197, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039950 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61774 [LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16165, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039971 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61650 [LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16168, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040029 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61796 [LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041024 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61797 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16176, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040533 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61758 [LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.039418 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62027 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 461 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16185, number of negative: 16185 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.039823 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 61919 [LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16206, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.037081 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61884 [LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16180, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034903 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61669 [LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 876, number of negative: 876 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006545 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 31720 [LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 887, number of negative: 887 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006266 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 32707 [LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 919, number of negative: 919 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007839 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 33019 [LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 917, number of negative: 917 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008070 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 32599 [LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008039 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 33005 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 909, number of negative: 909 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008167 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 32255 [LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007055 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 32406 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 900, number of negative: 900 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006664 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 32551 [LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 879, number of negative: 879 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006223 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 31534 [LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 905, number of negative: 905 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007786 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 32678 [LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 876, number of negative: 876 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006510 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 33485 [LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 374 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 887, number of negative: 887 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006583 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 35037 [LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 374 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 919, number of negative: 919 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006168 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 34173 [LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 374 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 917, number of negative: 917 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005509 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 33683 [LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 376 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005482 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 34922 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 384 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 909, number of negative: 909 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006284 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 34375 [LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 378 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006070 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 35025 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 370 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 900, number of negative: 900 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005750 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 34905 [LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 376 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 879, number of negative: 879 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005391 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 34978 [LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 378 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 905, number of negative: 905 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006047 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 35076 [LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 370 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 876, number of negative: 16208 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.041815 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 61475 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894 [LightGBM] [Info] Start training from score -2.917894 [LightGBM] [Info] Number of positive: 887, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041670 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61551 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736 [LightGBM] [Info] Start training from score -2.904736 [LightGBM] [Info] Number of positive: 919, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.049451 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61488 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318 [LightGBM] [Info] Start training from score -2.867318 [LightGBM] [Info] Number of positive: 917, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.050384 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61567 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682 [LightGBM] [Info] Start training from score -2.869682 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.054621 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61559 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 909, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.049796 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61526 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939 [LightGBM] [Info] Start training from score -2.878939 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047575 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61606 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 900, number of negative: 16185 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.053988 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61627 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445 [LightGBM] [Info] Start training from score -2.889445 [LightGBM] [Info] Number of positive: 879, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047358 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61520 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352 [LightGBM] [Info] Start training from score -2.914352 [LightGBM] [Info] Number of positive: 905, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.049442 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61504 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596 [LightGBM] [Info] Start training from score -2.883596 [LightGBM] [Info] Number of positive: 876, number of negative: 16208 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.026248 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61066 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894 [LightGBM] [Info] Start training from score -2.917894 [LightGBM] [Info] Number of positive: 887, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.024109 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61061 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736 [LightGBM] [Info] Start training from score -2.904736 [LightGBM] [Info] Number of positive: 919, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.026159 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61018 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318 [LightGBM] [Info] Start training from score -2.867318 [LightGBM] [Info] Number of positive: 917, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.024050 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61164 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682 [LightGBM] [Info] Start training from score -2.869682 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.024195 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61147 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 909, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029819 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61127 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939 [LightGBM] [Info] Start training from score -2.878939 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.026731 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61202 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 900, number of negative: 16185 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.026547 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61231 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445 [LightGBM] [Info] Start training from score -2.889445 [LightGBM] [Info] Number of positive: 879, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.023489 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61118 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352 [LightGBM] [Info] Start training from score -2.914352 [LightGBM] [Info] Number of positive: 905, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.021368 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61084 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596 [LightGBM] [Info] Start training from score -2.883596 [LightGBM] [Info] Number of positive: 16208, number of negative: 16208 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045186 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62543 [LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16197, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045889 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62723 [LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16165, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041667 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62663 [LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16168, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.055937 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62662 [LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.060341 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62727 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16176, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.058925 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62751 [LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.066769 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62682 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16185, number of negative: 16185 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047483 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62716 [LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16206, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042862 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62672 [LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16180, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047189 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62659 [LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16208, number of negative: 16208 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.045071 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62154 [LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16197, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034858 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62310 [LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16165, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046122 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62282 [LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16168, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.044882 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62366 [LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040097 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62398 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16176, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046519 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62381 [LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.047131 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62322 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 461 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16185, number of negative: 16185 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035040 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62398 [LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16206, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.041418 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62312 [LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16180, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042296 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62379 [LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 876, number of negative: 876 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006771 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29875 [LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 887, number of negative: 887 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005793 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 30664 [LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 919, number of negative: 919 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006991 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 30452 [LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 917, number of negative: 917 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006360 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 31238 [LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007183 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 30293 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 909, number of negative: 909 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007435 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 30749 [LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006224 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 30668 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 900, number of negative: 900 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005933 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 30651 [LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 879, number of negative: 879 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006575 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29877 [LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 905, number of negative: 905 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007203 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 30791 [LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 876, number of negative: 876 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004961 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28601 [LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 376 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 887, number of negative: 887 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005732 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29023 [LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 378 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 919, number of negative: 919 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005757 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28998 [LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 378 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 917, number of negative: 917 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005044 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28599 [LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 370 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005608 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28488 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 374 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 909, number of negative: 909 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005140 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29079 [LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 378 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004689 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28551 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 376 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 900, number of negative: 900 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005174 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28972 [LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 376 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 879, number of negative: 879 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004892 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28134 [LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 370 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 905, number of negative: 905 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007210 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 28589 [LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 374 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 876, number of negative: 16208 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.048636 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61413 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894 [LightGBM] [Info] Start training from score -2.917894 [LightGBM] [Info] Number of positive: 887, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040717 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61402 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736 [LightGBM] [Info] Start training from score -2.904736 [LightGBM] [Info] Number of positive: 919, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040135 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61361 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318 [LightGBM] [Info] Start training from score -2.867318 [LightGBM] [Info] Number of positive: 917, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.029583 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61498 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682 [LightGBM] [Info] Start training from score -2.869682 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036507 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61484 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 909, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040623 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61459 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939 [LightGBM] [Info] Start training from score -2.878939 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036551 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61537 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 900, number of negative: 16185 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040487 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61551 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445 [LightGBM] [Info] Start training from score -2.889445 [LightGBM] [Info] Number of positive: 879, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036960 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61470 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352 [LightGBM] [Info] Start training from score -2.914352 [LightGBM] [Info] Number of positive: 905, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036803 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61416 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596 [LightGBM] [Info] Start training from score -2.883596 [LightGBM] [Info] Number of positive: 876, number of negative: 16208 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.022233 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61009 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051276 -> initscore=-2.917894 [LightGBM] [Info] Start training from score -2.917894 [LightGBM] [Info] Number of positive: 887, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.018690 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61052 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051920 -> initscore=-2.904736 [LightGBM] [Info] Start training from score -2.904736 [LightGBM] [Info] Number of positive: 919, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.020157 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61002 [LightGBM] [Info] Number of data points in the train set: 17084, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053793 -> initscore=-2.867318 [LightGBM] [Info] Start training from score -2.867318 [LightGBM] [Info] Number of positive: 917, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.022771 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61149 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053673 -> initscore=-2.869682 [LightGBM] [Info] Start training from score -2.869682 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.020630 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61131 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 909, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.022528 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61103 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.053205 -> initscore=-2.878939 [LightGBM] [Info] Start training from score -2.878939 [LightGBM] [Info] Number of positive: 904, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.026071 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61133 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052912 -> initscore=-2.884764 [LightGBM] [Info] Start training from score -2.884764 [LightGBM] [Info] Number of positive: 900, number of negative: 16185 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.022391 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61201 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052678 -> initscore=-2.889445 [LightGBM] [Info] Start training from score -2.889445 [LightGBM] [Info] Number of positive: 879, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.024247 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61117 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.051449 -> initscore=-2.914352 [LightGBM] [Info] Start training from score -2.914352 [LightGBM] [Info] Number of positive: 905, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.020155 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 61061 [LightGBM] [Info] Number of data points in the train set: 17085, number of used features: 459 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.052970 -> initscore=-2.883596 [LightGBM] [Info] Start training from score -2.883596 [LightGBM] [Info] Number of positive: 16208, number of negative: 16208 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.071758 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62441 [LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16197, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.068242 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62531 [LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16165, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.055842 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62338 [LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16168, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.055987 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62480 [LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.064688 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62537 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16176, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.052835 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62585 [LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.056076 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62528 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16185, number of negative: 16185 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.073528 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62564 [LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16206, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.065818 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62539 [LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16180, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.057757 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62439 [LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 478 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16208, number of negative: 16208 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.031782 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62044 [LightGBM] [Info] Number of data points in the train set: 32416, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16197, number of negative: 16197 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034899 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62063 [LightGBM] [Info] Number of data points in the train set: 32394, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16165, number of negative: 16165 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040883 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62013 [LightGBM] [Info] Number of data points in the train set: 32330, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16168, number of negative: 16168 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.035477 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62079 [LightGBM] [Info] Number of data points in the train set: 32336, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.034024 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62216 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16176, number of negative: 16176 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032373 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62221 [LightGBM] [Info] Number of data points in the train set: 32352, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16181, number of negative: 16181 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.036436 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62122 [LightGBM] [Info] Number of data points in the train set: 32362, number of used features: 461 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16185, number of negative: 16185 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.038723 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62224 [LightGBM] [Info] Number of data points in the train set: 32370, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16206, number of negative: 16206 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.040779 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62169 [LightGBM] [Info] Number of data points in the train set: 32412, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 16180, number of negative: 16180 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.033885 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 62147 [LightGBM] [Info] Number of data points in the train set: 32360, number of used features: 463 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 876, number of negative: 876 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007895 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28784 [LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 887, number of negative: 887 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008513 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29177 [LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 919, number of negative: 919 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008251 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29142 [LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 917, number of negative: 917 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008439 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29321 [LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008438 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28874 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 909, number of negative: 909 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008157 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29148 [LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009792 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28661 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 900, number of negative: 900 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008515 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29546 [LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 879, number of negative: 879 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008903 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28761 [LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 905, number of negative: 905 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009640 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29201 [LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 477 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 876, number of negative: 876 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005890 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28844 [LightGBM] [Info] Number of data points in the train set: 1752, number of used features: 380 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 887, number of negative: 887 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005040 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29364 [LightGBM] [Info] Number of data points in the train set: 1774, number of used features: 374 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 919, number of negative: 919 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005928 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 30121 [LightGBM] [Info] Number of data points in the train set: 1838, number of used features: 378 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 917, number of negative: 917 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006779 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29597 [LightGBM] [Info] Number of data points in the train set: 1834, number of used features: 374 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005261 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29662 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 378 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 909, number of negative: 909 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006329 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29611 [LightGBM] [Info] Number of data points in the train set: 1818, number of used features: 382 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 904, number of negative: 904 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007190 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29764 [LightGBM] [Info] Number of data points in the train set: 1808, number of used features: 372 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 900, number of negative: 900 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006522 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29616 [LightGBM] [Info] Number of data points in the train set: 1800, number of used features: 376 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 879, number of negative: 879 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005637 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 28870 [LightGBM] [Info] Number of data points in the train set: 1758, number of used features: 372 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 [LightGBM] [Info] Number of positive: 905, number of negative: 905 [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006550 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 29734 [LightGBM] [Info] Number of data points in the train set: 1810, number of used features: 378 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
| Algorithm | Hyperparameters | Imputation Technique | Imbalance Class Technique | Model Unique Code | Execution Time | Init_Num_Class_0 | Init_Num_Class_1 | Res_Num_Class_0 | Res_Num_Class_1 | execution_time_seg | Class | TP | TN | FP | FN | Sensitivity_TPR | Specificity | AUC_ROC | AUC_PRC | Gini | KS | MCC | Kappa | Accuracy | Balanced_Accuracy | F1 | F1_Weighted | Precision | Recall | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Random Forest | {'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': No... | Zero Imputation | None | Random Forest_None_Zero Imputation_MinMaxScaler [0, 1]_fold_0 | 15.16 | 16208 | 876 | 16208 | 876 | 15.16 | Class 1 | 1770 | 5 | 122 | 2 | 0.998871 | 0.039370 | 0.604995 | 0.112620 | 0.209991 | 0.193180 | 0.054269 | 0.023722 | 0.933123 | 0.506656 | 0.030534 | 0.904325 | 0.285714 | 0.016129 |
| 1 | Random Forest | {'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': No... | Zero Imputation | None | Random Forest_None_Zero Imputation_MinMaxScaler [0, 1]_fold_1 | 16.00 | 16197 | 887 | 16197 | 887 | 16.00 | Class 1 | 1776 | 10 | 110 | 3 | 0.998314 | 0.083333 | 0.665332 | 0.119504 | 0.330664 | 0.290881 | 0.060105 | 0.035780 | 0.936809 | 0.510475 | 0.047619 | 0.912593 | 0.230769 | 0.026549 |
| 2 | Random Forest | {'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': No... | Zero Imputation | None | Random Forest_None_Zero Imputation_MinMaxScaler [0, 1]_fold_2 | 15.78 | 16165 | 919 | 16165 | 919 | 15.78 | Class 1 | 1804 | 14 | 78 | 3 | 0.998340 | 0.152174 | 0.694706 | 0.089273 | 0.389412 | 0.342745 | 0.062938 | 0.047124 | 0.951553 | 0.514668 | 0.061224 | 0.936153 | 0.176471 | 0.037037 |
| 3 | Random Forest | {'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': No... | Zero Imputation | None | Random Forest_None_Zero Imputation_MinMaxScaler [0, 1]_fold_3 | 16.04 | 16168 | 917 | 16168 | 917 | 16.04 | Class 1 | 1806 | 9 | 83 | 0 | 1.000000 | 0.097826 | 0.636679 | 0.080354 | 0.273358 | 0.258827 | -0.014761 | -0.008630 | 0.951528 | 0.497521 | 0.000000 | 0.932518 | 0.000000 | 0.000000 |
| 4 | Random Forest | {'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': No... | Zero Imputation | None | Random Forest_None_Zero Imputation_MinMaxScaler [0, 1]_fold_4 | 15.51 | 16181 | 904 | 16181 | 904 | 15.51 | Class 1 | 1794 | 8 | 96 | 0 | 1.000000 | 0.076923 | 0.657643 | 0.116523 | 0.315286 | 0.282036 | -0.015017 | -0.007842 | 0.945205 | 0.497780 | 0.000000 | 0.922676 | 0.000000 | 0.000000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 955 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | Zero Imputation | Undersampling | HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_... | 6.67 | 16176 | 909 | 909 | 909 | 6.67 | Class 1 | 1137 | 670 | 33 | 58 | 0.951464 | 0.953058 | 0.688300 | 0.096823 | 0.376600 | 0.316151 | 0.117131 | 0.061655 | 0.629610 | 0.633291 | 0.141636 | 0.734024 | 0.079670 | 0.637363 |
| 956 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | Zero Imputation | Undersampling | HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_... | 6.63 | 16181 | 904 | 904 | 904 | 6.63 | Class 1 | 1137 | 665 | 38 | 58 | 0.951464 | 0.945946 | 0.676846 | 0.105696 | 0.353693 | 0.311217 | 0.106105 | 0.057466 | 0.629610 | 0.617566 | 0.141636 | 0.732385 | 0.080221 | 0.604167 |
| 957 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | Zero Imputation | Undersampling | HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_... | 6.63 | 16185 | 900 | 900 | 900 | 6.63 | Class 1 | 1096 | 702 | 39 | 61 | 0.947277 | 0.947368 | 0.663968 | 0.086106 | 0.327937 | 0.258943 | 0.100046 | 0.053156 | 0.609589 | 0.609783 | 0.141367 | 0.715430 | 0.079948 | 0.610000 |
| 958 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | Zero Imputation | Undersampling | HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_... | 6.73 | 16206 | 879 | 879 | 879 | 6.73 | Class 1 | 1106 | 671 | 35 | 86 | 0.927852 | 0.950425 | 0.702368 | 0.127047 | 0.404735 | 0.363981 | 0.166216 | 0.096587 | 0.628030 | 0.666571 | 0.195900 | 0.722215 | 0.113606 | 0.710744 |
| 959 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | Zero Imputation | Undersampling | HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_... | 8.73 | 16180 | 905 | 905 | 905 | 8.73 | Class 1 | 1158 | 645 | 28 | 67 | 0.945306 | 0.958395 | 0.737484 | 0.122450 | 0.474969 | 0.396771 | 0.156518 | 0.085256 | 0.645416 | 0.673763 | 0.166047 | 0.744369 | 0.094101 | 0.705263 |
960 rows × 30 columns
# Order df_results_final_3 by 'Balanced_Accuracy'
display(df_results_final_3.sort_values(by=['Balanced_Accuracy'], ascending=False).head(10))
display(df_results_final_3.sort_values(by=['AUC_ROC'], ascending=False).head(10))
display(df_results_final_3.sort_values(by=['KS'], ascending=False).head(10))
| Algorithm | Hyperparameters | Imputation Technique | Imbalance Class Technique | Model Unique Code | Execution Time | Init_Num_Class_0 | Init_Num_Class_1 | Res_Num_Class_0 | Res_Num_Class_1 | execution_time_seg | Class | TP | TN | FP | FN | Sensitivity_TPR | Specificity | AUC_ROC | AUC_PRC | Gini | KS | MCC | Kappa | Accuracy | Balanced_Accuracy | F1 | F1_Weighted | Precision | Recall | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | Random Forest | {'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': No... | Zero Imputation | Undersampling | Random Forest_Undersampling_Zero Imputation_StandardScaler_fold_1 | 1.12 | 16197 | 887 | 887 | 887 | 1.12 | Class 1 | 1173 | 613 | 28 | 85 | 0.932432 | 0.956318 | 0.747470 | 0.150728 | 0.494941 | 0.421523 | 0.200674 | 0.119422 | 0.662454 | 0.704494 | 0.209618 | 0.751141 | 0.121777 | 0.752212 |
| 111 | Random Forest | {'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': No... | Zero Imputation | Undersampling | Random Forest_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_fold_1 | 1.09 | 16197 | 887 | 887 | 887 | 1.09 | Class 1 | 1200 | 586 | 30 | 83 | 0.935308 | 0.951299 | 0.733686 | 0.138561 | 0.467372 | 0.409314 | 0.201268 | 0.122985 | 0.675619 | 0.703203 | 0.212276 | 0.761036 | 0.124066 | 0.734513 |
| 762 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | None | Undersampling | HistGradientBoostingClassifier_Undersampling_None_MinMaxScaler [0, 1]_fold_2 | 6.89 | 16165 | 919 | 919 | 919 | 6.89 | Class 1 | 1196 | 622 | 21 | 60 | 0.952229 | 0.967341 | 0.731033 | 0.125523 | 0.462067 | 0.436010 | 0.167898 | 0.087710 | 0.661401 | 0.699303 | 0.157274 | 0.761229 | 0.087977 | 0.740741 |
| 469 | XGBoost | {'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsam... | None | Undersampling | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_9 | 1.33 | 16180 | 905 | 905 | 905 | 1.33 | Class 1 | 1225 | 578 | 27 | 68 | 0.947409 | 0.955372 | 0.752801 | 0.124339 | 0.505602 | 0.412430 | 0.181875 | 0.105469 | 0.681243 | 0.697606 | 0.183536 | 0.771010 | 0.105263 | 0.715789 |
| 462 | XGBoost | {'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsam... | None | Undersampling | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_2 | 1.39 | 16165 | 919 | 919 | 919 | 1.39 | Class 1 | 1167 | 651 | 20 | 61 | 0.950326 | 0.970194 | 0.746740 | 0.110602 | 0.493481 | 0.425804 | 0.164881 | 0.083660 | 0.646656 | 0.697500 | 0.153846 | 0.750138 | 0.085674 | 0.753086 |
| 598 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | Zero Imputation | Undersampling | LightGBM_Undersampling_Zero Imputation_MinMaxScaler [1, 2]_fold_8 | 0.49 | 16206 | 879 | 879 | 879 | 0.49 | Class 1 | 1167 | 610 | 32 | 89 | 0.929140 | 0.950156 | 0.717218 | 0.128402 | 0.434435 | 0.405559 | 0.198685 | 0.121602 | 0.661749 | 0.696131 | 0.217073 | 0.748114 | 0.127325 | 0.735537 |
| 359 | XGBoost | {'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsam... | Zero Imputation | Undersampling | XGBoost_Undersampling_Zero Imputation_MinMaxScaler [1, 2]_fold_9 | 0.87 | 16180 | 905 | 905 | 905 | 0.87 | Class 1 | 1161 | 642 | 24 | 71 | 0.942370 | 0.963964 | 0.740401 | 0.102955 | 0.480801 | 0.441212 | 0.176181 | 0.095876 | 0.649104 | 0.695648 | 0.175743 | 0.747008 | 0.099579 | 0.747368 |
| 949 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | None | Undersampling | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_9 | 7.00 | 16180 | 905 | 905 | 905 | 7.00 | Class 1 | 1177 | 626 | 25 | 70 | 0.943865 | 0.961598 | 0.756073 | 0.149272 | 0.512146 | 0.419003 | 0.176307 | 0.097496 | 0.657007 | 0.694821 | 0.176991 | 0.753011 | 0.100575 | 0.736842 |
| 761 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | None | Undersampling | HistGradientBoostingClassifier_Undersampling_None_MinMaxScaler [0, 1]_fold_1 | 7.03 | 16197 | 887 | 887 | 887 | 7.03 | Class 1 | 1110 | 676 | 27 | 86 | 0.928094 | 0.961593 | 0.729999 | 0.124344 | 0.459999 | 0.395564 | 0.184640 | 0.103676 | 0.629805 | 0.691281 | 0.196571 | 0.725997 | 0.112861 | 0.761062 |
| 419 | XGBoost | {'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsam... | Zero Imputation | Undersampling | XGBoost_Undersampling_Zero Imputation_StandardScaler_fold_9 | 0.86 | 16180 | 905 | 905 | 905 | 0.86 | Class 1 | 1201 | 602 | 27 | 68 | 0.946414 | 0.957075 | 0.734571 | 0.118427 | 0.469142 | 0.389643 | 0.174251 | 0.098763 | 0.668599 | 0.690951 | 0.177778 | 0.761710 | 0.101493 | 0.715789 |
| Algorithm | Hyperparameters | Imputation Technique | Imbalance Class Technique | Model Unique Code | Execution Time | Init_Num_Class_0 | Init_Num_Class_1 | Res_Num_Class_0 | Res_Num_Class_1 | execution_time_seg | Class | TP | TN | FP | FN | Sensitivity_TPR | Specificity | AUC_ROC | AUC_PRC | Gini | KS | MCC | Kappa | Accuracy | Balanced_Accuracy | F1 | F1_Weighted | Precision | Recall | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 549 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | None | None | LightGBM_None_None_MinMaxScaler [1, 2]_fold_9 | 2.03 | 16180 | 905 | 16180 | 905 | 2.03 | Class 1 | 1802 | 1 | 95 | 0 | 1.000000 | 0.010417 | 0.770786 | 0.154870 | 0.541571 | 0.437411 | -0.005270 | -0.001044 | 0.949420 | 0.499723 | 0.000000 | 0.925300 | 0.000000 | 0.000000 |
| 859 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | Zero Imputation | None | HistGradientBoostingClassifier_None_Zero Imputation_StandardScaler_fold_9 | 2.70 | 16180 | 905 | 16180 | 905 | 2.70 | Class 1 | 1803 | 0 | 95 | 0 | 1.000000 | 0.000000 | 0.767262 | 0.144842 | 0.534524 | 0.421269 | 0.000000 | 0.000000 | 0.949947 | 0.500000 | 0.000000 | 0.925563 | 0.000000 | 0.000000 |
| 739 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | Zero Imputation | None | HistGradientBoostingClassifier_None_Zero Imputation_MinMaxScaler [0, 1]_fold_9 | 3.53 | 16180 | 905 | 16180 | 905 | 3.53 | Class 1 | 1803 | 0 | 95 | 0 | 1.000000 | 0.000000 | 0.759383 | 0.150476 | 0.518767 | 0.404711 | 0.000000 | 0.000000 | 0.949947 | 0.500000 | 0.000000 | 0.925563 | 0.000000 | 0.000000 |
| 949 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | None | Undersampling | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_9 | 7.00 | 16180 | 905 | 905 | 905 | 7.00 | Class 1 | 1177 | 626 | 25 | 70 | 0.943865 | 0.961598 | 0.756073 | 0.149272 | 0.512146 | 0.419003 | 0.176307 | 0.097496 | 0.657007 | 0.694821 | 0.176991 | 0.753011 | 0.100575 | 0.736842 |
| 902 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | None | None | HistGradientBoostingClassifier_None_None_StandardScaler MinMaxScaler [0, 1]_fold_2 | 2.72 | 16165 | 919 | 16165 | 919 | 2.72 | Class 1 | 1816 | 2 | 81 | 0 | 1.000000 | 0.024096 | 0.755134 | 0.117710 | 0.510268 | 0.427271 | -0.006854 | -0.002060 | 0.956293 | 0.499450 | 0.000000 | 0.935957 | 0.000000 | 0.000000 |
| 909 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | None | None | HistGradientBoostingClassifier_None_None_StandardScaler MinMaxScaler [0, 1]_fold_9 | 2.60 | 16180 | 905 | 16180 | 905 | 2.60 | Class 1 | 1803 | 0 | 95 | 0 | 1.000000 | 0.000000 | 0.754663 | 0.138978 | 0.509327 | 0.391435 | 0.000000 | 0.000000 | 0.949947 | 0.500000 | 0.000000 | 0.925563 | 0.000000 | 0.000000 |
| 559 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | Zero Imputation | None | LightGBM_None_Zero Imputation_MinMaxScaler [1, 2]_fold_9 | 1.57 | 16180 | 905 | 16180 | 905 | 1.57 | Class 1 | 1803 | 0 | 95 | 0 | 1.000000 | 0.000000 | 0.753884 | 0.144285 | 0.507768 | 0.430131 | 0.000000 | 0.000000 | 0.949947 | 0.500000 | 0.000000 | 0.925563 | 0.000000 | 0.000000 |
| 469 | XGBoost | {'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsam... | None | Undersampling | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_9 | 1.33 | 16180 | 905 | 905 | 905 | 1.33 | Class 1 | 1225 | 578 | 27 | 68 | 0.947409 | 0.955372 | 0.752801 | 0.124339 | 0.505602 | 0.412430 | 0.181875 | 0.105469 | 0.681243 | 0.697606 | 0.183536 | 0.771010 | 0.105263 | 0.715789 |
| 789 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | None | None | HistGradientBoostingClassifier_None_None_MinMaxScaler [1, 2]_fold_9 | 5.60 | 16180 | 905 | 16180 | 905 | 5.60 | Class 1 | 1803 | 0 | 95 | 0 | 1.000000 | 0.000000 | 0.752763 | 0.132218 | 0.505526 | 0.404595 | 0.000000 | 0.000000 | 0.949947 | 0.500000 | 0.000000 | 0.925563 | 0.000000 | 0.000000 |
| 319 | XGBoost | {'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsam... | Zero Imputation | None | XGBoost_None_Zero Imputation_MinMaxScaler [1, 2]_fold_9 | 2.20 | 16180 | 905 | 16180 | 905 | 2.20 | Class 1 | 1803 | 0 | 93 | 2 | 0.998892 | 0.000000 | 0.750398 | 0.167632 | 0.500797 | 0.417410 | 0.141492 | 0.039254 | 0.951001 | 0.510526 | 0.041237 | 0.928128 | 1.000000 | 0.021053 |
| Algorithm | Hyperparameters | Imputation Technique | Imbalance Class Technique | Model Unique Code | Execution Time | Init_Num_Class_0 | Init_Num_Class_1 | Res_Num_Class_0 | Res_Num_Class_1 | execution_time_seg | Class | TP | TN | FP | FN | Sensitivity_TPR | Specificity | AUC_ROC | AUC_PRC | Gini | KS | MCC | Kappa | Accuracy | Balanced_Accuracy | F1 | F1_Weighted | Precision | Recall | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 359 | XGBoost | {'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsam... | Zero Imputation | Undersampling | XGBoost_Undersampling_Zero Imputation_MinMaxScaler [1, 2]_fold_9 | 0.87 | 16180 | 905 | 905 | 905 | 0.87 | Class 1 | 1161 | 642 | 24 | 71 | 0.942370 | 0.963964 | 0.740401 | 0.102955 | 0.480801 | 0.441212 | 0.176181 | 0.095876 | 0.649104 | 0.695648 | 0.175743 | 0.747008 | 0.099579 | 0.747368 |
| 549 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | None | None | LightGBM_None_None_MinMaxScaler [1, 2]_fold_9 | 2.03 | 16180 | 905 | 16180 | 905 | 2.03 | Class 1 | 1802 | 1 | 95 | 0 | 1.000000 | 0.010417 | 0.770786 | 0.154870 | 0.541571 | 0.437411 | -0.005270 | -0.001044 | 0.949420 | 0.499723 | 0.000000 | 0.925300 | 0.000000 | 0.000000 |
| 762 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | None | Undersampling | HistGradientBoostingClassifier_Undersampling_None_MinMaxScaler [0, 1]_fold_2 | 6.89 | 16165 | 919 | 919 | 919 | 6.89 | Class 1 | 1196 | 622 | 21 | 60 | 0.952229 | 0.967341 | 0.731033 | 0.125523 | 0.462067 | 0.436010 | 0.167898 | 0.087710 | 0.661401 | 0.699303 | 0.157274 | 0.761229 | 0.087977 | 0.740741 |
| 918 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | Zero Imputation | None | HistGradientBoostingClassifier_None_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_fold_8 | 2.18 | 16206 | 879 | 16206 | 879 | 2.18 | Class 1 | 1777 | 0 | 121 | 0 | 1.000000 | 0.000000 | 0.743065 | 0.163065 | 0.486129 | 0.430771 | 0.000000 | 0.000000 | 0.936249 | 0.500000 | 0.000000 | 0.905423 | 0.000000 | 0.000000 |
| 559 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | Zero Imputation | None | LightGBM_None_Zero Imputation_MinMaxScaler [1, 2]_fold_9 | 1.57 | 16180 | 905 | 16180 | 905 | 1.57 | Class 1 | 1803 | 0 | 95 | 0 | 1.000000 | 0.000000 | 0.753884 | 0.144285 | 0.507768 | 0.430131 | 0.000000 | 0.000000 | 0.949947 | 0.500000 | 0.000000 | 0.925563 | 0.000000 | 0.000000 |
| 902 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | None | None | HistGradientBoostingClassifier_None_None_StandardScaler MinMaxScaler [0, 1]_fold_2 | 2.72 | 16165 | 919 | 16165 | 919 | 2.72 | Class 1 | 1816 | 2 | 81 | 0 | 1.000000 | 0.024096 | 0.755134 | 0.117710 | 0.510268 | 0.427271 | -0.006854 | -0.002060 | 0.956293 | 0.499450 | 0.000000 | 0.935957 | 0.000000 | 0.000000 |
| 462 | XGBoost | {'objective': 'binary:logistic', 'base_score': None, 'booster': None, 'callbacks': None, 'colsam... | None | Undersampling | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_2 | 1.39 | 16165 | 919 | 919 | 919 | 1.39 | Class 1 | 1167 | 651 | 20 | 61 | 0.950326 | 0.970194 | 0.746740 | 0.110602 | 0.493481 | 0.425804 | 0.164881 | 0.083660 | 0.646656 | 0.697500 | 0.153846 | 0.750138 | 0.085674 | 0.753086 |
| 81 | Random Forest | {'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': No... | Zero Imputation | Undersampling | Random Forest_Undersampling_Zero Imputation_StandardScaler_fold_1 | 1.12 | 16197 | 887 | 887 | 887 | 1.12 | Class 1 | 1173 | 613 | 28 | 85 | 0.932432 | 0.956318 | 0.747470 | 0.150728 | 0.494941 | 0.421523 | 0.200674 | 0.119422 | 0.662454 | 0.704494 | 0.209618 | 0.751141 | 0.121777 | 0.752212 |
| 859 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | Zero Imputation | None | HistGradientBoostingClassifier_None_Zero Imputation_StandardScaler_fold_9 | 2.70 | 16180 | 905 | 16180 | 905 | 2.70 | Class 1 | 1803 | 0 | 95 | 0 | 1.000000 | 0.000000 | 0.767262 | 0.144842 | 0.534524 | 0.421269 | 0.000000 | 0.000000 | 0.949947 | 0.500000 | 0.000000 | 0.925563 | 0.000000 | 0.000000 |
| 949 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | None | Undersampling | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_9 | 7.00 | 16180 | 905 | 905 | 905 | 7.00 | Class 1 | 1177 | 626 | 25 | 70 | 0.943865 | 0.961598 | 0.756073 | 0.149272 | 0.512146 | 0.419003 | 0.176307 | 0.097496 | 0.657007 | 0.694821 | 0.176991 | 0.753011 | 0.100575 | 0.736842 |
# Concat df_results_final_2 and df_results_final_3
df_results_final_concat = pd.concat([df_results_final_2, df_results_final_3])
df_results_final_concat
| Algorithm | Hyperparameters | Imputation Technique | Imbalance Class Technique | Model Unique Code | Execution Time | Init_Num_Class_0 | Init_Num_Class_1 | Res_Num_Class_0 | Res_Num_Class_1 | execution_time_seg | Class | TP | TN | FP | FN | Sensitivity_TPR | Specificity | AUC_ROC | AUC_PRC | Gini | KS | MCC | Kappa | Accuracy | Balanced_Accuracy | F1 | F1_Weighted | Precision | Recall | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 559 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | Median Imputation | None | LightGBM_None_Median Imputation_None_fold_4 | 0.97 | 14403 | 784 | 14403 | 784 | 0.97 | Class 1 | 3579 | 1 | 215 | 1 | 0.999721 | 0.004630 | 0.723510 | 0.136000 | 0.447020 | 0.353011 | 0.043916 | 0.008139 | 0.943098 | 0.502175 | 0.009174 | 0.915995 | 0.500000 | 0.004630 |
| 579 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | Median Imputation | Oversampling | LightGBM_Oversampling_Median Imputation_None_fold_4 | 0.97 | 14403 | 784 | 14403 | 14403 | 0.97 | Class 1 | 3075 | 505 | 133 | 83 | 0.973718 | 0.791536 | 0.707663 | 0.137440 | 0.415326 | 0.323081 | 0.155712 | 0.134426 | 0.831928 | 0.621599 | 0.206468 | 0.866205 | 0.141156 | 0.384259 |
| 578 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | Median Imputation | Oversampling | LightGBM_Oversampling_Median Imputation_None_fold_3 | 0.94 | 14383 | 804 | 14383 | 14383 | 0.94 | Class 1 | 3015 | 585 | 122 | 74 | 0.976044 | 0.827440 | 0.690359 | 0.103601 | 0.380717 | 0.298929 | 0.125638 | 0.101592 | 0.813751 | 0.607526 | 0.173099 | 0.857780 | 0.112291 | 0.377551 |
| 577 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | Median Imputation | Oversampling | LightGBM_Oversampling_Median Imputation_None_fold_2 | 0.94 | 14373 | 813 | 14373 | 14373 | 0.94 | Class 1 | 2990 | 620 | 107 | 80 | 0.973941 | 0.852820 | 0.694404 | 0.102240 | 0.388807 | 0.292502 | 0.142890 | 0.111302 | 0.808533 | 0.628031 | 0.180383 | 0.856578 | 0.114286 | 0.427807 |
| 576 | LightGBM | {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'spl... | Median Imputation | Oversampling | LightGBM_Oversampling_Median Imputation_None_fold_1 | 0.94 | 14350 | 836 | 14350 | 14350 | 0.94 | Class 1 | 3073 | 560 | 105 | 59 | 0.981162 | 0.842105 | 0.688934 | 0.090925 | 0.377868 | 0.319675 | 0.113158 | 0.088452 | 0.824862 | 0.602807 | 0.150702 | 0.869898 | 0.095315 | 0.359756 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 955 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | Zero Imputation | Undersampling | HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_... | 6.67 | 16176 | 909 | 909 | 909 | 6.67 | Class 1 | 1137 | 670 | 33 | 58 | 0.951464 | 0.953058 | 0.688300 | 0.096823 | 0.376600 | 0.316151 | 0.117131 | 0.061655 | 0.629610 | 0.633291 | 0.141636 | 0.734024 | 0.079670 | 0.637363 |
| 956 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | Zero Imputation | Undersampling | HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_... | 6.63 | 16181 | 904 | 904 | 904 | 6.63 | Class 1 | 1137 | 665 | 38 | 58 | 0.951464 | 0.945946 | 0.676846 | 0.105696 | 0.353693 | 0.311217 | 0.106105 | 0.057466 | 0.629610 | 0.617566 | 0.141636 | 0.732385 | 0.080221 | 0.604167 |
| 957 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | Zero Imputation | Undersampling | HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_... | 6.63 | 16185 | 900 | 900 | 900 | 6.63 | Class 1 | 1096 | 702 | 39 | 61 | 0.947277 | 0.947368 | 0.663968 | 0.086106 | 0.327937 | 0.258943 | 0.100046 | 0.053156 | 0.609589 | 0.609783 | 0.141367 | 0.715430 | 0.079948 | 0.610000 |
| 958 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | Zero Imputation | Undersampling | HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_... | 6.73 | 16206 | 879 | 879 | 879 | 6.73 | Class 1 | 1106 | 671 | 35 | 86 | 0.927852 | 0.950425 | 0.702368 | 0.127047 | 0.404735 | 0.363981 | 0.166216 | 0.096587 | 0.628030 | 0.666571 | 0.195900 | 0.722215 | 0.113606 | 0.710744 |
| 959 | HistGradientBoostingClassifier | {'categorical_features': None, 'class_weight': None, 'early_stopping': 'auto', 'interaction_cst'... | Zero Imputation | Undersampling | HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1]_... | 8.73 | 16180 | 905 | 905 | 905 | 8.73 | Class 1 | 1158 | 645 | 28 | 67 | 0.945306 | 0.958395 | 0.737484 | 0.122450 | 0.474969 | 0.396771 | 0.156518 | 0.085256 | 0.645416 | 0.673763 | 0.166047 | 0.744369 | 0.094101 | 0.705263 |
1680 rows × 30 columns
import seaborn as sns
import matplotlib.pyplot as plt
# Extract relevant data for plotting
data_to_plot_concat = df_results_final_concat[['Algorithm', 'Imbalance Class Technique', 'Imputation Technique', 'AUC_ROC', 'AUC_PRC', 'Gini', 'KS', 'Balanced_Accuracy', 'F1_Weighted', 'Model Unique Code', 'Execution Time']]
# Extract fold number from 'Model Unique Code'
data_to_plot_concat['Fold'] = data_to_plot_concat['Model Unique Code'].str.extract(r'fold_(\d+)').astype(int)
# Exclude the text _fold_ and the fold number from 'Model Unique Code'
data_to_plot_concat['Model Code'] = data_to_plot_concat['Model Unique Code'].str.extract(r'^(.*?)_fold_\d+').astype(str)
# Melt the dataframe to long format for easier plotting
melted_data_concat = pd.melt(data_to_plot_concat, id_vars=['Model Code', 'Imbalance Class Technique', 'Imputation Technique', 'Model Unique Code', 'Fold'],
value_vars=['AUC_ROC', 'AUC_PRC', 'KS', 'Balanced_Accuracy', 'F1_Weighted', 'Execution Time'],
var_name='Metrics', value_name='Metric Value')
# Create separate boxplots for each metric
metrics = melted_data_concat['Metrics'].unique()
for metric in metrics:
plt.figure(figsize=(21, 7))
sns.set(style='whitegrid')
sns.boxplot(x='Model Code', y='Metric Value', hue='Imbalance Class Technique',
data=melted_data_concat[melted_data_concat['Metrics'] == metric], palette='Set3', showfliers=False, dodge=True)
plt.title(f'{metric} Comparison by Algorithm, Imbalance Technique, Imputation Technique, and Class Weight')
plt.xlabel('Model Code')
plt.ylabel('Metric Value')
plt.xticks(rotation=90, fontsize=8)
plt.legend(title='Imbalance Class Technique')
plt.show()
import plotly.express as px
# Create a function to generate boxplots for each metric
def plot_metric_boxplots(data, metric):
fig = px.box(data, x='Model Code', y='Metric Value', color='Imbalance Class Technique',
category_orders={'Imbalance Class Technique': ['None', 'Over-sampling', 'Under-sampling']},
points=False,
title=f'{metric} Comparison by Algorithm, Imbalance Technique, Imputation Technique, Class Weight and Normalization',
labels={'Metric Value': metric, 'Model Code': 'Model Code'},
width=1200, height=600)
# Customize the layout
fig.update_layout(xaxis=dict(tickangle=90, showticklabels=False), legend_title_text='Imbalance Class Technique')
#fig.update_layout(xaxis=dict(tickangle=90), legend_title_text='Imbalance Class Technique')
# Save the HTML file
fig.write_html(f'MoDelEvaluation_{metric}_boxplot.html')
# Show the plot
fig.show()
# Melt the dataframe to long format for easier plotting
melted_data_concat = pd.melt(data_to_plot_concat, id_vars=['Model Code', 'Imbalance Class Technique', 'Imputation Technique', 'Model Unique Code', 'Fold'],
value_vars=['AUC_ROC', 'AUC_PRC', 'KS', 'Balanced_Accuracy', 'F1_Weighted', 'Execution Time'],
var_name='Metrics', value_name='Metric Value')
# Create boxplots for each metric
for metric in melted_data_concat['Metrics'].unique():
# Filter relevant columns for hover_data
relevant_columns = ['Model Code', 'Imbalance Class Technique', 'Imputation Technique', 'Model Unique Code', 'Fold', 'Metrics', 'Metric Value']
plot_metric_boxplots(melted_data_concat[melted_data_concat['Metrics'] == metric][relevant_columns], metric)
data_to_plot_concat.columns
Index(['Algorithm', 'Imbalance Class Technique', 'Imputation Technique',
'AUC_ROC', 'AUC_PRC', 'Gini', 'KS', 'Balanced_Accuracy', 'F1_Weighted',
'Model Unique Code', 'Execution Time', 'Fold', 'Model Code'],
dtype='object')
# Sort the dataframe based on AUC_ROC, KS, and Balanced Accuracy
sorted_data_concat_AUC = data_to_plot_concat.sort_values(by='AUC_ROC', ascending=False)
sorted_data_concat_KS = data_to_plot_concat.sort_values(by='KS', ascending=False)
sorted_data_concat_BA = data_to_plot_concat.sort_values(by='Balanced_Accuracy', ascending=False)
# Select the top 30 models
top_30_models_AUC = sorted_data_concat_AUC.head(30)
top_30_models_KS = sorted_data_concat_KS.head(30)
top_30_models_BA = sorted_data_concat_BA.head(30)
# Print the list of each metric for the top 30 models
print("Top 30 Models based on AUC_ROC:")
display(top_30_models_AUC[['Model Code', 'AUC_ROC']])
print("\nTop 30 Models based on KS:")
display(top_30_models_KS[['Model Code', 'KS']])
print("\nTop 30 Models based on Balanced Accuracy:")
display(top_30_models_BA[['Model Code', 'Balanced_Accuracy']])
# Find the intersection of Model Codes from the three lists
intersection_model_codes = set(top_30_models_AUC['Model Code']).intersection(top_30_models_KS['Model Code'], top_30_models_BA['Model Code'])
# Print the intersection
print("\nIntersection of Model Codes:")
print(intersection_model_codes)
# Filter the DataFrame based on Model Codes in the intersection
best_10_models = data_to_plot_concat[data_to_plot_concat['Model Code'].isin(intersection_model_codes)]
# Display the filtered DataFrame soted by KS
best_10_models = best_10_models.sort_values(by='KS', ascending=False)
display(best_10_models)
Top 30 Models based on AUC_ROC:
| Model Code | AUC_ROC | |
|---|---|---|
| 549 | LightGBM_None_None_MinMaxScaler [1, 2] | 0.770786 |
| 859 | HistGradientBoostingClassifier_None_Zero Imputation_StandardScaler | 0.767262 |
| 739 | HistGradientBoostingClassifier_None_Zero Imputation_MinMaxScaler [0, 1] | 0.759383 |
| 949 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1] | 0.756073 |
| 902 | HistGradientBoostingClassifier_None_None_StandardScaler MinMaxScaler [0, 1] | 0.755134 |
| 909 | HistGradientBoostingClassifier_None_None_StandardScaler MinMaxScaler [0, 1] | 0.754663 |
| 559 | LightGBM_None_Zero Imputation_MinMaxScaler [1, 2] | 0.753884 |
| 469 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] | 0.752801 |
| 789 | HistGradientBoostingClassifier_None_None_MinMaxScaler [1, 2] | 0.752763 |
| 319 | XGBoost_None_Zero Imputation_MinMaxScaler [1, 2] | 0.750398 |
| 799 | HistGradientBoostingClassifier_None_Zero Imputation_MinMaxScaler [1, 2] | 0.749062 |
| 849 | HistGradientBoostingClassifier_None_None_StandardScaler | 0.747999 |
| 81 | Random Forest_Undersampling_Zero Imputation_StandardScaler | 0.747470 |
| 579 | LightGBM_Oversampling_Zero Imputation_MinMaxScaler [1, 2] | 0.747290 |
| 462 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] | 0.746740 |
| 671 | LightGBM_None_Zero Imputation_StandardScaler MinMaxScaler [0, 1] | 0.746294 |
| 491 | LightGBM_None_Zero Imputation_MinMaxScaler [0, 1] | 0.746294 |
| 819 | HistGradientBoostingClassifier_Oversampling_Zero Imputation_MinMaxScaler [1, 2] | 0.745956 |
| 129 | Gradient Boosting Machines (GBM)_None_Zero Imputation_MinMaxScaler [0, 1] | 0.745632 |
| 139 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1] | 0.745480 |
| 371 | XGBoost_None_Zero Imputation_StandardScaler | 0.745429 |
| 181 | Gradient Boosting Machines (GBM)_None_Zero Imputation_StandardScaler | 0.745380 |
| 199 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_StandardScaler | 0.745159 |
| 912 | HistGradientBoostingClassifier_None_Zero Imputation_StandardScaler MinMaxScaler [0, 1] | 0.744482 |
| 239 | Gradient Boosting Machines (GBM)_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1] | 0.744216 |
| 259 | XGBoost_None_Zero Imputation_MinMaxScaler [0, 1] | 0.744178 |
| 439 | XGBoost_None_Zero Imputation_StandardScaler MinMaxScaler [0, 1] | 0.744178 |
| 189 | Gradient Boosting Machines (GBM)_None_Zero Imputation_StandardScaler | 0.743752 |
| 918 | HistGradientBoostingClassifier_None_Zero Imputation_StandardScaler MinMaxScaler [0, 1] | 0.743065 |
| 161 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2] | 0.742927 |
Top 30 Models based on KS:
| Model Code | KS | |
|---|---|---|
| 359 | XGBoost_Undersampling_Zero Imputation_MinMaxScaler [1, 2] | 0.441212 |
| 549 | LightGBM_None_None_MinMaxScaler [1, 2] | 0.437411 |
| 762 | HistGradientBoostingClassifier_Undersampling_None_MinMaxScaler [0, 1] | 0.436010 |
| 918 | HistGradientBoostingClassifier_None_Zero Imputation_StandardScaler MinMaxScaler [0, 1] | 0.430771 |
| 559 | LightGBM_None_Zero Imputation_MinMaxScaler [1, 2] | 0.430131 |
| 902 | HistGradientBoostingClassifier_None_None_StandardScaler MinMaxScaler [0, 1] | 0.427271 |
| 462 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] | 0.425804 |
| 81 | Random Forest_Undersampling_Zero Imputation_StandardScaler | 0.421523 |
| 859 | HistGradientBoostingClassifier_None_Zero Imputation_StandardScaler | 0.421269 |
| 949 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1] | 0.419003 |
| 319 | XGBoost_None_Zero Imputation_MinMaxScaler [1, 2] | 0.417410 |
| 82 | Random Forest_Undersampling_Zero Imputation_StandardScaler | 0.417003 |
| 891 | HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler | 0.416127 |
| 469 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] | 0.412430 |
| 799 | HistGradientBoostingClassifier_None_Zero Imputation_MinMaxScaler [1, 2] | 0.411262 |
| 12 | Random Forest_Oversampling_Zero Imputation_MinMaxScaler [0, 1] | 0.410158 |
| 111 | Random Forest_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1] | 0.409314 |
| 912 | HistGradientBoostingClassifier_None_Zero Imputation_StandardScaler MinMaxScaler [0, 1] | 0.408996 |
| 201 | Gradient Boosting Machines (GBM)_Undersampling_Zero Imputation_StandardScaler | 0.408784 |
| 191 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_StandardScaler | 0.408660 |
| 161 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2] | 0.408457 |
| 941 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1] | 0.407853 |
| 92 | Random Forest_None_Zero Imputation_StandardScaler MinMaxScaler [0, 1] | 0.406980 |
| 371 | XGBoost_None_Zero Imputation_StandardScaler | 0.406559 |
| 698 | LightGBM_Oversampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1] | 0.406377 |
| 538 | LightGBM_Undersampling_Zero Imputation_MinMaxScaler [0, 1] | 0.406363 |
| 131 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1] | 0.405752 |
| 598 | LightGBM_Undersampling_Zero Imputation_MinMaxScaler [1, 2] | 0.405559 |
| 102 | Random Forest_Oversampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1] | 0.405146 |
| 739 | HistGradientBoostingClassifier_None_Zero Imputation_MinMaxScaler [0, 1] | 0.404711 |
Top 30 Models based on Balanced Accuracy:
| Model Code | Balanced_Accuracy | |
|---|---|---|
| 81 | Random Forest_Undersampling_Zero Imputation_StandardScaler | 0.704494 |
| 111 | Random Forest_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1] | 0.703203 |
| 762 | HistGradientBoostingClassifier_Undersampling_None_MinMaxScaler [0, 1] | 0.699303 |
| 469 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] | 0.697606 |
| 462 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] | 0.697500 |
| 598 | LightGBM_Undersampling_Zero Imputation_MinMaxScaler [1, 2] | 0.696131 |
| 359 | XGBoost_Undersampling_Zero Imputation_MinMaxScaler [1, 2] | 0.695648 |
| 949 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1] | 0.694821 |
| 761 | HistGradientBoostingClassifier_Undersampling_None_MinMaxScaler [0, 1] | 0.691281 |
| 419 | XGBoost_Undersampling_Zero Imputation_StandardScaler | 0.690951 |
| 538 | LightGBM_Undersampling_Zero Imputation_MinMaxScaler [0, 1] | 0.688148 |
| 711 | LightGBM_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1] | 0.687471 |
| 208 | Gradient Boosting Machines (GBM)_Undersampling_Zero Imputation_StandardScaler | 0.686862 |
| 142 | Gradient Boosting Machines (GBM)_Undersampling_Zero Imputation_MinMaxScaler [0, 1] | 0.686071 |
| 941 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1] | 0.686071 |
| 82 | Random Forest_Undersampling_Zero Imputation_StandardScaler | 0.686010 |
| 864 | HistGradientBoostingClassifier_Oversampling_None_StandardScaler | 0.685650 |
| 201 | Gradient Boosting Machines (GBM)_Undersampling_Zero Imputation_StandardScaler | 0.685457 |
| 891 | HistGradientBoostingClassifier_Undersampling_Zero Imputation_StandardScaler | 0.683722 |
| 461 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] | 0.683381 |
| 169 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2] | 0.683227 |
| 585 | LightGBM_Undersampling_None_MinMaxScaler [1, 2] | 0.683058 |
| 139 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1] | 0.682395 |
| 479 | XGBoost_Undersampling_Zero Imputation_StandardScaler MinMaxScaler [0, 1] | 0.682071 |
| 405 | Random Forest_Undersampling_Zero Imputation_None | 0.681244 |
| 889 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler | 0.680702 |
| 138 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1] | 0.680132 |
| 202 | Gradient Boosting Machines (GBM)_Undersampling_Zero Imputation_StandardScaler | 0.679501 |
| 772 | HistGradientBoostingClassifier_Undersampling_Zero Imputation_MinMaxScaler [0, 1] | 0.679410 |
| 641 | LightGBM_Undersampling_None_StandardScaler | 0.678956 |
Intersection of Model Codes:
{'XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]', 'Random Forest_Undersampling_Zero Imputation_StandardScaler', 'Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]', 'Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2]', 'HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]'}
| Algorithm | Imbalance Class Technique | Imputation Technique | AUC_ROC | AUC_PRC | Gini | KS | Balanced_Accuracy | F1_Weighted | Model Unique Code | Execution Time | Fold | Model Code | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 462 | XGBoost | Undersampling | None | 0.746740 | 0.110602 | 0.493481 | 0.425804 | 0.697500 | 0.750138 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_2 | 1.39 | 2 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 81 | Random Forest | Undersampling | Zero Imputation | 0.747470 | 0.150728 | 0.494941 | 0.421523 | 0.704494 | 0.751141 | Random Forest_Undersampling_Zero Imputation_StandardScaler_fold_1 | 1.12 | 1 | Random Forest_Undersampling_Zero Imputation_StandardScaler |
| 949 | HistGradientBoostingClassifier | Undersampling | None | 0.756073 | 0.149272 | 0.512146 | 0.419003 | 0.694821 | 0.753011 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_9 | 7.00 | 9 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 82 | Random Forest | Undersampling | Zero Imputation | 0.725961 | 0.101703 | 0.451921 | 0.417003 | 0.686010 | 0.767554 | Random Forest_Undersampling_Zero Imputation_StandardScaler_fold_2 | 1.16 | 2 | Random Forest_Undersampling_Zero Imputation_StandardScaler |
| 469 | XGBoost | Undersampling | None | 0.752801 | 0.124339 | 0.505602 | 0.412430 | 0.697606 | 0.771010 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_9 | 1.33 | 9 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 161 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.742927 | 0.151731 | 0.485854 | 0.408457 | 0.665503 | 0.777514 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2]_fold_1 | 107.21 | 1 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2] |
| 941 | HistGradientBoostingClassifier | Undersampling | None | 0.732549 | 0.131021 | 0.465097 | 0.407853 | 0.686071 | 0.730600 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_1 | 7.00 | 1 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 131 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.739327 | 0.147206 | 0.478654 | 0.405752 | 0.671328 | 0.779803 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]_fold_1 | 108.74 | 1 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1] |
| 461 | XGBoost | Undersampling | None | 0.721705 | 0.126672 | 0.443409 | 0.401381 | 0.683381 | 0.738795 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_1 | 1.32 | 1 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 139 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.745480 | 0.131182 | 0.490960 | 0.389690 | 0.682395 | 0.797704 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]_fold_9 | 116.13 | 9 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1] |
| 169 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.739452 | 0.144183 | 0.478904 | 0.389123 | 0.683227 | 0.798808 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2]_fold_9 | 106.48 | 9 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2] |
| 132 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.718195 | 0.120410 | 0.436391 | 0.374771 | 0.676170 | 0.786634 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]_fold_2 | 109.04 | 2 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1] |
| 162 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.729149 | 0.127230 | 0.458298 | 0.372815 | 0.653679 | 0.788149 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2]_fold_2 | 107.97 | 2 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2] |
| 138 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.728684 | 0.144654 | 0.457368 | 0.369896 | 0.680132 | 0.779181 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]_fold_8 | 113.13 | 8 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1] |
| 942 | HistGradientBoostingClassifier | Undersampling | None | 0.708926 | 0.104777 | 0.417852 | 0.369026 | 0.661411 | 0.740813 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_2 | 6.92 | 2 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 88 | Random Forest | Undersampling | Zero Imputation | 0.707414 | 0.137465 | 0.414828 | 0.358739 | 0.673484 | 0.759598 | Random Forest_Undersampling_Zero Imputation_StandardScaler_fold_8 | 1.12 | 8 | Random Forest_Undersampling_Zero Imputation_StandardScaler |
| 84 | Random Forest | Undersampling | Zero Imputation | 0.698359 | 0.112171 | 0.396718 | 0.357230 | 0.673534 | 0.741606 | Random Forest_Undersampling_Zero Imputation_StandardScaler_fold_4 | 1.16 | 4 | Random Forest_Undersampling_Zero Imputation_StandardScaler |
| 948 | HistGradientBoostingClassifier | Undersampling | None | 0.715934 | 0.136291 | 0.431868 | 0.356809 | 0.672937 | 0.742591 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_8 | 6.96 | 8 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 168 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.717973 | 0.152118 | 0.435947 | 0.350665 | 0.668210 | 0.773426 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2]_fold_8 | 106.74 | 8 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2] |
| 464 | XGBoost | Undersampling | None | 0.678731 | 0.091227 | 0.357462 | 0.345230 | 0.667557 | 0.747272 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_4 | 1.35 | 4 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 166 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.717282 | 0.114918 | 0.434563 | 0.344004 | 0.647255 | 0.774180 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2]_fold_6 | 105.56 | 6 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2] |
| 85 | Random Forest | Undersampling | Zero Imputation | 0.704744 | 0.084935 | 0.409488 | 0.343664 | 0.646296 | 0.752729 | Random Forest_Undersampling_Zero Imputation_StandardScaler_fold_5 | 1.14 | 5 | Random Forest_Undersampling_Zero Imputation_StandardScaler |
| 89 | Random Forest | Undersampling | Zero Imputation | 0.713889 | 0.116671 | 0.427778 | 0.340304 | 0.640815 | 0.761512 | Random Forest_Undersampling_Zero Imputation_StandardScaler_fold_9 | 1.17 | 9 | Random Forest_Undersampling_Zero Imputation_StandardScaler |
| 86 | Random Forest | Undersampling | Zero Imputation | 0.688064 | 0.092786 | 0.376127 | 0.338825 | 0.669412 | 0.770733 | Random Forest_Undersampling_Zero Imputation_StandardScaler_fold_6 | 1.13 | 6 | Random Forest_Undersampling_Zero Imputation_StandardScaler |
| 136 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.709605 | 0.106213 | 0.419210 | 0.334732 | 0.644544 | 0.777201 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]_fold_6 | 111.27 | 6 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1] |
| 466 | XGBoost | Undersampling | None | 0.703819 | 0.120919 | 0.407637 | 0.334640 | 0.652637 | 0.732866 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_6 | 1.34 | 6 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 945 | HistGradientBoostingClassifier | Undersampling | None | 0.680881 | 0.088468 | 0.361762 | 0.334177 | 0.645664 | 0.736746 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_5 | 7.50 | 5 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 947 | HistGradientBoostingClassifier | Undersampling | None | 0.677564 | 0.101311 | 0.355128 | 0.331935 | 0.625895 | 0.718147 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_7 | 7.06 | 7 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 164 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.694431 | 0.146453 | 0.388862 | 0.328628 | 0.655666 | 0.765378 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2]_fold_4 | 107.52 | 4 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2] |
| 943 | HistGradientBoostingClassifier | Undersampling | None | 0.695546 | 0.083292 | 0.391092 | 0.327850 | 0.646789 | 0.747276 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_3 | 6.92 | 3 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 167 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.679224 | 0.104085 | 0.358448 | 0.327364 | 0.644822 | 0.765581 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2]_fold_7 | 105.75 | 7 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2] |
| 944 | HistGradientBoostingClassifier | Undersampling | None | 0.701319 | 0.104452 | 0.402637 | 0.325506 | 0.649926 | 0.736147 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_4 | 7.09 | 4 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 134 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.701304 | 0.135172 | 0.402608 | 0.318708 | 0.646914 | 0.766935 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]_fold_4 | 108.13 | 4 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1] |
| 468 | XGBoost | Undersampling | None | 0.682248 | 0.107131 | 0.364497 | 0.315454 | 0.650025 | 0.737465 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_8 | 1.35 | 8 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 163 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.690322 | 0.093228 | 0.380643 | 0.304192 | 0.650443 | 0.768788 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2]_fold_3 | 106.11 | 3 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2] |
| 463 | XGBoost | Undersampling | None | 0.667244 | 0.067863 | 0.334488 | 0.303794 | 0.613363 | 0.740758 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_3 | 1.34 | 3 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 83 | Random Forest | Undersampling | Zero Imputation | 0.671811 | 0.074971 | 0.343622 | 0.301437 | 0.639424 | 0.753279 | Random Forest_Undersampling_Zero Imputation_StandardScaler_fold_3 | 1.17 | 3 | Random Forest_Undersampling_Zero Imputation_StandardScaler |
| 946 | HistGradientBoostingClassifier | Undersampling | None | 0.675745 | 0.097489 | 0.351490 | 0.300881 | 0.627428 | 0.732294 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_6 | 7.23 | 6 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 165 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.693047 | 0.101393 | 0.386093 | 0.300261 | 0.633489 | 0.771357 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2]_fold_5 | 105.79 | 5 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2] |
| 137 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.681941 | 0.099972 | 0.363882 | 0.292392 | 0.633988 | 0.763672 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]_fold_7 | 110.13 | 7 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1] |
| 133 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.688161 | 0.093008 | 0.376322 | 0.287185 | 0.638707 | 0.776451 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]_fold_3 | 110.49 | 3 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1] |
| 135 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.685159 | 0.094191 | 0.370318 | 0.285161 | 0.626650 | 0.776279 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]_fold_5 | 109.89 | 5 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1] |
| 465 | XGBoost | Undersampling | None | 0.674061 | 0.091905 | 0.348121 | 0.283263 | 0.633568 | 0.734427 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_5 | 1.39 | 5 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 87 | Random Forest | Undersampling | Zero Imputation | 0.651257 | 0.107437 | 0.302514 | 0.282937 | 0.635923 | 0.753190 | Random Forest_Undersampling_Zero Imputation_StandardScaler_fold_7 | 1.17 | 7 | Random Forest_Undersampling_Zero Imputation_StandardScaler |
| 467 | XGBoost | Undersampling | None | 0.627027 | 0.072777 | 0.254055 | 0.246830 | 0.609499 | 0.707884 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_7 | 1.34 | 7 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 160 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.644400 | 0.135859 | 0.288801 | 0.241086 | 0.615248 | 0.775934 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2]_fold_0 | 107.01 | 0 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [1, 2] |
| 460 | XGBoost | Undersampling | None | 0.645586 | 0.100155 | 0.291172 | 0.240800 | 0.603103 | 0.728417 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_0 | 1.29 | 0 | XGBoost_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 130 | Gradient Boosting Machines (GBM) | Oversampling | Zero Imputation | 0.640100 | 0.123523 | 0.280200 | 0.229514 | 0.606427 | 0.769144 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1]_fold_0 | 110.23 | 0 | Gradient Boosting Machines (GBM)_Oversampling_Zero Imputation_MinMaxScaler [0, 1] |
| 940 | HistGradientBoostingClassifier | Undersampling | None | 0.639877 | 0.104303 | 0.279755 | 0.221563 | 0.599634 | 0.728833 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1]_fold_0 | 6.94 | 0 | HistGradientBoostingClassifier_Undersampling_None_StandardScaler MinMaxScaler [0, 1] |
| 80 | Random Forest | Undersampling | Zero Imputation | 0.621186 | 0.112063 | 0.242372 | 0.206497 | 0.589911 | 0.756953 | Random Forest_Undersampling_Zero Imputation_StandardScaler_fold_0 | 1.08 | 0 | Random Forest_Undersampling_Zero Imputation_StandardScaler |
data_to_plot_concat['Model Code'].nunique()
240
# Read df_results_final_3 and df_results_final_2
# df_results_final_3 = pd.read_csv('df_results_final_3.csv')
# df_results_final_2 = pd.read_csv('df_results_final_2.csv')
df = df_droped_001
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from scipy.stats import loguniform, randint
from skopt import BayesSearchCV
from sklearn.pipeline import Pipeline
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, auc
from xgboost import XGBClassifier
np.int = int
# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.iloc[:, 1:-1]
target = df['label']
# Sample features and target to .01% of the original size
features = features.sample(frac=0.01, random_state=42)
target = target.sample(frac=0.01, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
# Create a pipeline with a classifier and two scalers (StandardScaler and MinMaxScaler [0, 1])
pipe = Pipeline([
('scaler', StandardScaler()),
('minmax_scaler', MinMaxScaler((0, 1))),
('classifier', XGBClassifier())
])
# Grid Search
param_grid = {
"classifier__learning_rate": [0.0001, 0.0005, 0.001, 0.01, 0.1],
"classifier__n_estimators": [100, 300, 600, 800, 1000],
"classifier__max_depth": [4, 20, 100, 250, 400]
}
reg_grid = GridSearchCV(pipe,
param_grid=param_grid,
cv=5,
n_jobs=8,
scoring='roc_auc'
)
model_grid = reg_grid.fit(X_train, y_train)
# Random Search
n_iter = 70
param_rand = {
"classifier__learning_rate": loguniform(1e-4, 0.1),
"classifier__n_estimators": randint(100, 1000),
"classifier__max_depth": randint(4, 400)
}
reg_rand = RandomizedSearchCV(pipe,
param_distributions=param_rand,
n_iter=n_iter,
cv=5,
n_jobs=8,
scoring='roc_auc',
random_state=123)
model_rand = reg_rand.fit(X_train, y_train)
# Bayesian Search
n_iter = 70
param_bay = {
"classifier__learning_rate": (0.0001, 0.1, "log-uniform"),
"classifier__n_estimators": (100, 1000),
"classifier__max_depth": (4, 400)
}
reg_bay = BayesSearchCV(estimator=pipe,
search_spaces=param_bay,
n_iter=n_iter,
cv=5,
n_jobs=8,
scoring='roc_auc',
random_state=123)
model_bay = reg_bay.fit(X_train, y_train)
# Evaluation Metrics
def evaluate_model(model, X_test, y_test):
y_pred_prob = model.predict_proba(X_test)[:, 1]
metrics_df = calculate_classification_metrics_one_class(y_test, y_pred_prob)
return metrics_df
# Evaluate models on the test set
metrics_grid = evaluate_model(model_grid, X_test, y_test)
metrics_rand = evaluate_model(model_rand, X_test, y_test)
metrics_bay = evaluate_model(model_bay, X_test, y_test)
# Print the results
print("Grid Search Metrics:")
print(metrics_grid)
print("\nRandom Search Metrics:")
print(metrics_rand)
print("\nBayesian Search Metrics:")
print(metrics_bay)
# Extract the parameter of interest for comparison
param = 'param_classifier__learning_rate'
grid = model_grid.cv_results_[param]
rand = model_rand.cv_results_[param]
bay = model_bay.cv_results_[param]
# Plot the comparison
fig = plt.figure(figsize=(15, 7))
ax = plt.gca()
ax.scatter(np.arange(len(grid)), grid, c='g', s=20, label='grid');
ax.scatter(np.arange(len(rand)), rand, c='r', s=20, label='random');
ax.scatter(np.arange(len(bay)), bay, c='b', s=20, label='bayesian');
ax.set_yscale('log')
plt.legend()
plt.title(param)
plt.show()
Grid Search Metrics:
Class TP TN FP FN Sensitivity_TPR Specificity AUC_ROC AUC_PRC \
0 Class 1 37 0 1 0 1.0 0.0 0.702703 0.041667
Gini KS MCC Kappa Accuracy Balanced_Accuracy F1 \
0 0.405405 0.702703 0.0 0.0 0.973684 0.5 0.0
F1_Weighted Precision Recall
0 0.960702 0.0 0.0
Random Search Metrics:
Class TP TN FP FN Sensitivity_TPR Specificity AUC_ROC AUC_PRC \
0 Class 1 36 1 1 0 1.0 0.5 0.756757 0.05
Gini KS MCC Kappa Accuracy Balanced_Accuracy F1 \
0 0.513514 0.756757 -0.027027 -0.027027 0.947368 0.486486 0.0
F1_Weighted Precision Recall
0 0.947368 0.0 0.0
Bayesian Search Metrics:
Class TP TN FP FN Sensitivity_TPR Specificity AUC_ROC AUC_PRC \
0 Class 1 36 1 1 0 1.0 0.5 0.756757 0.05
Gini KS MCC Kappa Accuracy Balanced_Accuracy F1 \
0 0.513514 0.756757 -0.027027 -0.027027 0.947368 0.486486 0.0
F1_Weighted Precision Recall
0 0.947368 0.0 0.0
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from scipy.stats import loguniform, randint
from skopt import BayesSearchCV
from sklearn.pipeline import Pipeline
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, auc
from xgboost import XGBClassifier
np.int = int
# Grid Search
param_grid = {
"classifier__learning_rate": [0.0001, 0.0005, 0.001, 0.01, 0.1],
"classifier__n_estimators": [100, 300, 600, 800, 1000],
"classifier__max_depth": [4, 20, 100, 250, 400]
}
reg_grid = GridSearchCV(pipe,
param_grid=param_grid,
cv=5,
n_jobs=8,
scoring='roc_auc'
)
model_grid = reg_grid.fit(X_train, y_train)
# Random Search
n_iter = 70
param_rand = {
"classifier__learning_rate": loguniform(1e-4, 0.1),
"classifier__n_estimators": randint(100, 1000),
"classifier__max_depth": randint(4, 400)
}
reg_rand = RandomizedSearchCV(pipe,
param_distributions=param_rand,
n_iter=n_iter,
cv=5,
n_jobs=8,
scoring='roc_auc',
random_state=123)
model_rand = reg_rand.fit(X_train, y_train)
# Bayesian Search
n_iter = 70
param_bay = {
"classifier__learning_rate": (0.0001, 0.1, "log-uniform"),
"classifier__n_estimators": (100, 1000),
"classifier__max_depth": (4, 400)
}
reg_bay = BayesSearchCV(estimator=pipe,
search_spaces=param_bay,
n_iter=n_iter,
cv=5,
n_jobs=8,
scoring='roc_auc',
random_state=123)
model_bay = reg_bay.fit(X_train, y_train)
# Extract the parameter of interest for comparison
param = 'param_classifier__learning_rate'
grid = model_grid.cv_results_[param]
rand = model_rand.cv_results_[param]
bay = model_bay.cv_results_[param]
# Plot the comparison
fig = plt.figure(figsize=(15, 7))
ax = plt.gca()
ax.scatter(np.arange(len(grid)), grid, c='g', s=20, label='grid');
ax.scatter(np.arange(len(rand)), rand, c='r', s=20, label='random');
ax.scatter(np.arange(len(bay)), bay, c='b', s=20, label='bayesian');
ax.set_yscale('log')
plt.legend()
plt.title(param)
plt.show()
# Using XGBoost. Hyperparameters are tuned using Bayesian Search, grid search, and random search
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score, balanced_accuracy_score, roc_curve, auc
from xgboost import XGBClassifier
import optuna
# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.iloc[:, 1:-1]
target = df['label']
# Sample features and target to .01% of the original size
features = features.sample(frac=0.01, random_state=42)
target = target.sample(frac=0.01, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
# Create a pipeline with a classifier and two scalers (StandardScaler and MinMaxScaler [0, 1])
pipe = Pipeline([
('scaler', StandardScaler()),
('minmax_scaler', MinMaxScaler((0, 1))),
('classifier', XGBClassifier())
])
# Grid Search
param_grid = {
"classifier__learning_rate": [0.0001, 0.0005, 0.001, 0.01, 0.1],
"classifier__n_estimators": [100, 300, 600, 800, 1000],
"classifier__max_depth": [4, 20, 100, 250, 400]
}
reg_grid = GridSearchCV(pipe,
param_grid=param_grid,
cv=5,
n_jobs=8,
scoring='roc_auc'
)
model_grid = reg_grid.fit(X_train, y_train)
# Random Search
n_iter_rand = 70
param_rand = {
"classifier__learning_rate": np.logspace(-4, -1, num=1000),
"classifier__n_estimators": np.random.randint(100, 1000, size=n_iter_rand),
"classifier__max_depth": np.random.randint(4, 400, size=n_iter_rand)
}
reg_rand = RandomizedSearchCV(pipe,
param_distributions=param_rand,
n_iter=n_iter_rand,
cv=5,
n_jobs=8,
scoring='roc_auc',
random_state=123)
model_rand = reg_rand.fit(X_train, y_train)
# Optuna Search
n_iter_optuna = 70
def objective(trial):
params = {
"learning_rate": trial.suggest_loguniform("learning_rate", 1e-4, 0.1),
"n_estimators": trial.suggest_int("n_estimators", 100, 1000),
"max_depth": trial.suggest_int("max_depth", 4, 400)
}
# Set the classifier parameters in the pipeline
pipe.set_params(classifier__learning_rate=params["learning_rate"],
classifier__n_estimators=params["n_estimators"],
classifier__max_depth=params["max_depth"])
# Fit the model and calculate ROC-AUC
model = pipe.fit(X_train, y_train)
y_pred_prob = model.predict_proba(X_test)[:, 1]
roc_auc = roc_auc_score(y_test, y_pred_prob)
return roc_auc
study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=n_iter_optuna)
# Extract results from studies
results_grid = pd.DataFrame(model_grid.cv_results_)
results_grid['iteration'] = range(1, len(results_grid) + 1) # Add iteration column
results_rand = pd.DataFrame(model_rand.cv_results_)
results_rand['iteration'] = range(1, len(results_rand) + 1) # Add iteration column
results_optuna = study.trials_dataframe()
results_optuna['iteration'] = range(1, len(results_optuna) + 1)
# Get the best parameters from each optimization
best_params_grid = model_grid.best_params_
best_params_rand = model_rand.best_params_
best_params_optuna = study.best_params
# Update the pipeline with the best parameters
pipe.set_params(classifier__learning_rate=best_params_optuna["learning_rate"],
classifier__n_estimators=best_params_optuna["n_estimators"],
classifier__max_depth=best_params_optuna["max_depth"])
# Fit the final model
final_model_optuna = pipe.fit(X_train, y_train)
# Evaluation Metrics
def evaluate_model(model, X_test, y_test):
y_pred_prob = model.predict_proba(X_test)[:, 1]
roc_auc = roc_auc_score(y_test, y_pred_prob)
balanced_acc = balanced_accuracy_score(y_test, (y_pred_prob >= 0.5).astype(int))
fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)
ks = np.max(tpr - fpr)
return roc_auc, balanced_acc, ks
# Evaluate models on the test set
roc_auc_grid, balanced_acc_grid, ks_grid = evaluate_model(model_grid, X_test, y_test)
roc_auc_rand, balanced_acc_rand, ks_rand = evaluate_model(model_rand, X_test, y_test)
roc_auc_optuna, balanced_acc_optuna, ks_optuna = evaluate_model(final_model_optuna, X_test, y_test)
# Scatter plot for Balanced Accuracy
fig, axes = plt.subplots(3, 1, figsize=(15, 15))
# Grid Search
sns.scatterplot(data=results_grid, x='iteration', y='mean_test_score', label='Grid Search', color='green', ax=axes[0])
# Random Search
sns.scatterplot(data=results_rand, x='iteration', y='mean_test_score', label='Random Search', color='red', ax=axes[1])
# Optuna Search
sns.scatterplot(data=results_optuna, x='iteration', y='value', label='Optuna Search', color='blue', ax=axes[2])
plt.tight_layout()
plt.legend()
plt.show()
[I 2024-01-07 20:22:01,493] A new study created in memory with name: no-name-b4d09e12-9b4c-4355-84be-95a9e4a63b55
[I 2024-01-07 20:22:02,190] Trial 0 finished with value: 0.6486486486486487 and parameters: {'learning_rate': 0.025965778968787823, 'n_estimators': 654, 'max_depth': 13}. Best is trial 0 with value: 0.6486486486486487.
[I 2024-01-07 20:22:03,449] Trial 1 finished with value: 0.6486486486486487 and parameters: {'learning_rate': 0.000671343015223328, 'n_estimators': 620, 'max_depth': 246}. Best is trial 0 with value: 0.6486486486486487.
[I 2024-01-07 20:22:05,636] Trial 2 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.0003351676721857623, 'n_estimators': 725, 'max_depth': 54}. Best is trial 2 with value: 0.7567567567567568.
[I 2024-01-07 20:22:06,825] Trial 3 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.004892589507419087, 'n_estimators': 536, 'max_depth': 207}. Best is trial 2 with value: 0.7567567567567568.
[I 2024-01-07 20:22:07,836] Trial 4 finished with value: 0.7027027027027026 and parameters: {'learning_rate': 0.0001075416374785266, 'n_estimators': 535, 'max_depth': 289}. Best is trial 2 with value: 0.7567567567567568.
[I 2024-01-07 20:22:08,393] Trial 5 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.014583763534570878, 'n_estimators': 360, 'max_depth': 9}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:08,667] Trial 6 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.001991124210189203, 'n_estimators': 122, 'max_depth': 102}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:09,626] Trial 7 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0003363310632254498, 'n_estimators': 471, 'max_depth': 227}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:10,155] Trial 8 finished with value: 0.7027027027027026 and parameters: {'learning_rate': 0.00011136028481532918, 'n_estimators': 272, 'max_depth': 219}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:10,713] Trial 9 finished with value: 0.6486486486486487 and parameters: {'learning_rate': 0.09725144251556718, 'n_estimators': 823, 'max_depth': 233}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:11,891] Trial 10 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.012822952519384409, 'n_estimators': 999, 'max_depth': 377}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:12,973] Trial 11 finished with value: 0.7027027027027027 and parameters: {'learning_rate': 0.002146678639934428, 'n_estimators': 316, 'max_depth': 21}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:14,014] Trial 12 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.013949002702370919, 'n_estimators': 806, 'max_depth': 102}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:14,840] Trial 13 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0006043500352381994, 'n_estimators': 402, 'max_depth': 98}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:15,429] Trial 14 finished with value: 0.5945945945945945 and parameters: {'learning_rate': 0.06960143328013517, 'n_estimators': 744, 'max_depth': 42}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:15,855] Trial 15 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.005278360731684821, 'n_estimators': 195, 'max_depth': 153}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:16,736] Trial 16 finished with value: 0.5675675675675675 and parameters: {'learning_rate': 0.02910461336786791, 'n_estimators': 963, 'max_depth': 63}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:17,528] Trial 17 finished with value: 0.7027027027027026 and parameters: {'learning_rate': 0.0009852406514023015, 'n_estimators': 377, 'max_depth': 150}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:18,566] Trial 18 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.008310746381248431, 'n_estimators': 698, 'max_depth': 155}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:20,171] Trial 19 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.010628671253220482, 'n_estimators': 880, 'max_depth': 328}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:21,175] Trial 20 finished with value: 0.5945945945945945 and parameters: {'learning_rate': 0.042370721334982, 'n_estimators': 625, 'max_depth': 157}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:22,335] Trial 21 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.01022403598345688, 'n_estimators': 892, 'max_depth': 365}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:23,937] Trial 22 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.007625021892927763, 'n_estimators': 816, 'max_depth': 325}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:24,894] Trial 23 finished with value: 0.6486486486486487 and parameters: {'learning_rate': 0.01943759300165951, 'n_estimators': 893, 'max_depth': 280}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:26,320] Trial 24 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0032695237679415635, 'n_estimators': 686, 'max_depth': 328}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:27,367] Trial 25 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.007036535264437527, 'n_estimators': 459, 'max_depth': 178}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:28,092] Trial 26 finished with value: 0.4864864864864865 and parameters: {'learning_rate': 0.05272574819387728, 'n_estimators': 892, 'max_depth': 397}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:29,157] Trial 27 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0031373974582856764, 'n_estimators': 576, 'max_depth': 276}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:30,502] Trial 28 finished with value: 0.6756756756756757 and parameters: {'learning_rate': 0.019016736749388616, 'n_estimators': 760, 'max_depth': 124}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:31,193] Trial 29 finished with value: 0.6216216216216216 and parameters: {'learning_rate': 0.03049279338828089, 'n_estimators': 696, 'max_depth': 4}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:31,692] Trial 30 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.008838040174117432, 'n_estimators': 259, 'max_depth': 188}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:32,869] Trial 31 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.013432282847501318, 'n_estimators': 891, 'max_depth': 345}. Best is trial 5 with value: 0.7837837837837838.
[I 2024-01-07 20:22:34,376] Trial 32 finished with value: 0.8108108108108107 and parameters: {'learning_rate': 0.009513091127698128, 'n_estimators': 873, 'max_depth': 350}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:35,362] Trial 33 finished with value: 0.6486486486486487 and parameters: {'learning_rate': 0.01965196926100892, 'n_estimators': 948, 'max_depth': 306}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:36,885] Trial 34 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.004832096458219818, 'n_estimators': 783, 'max_depth': 251}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:38,287] Trial 35 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.003131501073417488, 'n_estimators': 651, 'max_depth': 73}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:39,926] Trial 36 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.0056938408858875226, 'n_estimators': 596, 'max_depth': 257}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:41,956] Trial 37 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0013323534187582912, 'n_estimators': 848, 'max_depth': 355}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:42,544] Trial 38 finished with value: 0.6216216216216216 and parameters: {'learning_rate': 0.03675698558198616, 'n_estimators': 491, 'max_depth': 310}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:43,346] Trial 39 finished with value: 0.6756756756756757 and parameters: {'learning_rate': 0.021581861970830285, 'n_estimators': 698, 'max_depth': 391}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:43,600] Trial 40 finished with value: 0.6756756756756757 and parameters: {'learning_rate': 0.010769989058309089, 'n_estimators': 105, 'max_depth': 29}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:44,966] Trial 41 finished with value: 0.8108108108108107 and parameters: {'learning_rate': 0.00984462684255336, 'n_estimators': 863, 'max_depth': 362}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:46,621] Trial 42 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.0042665509632374855, 'n_estimators': 852, 'max_depth': 342}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:48,164] Trial 43 finished with value: 0.6486486486486487 and parameters: {'learning_rate': 0.016096497248778596, 'n_estimators': 950, 'max_depth': 372}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:49,510] Trial 44 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.00795611515849533, 'n_estimators': 990, 'max_depth': 305}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:50,980] Trial 45 finished with value: 0.8108108108108107 and parameters: {'learning_rate': 0.011619927770037624, 'n_estimators': 739, 'max_depth': 206}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:51,783] Trial 46 finished with value: 0.6216216216216216 and parameters: {'learning_rate': 0.02541243937950875, 'n_estimators': 770, 'max_depth': 209}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:52,703] Trial 47 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.014154343606122883, 'n_estimators': 717, 'max_depth': 127}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:53,722] Trial 48 finished with value: 0.7027027027027026 and parameters: {'learning_rate': 0.0020671816761863703, 'n_estimators': 516, 'max_depth': 72}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:55,226] Trial 49 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.00646344480543513, 'n_estimators': 660, 'max_depth': 233}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:56,077] Trial 50 finished with value: 0.7027027027027027 and parameters: {'learning_rate': 0.004268416695500814, 'n_estimators': 154, 'max_depth': 183}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:57,156] Trial 51 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.012280808994455104, 'n_estimators': 851, 'max_depth': 378}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:58,674] Trial 52 finished with value: 0.8108108108108107 and parameters: {'learning_rate': 0.010402415933777507, 'n_estimators': 790, 'max_depth': 329}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:22:59,783] Trial 53 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.009917071374354146, 'n_estimators': 798, 'max_depth': 354}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:01,205] Trial 54 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.01614759389652774, 'n_estimators': 758, 'max_depth': 120}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:02,779] Trial 55 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.0064317297312761635, 'n_estimators': 929, 'max_depth': 257}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:05,135] Trial 56 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0002056946747683765, 'n_estimators': 825, 'max_depth': 84}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:05,862] Trial 57 finished with value: 0.7567567567567568 and parameters: {'learning_rate': 0.008527330691775727, 'n_estimators': 410, 'max_depth': 316}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:06,495] Trial 58 finished with value: 0.4864864864864865 and parameters: {'learning_rate': 0.052042202286553964, 'n_estimators': 729, 'max_depth': 292}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:07,003] Trial 59 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.025647802078437684, 'n_estimators': 355, 'max_depth': 386}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:08,314] Trial 60 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0040889314649698695, 'n_estimators': 627, 'max_depth': 46}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:09,716] Trial 61 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.011357254937295789, 'n_estimators': 921, 'max_depth': 331}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:10,885] Trial 62 finished with value: 0.6756756756756757 and parameters: {'learning_rate': 0.01644331011111973, 'n_estimators': 870, 'max_depth': 361}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:12,338] Trial 63 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.009191924000294898, 'n_estimators': 826, 'max_depth': 290}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:13,757] Trial 64 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.006223056711545702, 'n_estimators': 786, 'max_depth': 339}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:14,518] Trial 65 finished with value: 0.7297297297297297 and parameters: {'learning_rate': 0.0026098869932645917, 'n_estimators': 241, 'max_depth': 164}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:15,526] Trial 66 finished with value: 0.8108108108108107 and parameters: {'learning_rate': 0.011487178978641032, 'n_estimators': 741, 'max_depth': 272}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:16,275] Trial 67 finished with value: 0.5945945945945945 and parameters: {'learning_rate': 0.03170790206833891, 'n_estimators': 731, 'max_depth': 241}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:17,073] Trial 68 finished with value: 0.7027027027027026 and parameters: {'learning_rate': 0.02106495154650321, 'n_estimators': 685, 'max_depth': 218}. Best is trial 32 with value: 0.8108108108108107.
[I 2024-01-07 20:23:17,890] Trial 69 finished with value: 0.7837837837837838 and parameters: {'learning_rate': 0.01304621508071494, 'n_estimators': 565, 'max_depth': 273}. Best is trial 32 with value: 0.8108108108108107.
# Plot the comparison using scatter plots
# Determine fig size
fig = plt.figure(figsize=(15, 7))
# Grid Search
sns.scatterplot(data=results_grid, x='iteration', y='mean_test_score', label='Grid Search', color='green')
# Random Search
sns.scatterplot(data=results_rand, x='iteration', y='mean_test_score', label='Random Search', color='red')
# Optuna Search
sns.scatterplot(data=results_optuna, x='iteration', y='value', label='Optuna Search', color='blue')
# Set y scale between 0.5 and 0.7
plt.ylim(0.5, 0.65)
plt.tight_layout()
plt.legend()
plt.show()
###################### Using XGBoost. Hyperparameters are tuned using Bayesian Search, grid search, and random search
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score, balanced_accuracy_score, roc_curve, auc
from xgboost import XGBClassifier
import optuna
# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.iloc[:, 1:-1]
target = df['label']
# Sample features and target to .01% of the original size
#features = features.sample(frac=1, random_state=42)
#target = target.sample(frac=1, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
# Create a pipeline with a classifier and two scalers (StandardScaler and MinMaxScaler [0, 1])
pipe = Pipeline([
('scaler', StandardScaler()),
('minmax_scaler', MinMaxScaler((0, 1))),
('classifier', XGBClassifier())
])
# Grid Search
param_grid = {
"classifier__learning_rate": [0.0001, 0.0005, 0.001, 0.01, 0.1],
"classifier__n_estimators": [100, 300, 600, 800, 1000],
"classifier__max_depth": [4, 20, 100, 250, 400]
}
reg_grid = GridSearchCV(pipe,
param_grid=param_grid,
cv=5,
n_jobs=8,
scoring='roc_auc'
)
model_grid = reg_grid.fit(X_train, y_train)
# Random Search
n_iter_rand = 70
param_rand = {
"classifier__learning_rate": np.logspace(-4, -1, num=1000),
"classifier__n_estimators": np.random.randint(100, 1000, size=n_iter_rand),
"classifier__max_depth": np.random.randint(4, 400, size=n_iter_rand)
}
reg_rand = RandomizedSearchCV(pipe,
param_distributions=param_rand,
n_iter=n_iter_rand,
cv=5,
n_jobs=8,
scoring='roc_auc',
random_state=123)
model_rand = reg_rand.fit(X_train, y_train)
# Optuna Search
n_iter_optuna = 70
def objective(trial):
params = {
"learning_rate": trial.suggest_loguniform("learning_rate", 1e-4, 0.1),
"n_estimators": trial.suggest_int("n_estimators", 100, 1000),
"max_depth": trial.suggest_int("max_depth", 4, 400)
}
# Set the classifier parameters in the pipeline
pipe.set_params(classifier__learning_rate=params["learning_rate"],
classifier__n_estimators=params["n_estimators"],
classifier__max_depth=params["max_depth"])
# Fit the model and calculate ROC-AUC
model = pipe.fit(X_train, y_train)
y_pred_prob = model.predict_proba(X_test)[:, 1]
roc_auc = roc_auc_score(y_test, y_pred_prob)
return roc_auc
study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=n_iter_optuna)
# Extract results from studies
results_grid = pd.DataFrame(model_grid.cv_results_)
results_grid['iteration'] = range(1, len(results_grid) + 1) # Add iteration column
results_rand = pd.DataFrame(model_rand.cv_results_)
results_rand['iteration'] = range(1, len(results_rand) + 1) # Add iteration column
results_optuna = study.trials_dataframe()
results_optuna['iteration'] = range(1, len(results_optuna) + 1)
# Get the best parameters from each optimization
best_params_grid = model_grid.best_params_
best_params_rand = model_rand.best_params_
best_params_optuna = study.best_params
# Update the pipeline with the best parameters
pipe.set_params(classifier__learning_rate=best_params_optuna["learning_rate"],
classifier__n_estimators=best_params_optuna["n_estimators"],
classifier__max_depth=best_params_optuna["max_depth"])
# Fit the final model
final_model_optuna = pipe.fit(X_train, y_train)
# Evaluation Metrics
def evaluate_model(model, X_test, y_test):
y_pred_prob = model.predict_proba(X_test)[:, 1]
roc_auc = roc_auc_score(y_test, y_pred_prob)
balanced_acc = balanced_accuracy_score(y_test, (y_pred_prob >= 0.5).astype(int))
fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)
ks = np.max(tpr - fpr)
return roc_auc, balanced_acc, ks
# Evaluate models on the test set
roc_auc_grid, balanced_acc_grid, ks_grid = evaluate_model(model_grid, X_test, y_test)
roc_auc_rand, balanced_acc_rand, ks_rand = evaluate_model(model_rand, X_test, y_test)
roc_auc_optuna, balanced_acc_optuna, ks_optuna = evaluate_model(final_model_optuna, X_test, y_test)
# Scatter plot for Balanced Accuracy
fig, axes = plt.subplots(3, 1, figsize=(15, 15))
# Grid Search
sns.scatterplot(data=results_grid, x='iteration', y='mean_test_score', label='Grid Search', color='green', ax=axes[0])
# Random Search
sns.scatterplot(data=results_rand, x='iteration', y='mean_test_score', label='Random Search', color='red', ax=axes[1])
# Optuna Search
sns.scatterplot(data=results_optuna, x='iteration', y='value', label='Optuna Search', color='blue', ax=axes[2])
plt.tight_layout()
plt.legend()
plt.show()
# Check the scoring metric used in Grid Search
print("Grid Search Scoring Metric:", reg_grid.scoring)
# Check the scoring metric used in Random Search
print("Random Search Scoring Metric:", reg_rand.scoring)
# Check the scoring metric used in Bayesian Search
print("Bayesian Search Scoring Metric:", reg_bay.scoring)
Grid Search Scoring Metric: roc_auc Random Search Scoring Metric: roc_auc Bayesian Search Scoring Metric: roc_auc
# For Grid Search
best_params_grid = model_grid.best_params_
print("Grid Search Best Hyperparameters:", best_params_grid)
# For Random Search
best_params_rand = model_rand.best_params_
print("Random Search Best Hyperparameters:", best_params_rand)
# For Optuna Search
best_params_optuna = study.best_params
print("Optuna Search Best Hyperparameters:", best_params_optuna)
Grid Search Best Hyperparameters: {'classifier__learning_rate': 0.0001, 'classifier__max_depth': 4, 'classifier__n_estimators': 100}
Random Search Best Hyperparameters: {'classifier__n_estimators': 868, 'classifier__max_depth': 326, 'classifier__learning_rate': 0.0016451905877536625}
Optuna Search Best Hyperparameters: {'learning_rate': 0.03764400391884294, 'n_estimators': 223, 'max_depth': 131}
study.trials_dataframe().head(20)
| number | value | datetime_start | datetime_complete | duration | params_learning_rate | params_max_depth | params_n_estimators | state | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0.729730 | 2024-01-07 20:02:46.799686 | 2024-01-07 20:02:47.861923 | 0 days 00:00:01.062237 | 0.000315 | 5 | 535 | COMPLETE |
| 1 | 1 | 0.729730 | 2024-01-07 20:02:47.863924 | 2024-01-07 20:02:49.378263 | 0 days 00:00:01.514339 | 0.000198 | 70 | 516 | COMPLETE |
| 2 | 2 | 0.756757 | 2024-01-07 20:02:49.379263 | 2024-01-07 20:02:50.126430 | 0 days 00:00:00.747167 | 0.006110 | 125 | 243 | COMPLETE |
| 3 | 3 | 0.729730 | 2024-01-07 20:02:50.127430 | 2024-01-07 20:02:51.185667 | 0 days 00:00:01.058237 | 0.014404 | 115 | 901 | COMPLETE |
| 4 | 4 | 0.540541 | 2024-01-07 20:02:51.186667 | 2024-01-07 20:02:52.376935 | 0 days 00:00:01.190268 | 0.044723 | 46 | 926 | COMPLETE |
| 5 | 5 | 0.756757 | 2024-01-07 20:02:52.377935 | 2024-01-07 20:02:53.842262 | 0 days 00:00:01.464327 | 0.002101 | 73 | 681 | COMPLETE |
| 6 | 6 | 0.756757 | 2024-01-07 20:02:53.844263 | 2024-01-07 20:02:54.989519 | 0 days 00:00:01.145256 | 0.014741 | 81 | 688 | COMPLETE |
| 7 | 7 | 0.648649 | 2024-01-07 20:02:54.990520 | 2024-01-07 20:02:55.486631 | 0 days 00:00:00.496111 | 0.002174 | 6 | 232 | COMPLETE |
| 8 | 8 | 0.621622 | 2024-01-07 20:02:55.487631 | 2024-01-07 20:02:55.808703 | 0 days 00:00:00.321072 | 0.069402 | 69 | 302 | COMPLETE |
| 9 | 9 | 0.729730 | 2024-01-07 20:02:55.809703 | 2024-01-07 20:02:56.549870 | 0 days 00:00:00.740167 | 0.000399 | 204 | 367 | COMPLETE |
| 10 | 10 | 0.702703 | 2024-01-07 20:02:56.550870 | 2024-01-07 20:02:56.854937 | 0 days 00:00:00.304067 | 0.008422 | 327 | 125 | COMPLETE |
| 11 | 11 | 0.729730 | 2024-01-07 20:02:56.856938 | 2024-01-07 20:02:58.489303 | 0 days 00:00:01.632365 | 0.001687 | 172 | 758 | COMPLETE |
| 12 | 12 | 0.729730 | 2024-01-07 20:02:58.491304 | 2024-01-07 20:03:00.353721 | 0 days 00:00:01.862417 | 0.000953 | 166 | 684 | COMPLETE |
| 13 | 13 | 0.729730 | 2024-01-07 20:03:00.354721 | 2024-01-07 20:03:01.447966 | 0 days 00:00:01.093245 | 0.004099 | 242 | 376 | COMPLETE |
| 14 | 14 | 0.756757 | 2024-01-07 20:03:01.448966 | 2024-01-07 20:03:02.339166 | 0 days 00:00:00.890200 | 0.005234 | 134 | 491 | COMPLETE |
| 15 | 15 | 0.729730 | 2024-01-07 20:03:02.341166 | 2024-01-07 20:03:02.603226 | 0 days 00:00:00.262060 | 0.000837 | 268 | 106 | COMPLETE |
| 16 | 16 | 0.567568 | 2024-01-07 20:03:02.604227 | 2024-01-07 20:03:03.396402 | 0 days 00:00:00.792175 | 0.031024 | 117 | 799 | COMPLETE |
| 17 | 17 | 0.702703 | 2024-01-07 20:03:03.397402 | 2024-01-07 20:03:04.939748 | 0 days 00:00:01.542346 | 0.001111 | 388 | 629 | COMPLETE |
| 18 | 18 | 0.702703 | 2024-01-07 20:03:04.940749 | 2024-01-07 20:03:05.972980 | 0 days 00:00:01.032231 | 0.000120 | 212 | 406 | COMPLETE |
| 19 | 19 | 0.729730 | 2024-01-07 20:03:05.974981 | 2024-01-07 20:03:06.413078 | 0 days 00:00:00.438097 | 0.007565 | 169 | 206 | COMPLETE |
# Get feature importances
importances = model.feature_importances_
# Get feature names
feature_names = X_train.columns
# Sort feature importances in descending order
indices = importances.argsort()[::-1]
# Plot the feature importances
# Plot the top 20 feature importances
top_n = 40
plt.figure(figsize=(21, 7))
plt.bar(range(top_n), importances[indices][:top_n])
plt.xticks(range(top_n), feature_names[indices][:top_n], rotation=45)
plt.xlabel('Feature')
plt.ylabel('Importance')
plt.title('Top 20 XGBoost Feature Importance')
plt.show()
Configuração do Ambiente de Produção Para configurar o ambiente de produção de forma eficiente, adotamos práticas específicas para garantir a estabilidade e o desempenho do modelo. Utilizamos a ferramenta pip freeze para gerar um arquivo requirements.txt, contendo as versões exatas das bibliotecas utilizadas durante o desenvolvimento. Isso previne possíveis erros e conflitos de versões, assegurando a consistência do ambiente. Além disso, optamos por armazenar o modelo em um arquivo do tipo pickle para facilitar a carga e descarga, proporcionando maior agilidade na integração do modelo no ambiente de produção. A utilização de containers, como Docker, é recomendada para garantir a robustez e reprodutibilidade do ambiente em diferentes configurações, facilitando a escalabilidade e a manutenção.
Integração com Sistemas Existentes Na integração do modelo com sistemas existentes, adotamos uma abordagem prática para garantir uma implementação eficaz. Desenvolvemos interfaces customizadas para facilitar a comunicação entre o modelo e outros componentes do sistema, garantindo uma integração suave. Para facilitar a manutenção, utilizamos logs detalhados para monitorar a entrada e saída do modelo, permitindo uma rápida identificação de eventuais problemas. Essa abordagem prática visa minimizar os impactos operacionais e otimizar a contribuição do modelo no contexto mais amplo do ambiente de produção. Optamos por integrar o aplicativo Streamlit com o pipeline de produção. Essa integração visa assegurar que os dados do usuário sejam processados de maneira eficiente e que as previsões sejam geradas em tempo real. O uso do Streamlit proporciona uma interface amigável e interativa para os usuários, tornando a experiência mais acessível e facilitando o acesso às funcionalidades do modelo. Essa abordagem de integração permite uma transição suave do modelo do ambiente de desenvolvimento para um ambiente de produção, garantindo que o modelo treinado seja facilmente acessível e capaz de fornecer previsões em tempo real conforme necessário.
Configuração do Ambiente de Produção
Para configurar o ambiente de produção de forma eficiente, adotamos práticas específicas para garantir a estabilidade e o desempenho do modelo. Utilizamos a ferramenta pip freeze para gerar um arquivo requirements.txt, contendo as versões exatas das bibliotecas utilizadas durante o desenvolvimento. Isso previne possíveis erros e conflitos de versões, assegurando a consistência do ambiente. Além disso, optamos por armazenar o modelo em um arquivo do tipo pickle para facilitar a carga e descarga, proporcionando maior agilidade na integração do modelo no ambiente de produção. A utilização de containers, como Docker, é recomendada para garantir a robustez e reprodutibilidade do ambiente em diferentes configurações, facilitando a escalabilidade e a manutenção.
Integração com Sistemas Existentes:
Na integração do modelo com sistemas existentes, adotamos uma abordagem prática para garantir uma implementação eficaz. Desenvolvemos interfaces customizadas para facilitar a comunicação entre o modelo e outros componentes do sistema, garantindo uma integração suave.
Para facilitar a manutenção, utilizamos logs detalhados para monitorar a entrada e saída do modelo, permitindo uma rápida identificação de eventuais problemas. Essa abordagem prática visa minimizar os impactos operacionais e otimizar a contribuição do modelo no contexto mais amplo do ambiente de produção.
Optamos por integrar o aplicativo Streamlit com o pipeline de produção. Essa integração visa assegurar que os dados do usuário sejam processados de maneira eficiente e que as previsões sejam geradas em tempo real. O uso do Streamlit proporciona uma interface amigável e interativa para os usuários, tornando a experiência mais acessível e facilitando o acesso às funcionalidades do modelo.
Essa abordagem de integração permite uma transição suave do modelo do ambiente de desenvolvimento para um ambiente de produção, garantindo que o modelo treinado seja facilmente acessível e capaz de fornecer previsões em tempo real conforme necessário.
Finalmente, é relevante ressaltar que o processo de modelagem é intrinsecamente complexo e não segue uma sequência rígida de etapas. Em vez disso, envolve a formulação e teste contínuo de hipóteses, ajustes e refinamentos iterativos. Esse ciclo de melhoria contínua é uma parte essencial do acompanhamento do modelo ao longo de sua vida útil, visando garantir não apenas a confiabilidade, mas também a otimização contínua dos resultados enquanto o modelo está em produção. A flexibilidade para se adaptar a novos dados, condições de mercado e requisitos de negócios é fundamental para a eficácia a longo prazo do modelo de risco de crédito.
pwd
'/content'
!jupyter contrib nbextension install --user
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "C:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Scripts\jupyter-contrib.EXE\__main__.py", line 7, in <module>
File "C:\Users\GabrielScatena\AppData\Roaming\Python\Python312\site-packages\jupyter_core\application.py", line 280, in launch_instance
super().launch_instance(argv=argv, **kwargs)
File "C:\Users\GabrielScatena\AppData\Roaming\Python\Python312\site-packages\traitlets\config\application.py", line 1051, in launch_instance
app = cls.instance(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\GabrielScatena\AppData\Roaming\Python\Python312\site-packages\traitlets\config\configurable.py", line 583, in instance
inst = cls(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^
File "C:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\jupyter_contrib_core\application.py", line 27, in __init__
self._refresh_subcommands()
File "C:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\jupyter_contrib_core\application.py", line 43, in _refresh_subcommands
get_subcommands_dict = entrypoint.load()
^^^^^^^^^^^^^^^^^
File "C:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\pkg_resources\__init__.py", line 2516, in load
return self.resolve()
^^^^^^^^^^^^^^
File "C:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\pkg_resources\__init__.py", line 2522, in resolve
module = __import__(self.module_name, fromlist=['__name__'], level=0)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\jupyter_contrib_nbextensions\__init__.py", line 5, in <module>
import jupyter_nbextensions_configurator
File "C:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\jupyter_nbextensions_configurator\__init__.py", line 18, in <module>
from notebook.base.handlers import APIHandler, IPythonHandler
ModuleNotFoundError: No module named 'notebook.base'
df = pd.read_csv(r'C:\Temp\Codes\df_droped_001.csv')
import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from imblearn.over_sampling import RandomOverSampler, SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.combine import SMOTEENN, SMOTETomek
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc, make_scorer
from sklearn.impute import SimpleImputer
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistGradientBoostingClassifier
#from catboost import CatBoostClassifier
import os
import json
# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.columns[1:479]
target = df['label']
# Define the hyperparameters for each model
hyperparameters = {
'Logistic Regression': {'C': [0.1, 1, 10]},
'k-Nearest Neighbors (k-NN)': {'n_neighbors': [3, 5, 7]},
'Support Vector Machines (SVM)': {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf']},
'Neural Networks': {'hidden_layer_sizes': [(50,), (100,), (50, 50)]},
'Naive Bayes': {},
'Decision Trees': {'max_depth': [None, 10, 20]},
'Random Forest': {'n_estimators': [50, 100, 200]},
'Gradient Boosting Machines (GBM)': {'n_estimators': [50, 100, 200]},
'XGBoost': {'class_weight=': [None, 'balanced'], 'n_estimators': [50, 100, 200]},
'LightGBM': {'n_estimators': [50, 100, 200]},
'CatBoost': {'iterations': [50, 100, 200], 'learning_rate': [0.01, 0.1, 0.2]},
'HistGradientBoostingClassifier': {'class_weight=': ['balanced', [None]], 'max_iter': [50, 100, 200], 'learning_rate': [0.01, 0.1, 0.2]}
}
# Define the imbalanced data handling techniques
sampling_techniques = {
'None': None,
'Oversampling': RandomOverSampler(),
'Undersampling': RandomUnderSampler(),
#'Hybrid Sampling (SMOTEENN)': SMOTEENN(), #Does not accept NaN values
#'Hybrid Sampling (SMOTETomek)': SMOTETomek()
}
# Define the imputation techniques
imputation_techniques = {
#'XGBoost': None,
'None': None,
'Zero Imputation': SimpleImputer(strategy='constant', fill_value=0),
#'Mean Imputation': SimpleImputer(strategy='mean'),
#'Median Imputation': SimpleImputer(strategy='median')
}
# Define a function to calculate KS and Gini
# def calculate_ks_gini(y_true, y_prob):
# fpr, tpr, thresholds = roc_curve(y_true, y_prob)
# ks = np.max(tpr - fpr)
# gini = 2 * auc(fpr, tpr) - 1
# return ks, gini
# Create a list to store the results
results_2 = []
# Define k-fold cross-validation
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)
# Iterate through each model
for model_name, model in [
# ('Logistic Regression', LogisticRegression()),
('k-Nearest Neighbors (k-NN)', KNeighborsClassifier()),
# ('Support Vector Machines (SVM)', SVC(probability=True)),
# ('Neural Networks', MLPClassifier()),
# ('Naive Bayes', GaussianNB()),
# ('Decision Trees', DecisionTreeClassifier()),
# ('Random Forest', RandomForestClassifier()),
# ('Gradient Boosting Machines (GBM)', GradientBoostingClassifier()),
('XGBoost', XGBClassifier()),
# ('LightGBM', LGBMClassifier()),
# ('CatBoost', CatBoostClassifier()),
# ('HistGradientBoostingClassifier', HistGradientBoostingClassifier())
]:
# Iterate through each imbalanced data handling technique
for sampling_name, sampling_technique in sampling_techniques.items():
# Iterate through each imputation technique
for imputation_name, imputation_technique in imputation_techniques.items():
# Create a new dataframe to store the results for this model, technique combination
df_result_2 = pd.DataFrame(columns=['Algorithm', 'Metrics', 'Hyperparameters', 'Imputation Technique', 'Imbalance Class Technique', 'Model Unique Code'])
# Perform k-fold cross-validation
for fold, (train_index, test_index) in enumerate(k_fold.split(df[features], target)):
X_train, X_test = df[features].iloc[train_index], df[features].iloc[test_index]
y_train, y_test = target.iloc[train_index], target.iloc[test_index]
# Apply imputation technique on the train and test sets, if imputation_name is not None
if imputation_name is not None and imputation_technique is not None:
print(imputation_name)
X_train_imputed = imputation_technique.fit_transform(X_train)
X_test_imputed = imputation_technique.transform(X_test)
else:
print(imputation_name)
if model_name in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
X_train_imputed, X_test_imputed = X_train, X_test
else:
break
# Apply the imbalanced data handling technique on the train set
if sampling_technique is not None:
X_train_resampled, y_train_resampled = sampling_technique.fit_resample(X_train_imputed, y_train)
else:
X_train_resampled, y_train_resampled = X_train_imputed, y_train
# Train the model on the resampled train set
model.fit(X_train_resampled, y_train_resampled)
# Make predictions on the test set
y_pred = model.predict(X_test_imputed)
y_prob = model.predict_proba(X_test_imputed)[:, 1]
# Calculate the evaluation metrics
metrics_df = calculate_classification_metrics_one_class(y_test, y_prob)
# Set 'Imputation Technique' based on the specified conditions
if imputation_name in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
imputation_name = 'None'
else:
imputation_name = imputation_name
#imputation_name = None if imputation_name not in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier'] else imputation_name
# Create a new DataFrame for each iteration
df_result_2 = pd.DataFrame({
'Algorithm': model_name,
'Hyperparameters': [model.get_params()],
'Imputation Technique': imputation_name,
'Imbalance Class Technique': sampling_name,
'Model Unique Code': f"{model_name}_{sampling_name}_{imputation_name}_fold_{fold}",
**metrics_df # Unpack the metrics_df columns
})
# Append the result to the list
results_2.append(df_result_2)
# Combine all the results DataFrames into one final DataFrame
df_results_final_2 = pd.concat(results_2, ignore_index=True)
# Save each model in a separate file with a name corresponding to the model unique code
for model_unique_code in df_results_final_2['Model Unique Code'].unique():
df_model = df_results_final_2[df_results_final_2['Model Unique Code'] == model_unique_code]
df_model.to_csv(f"{model_unique_code}_2.csv", index=False)
# Display the final DataFrame
display(df_results_final_2)
import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from imblearn.over_sampling import RandomOverSampler, SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.combine import SMOTEENN, SMOTETomek
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc, make_scorer
from sklearn.impute import SimpleImputer
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistGradientBoostingClassifier
#from catboost import CatBoostClassifier
import os
import json
# Define the dataframe 'df', features, and target
# 'label' is the target column
features = df.columns[1:479]
target = df['label']
# Define the hyperparameters for each model
hyperparameters = {
'XGBoost': { 'learning_rate': 0.03764400391884294, 'n_estimators': 223, 'max_depth': 131},
}
# Define the imbalanced data handling techniques
sampling_techniques = {
# 'None': None,
# 'Oversampling': RandomOverSampler(),
'Undersampling': RandomUnderSampler(),
# #'Hybrid Sampling (SMOTEENN)': SMOTEENN(), #Does not accept NaN values
# #'Hybrid Sampling (SMOTETomek)': SMOTETomek()
}
# Define the imputation techniques
imputation_techniques = {
#'XGBoost': None,
'None': None,
# 'Zero Imputation': SimpleImputer(strategy='constant', fill_value=0),
#'Mean Imputation': SimpleImputer(strategy='mean'),
#'Median Imputation': SimpleImputer(strategy='median')
}
# Define a function to calculate KS and Gini
# def calculate_ks_gini(y_true, y_prob):
# fpr, tpr, thresholds = roc_curve(y_true, y_prob)
# ks = np.max(tpr - fpr)
# gini = 2 * auc(fpr, tpr) - 1
# return ks, gini
# Create a list to store the results
results_2 = []
# Define k-fold cross-validation
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)
# Iterate through each model
for model_name, model in [
('XGBoost', XGBClassifier()),
]:
# Iterate through each imbalanced data handling technique
for sampling_name, sampling_technique in sampling_techniques.items():
# Iterate through each imputation technique
for imputation_name, imputation_technique in imputation_techniques.items():
# Create a new dataframe to store the results for this model, technique combination
df_result_2 = pd.DataFrame(columns=['Algorithm', 'Metrics', 'Hyperparameters', 'Imputation Technique', 'Imbalance Class Technique', 'Model Unique Code'])
# Perform k-fold cross-validation
for fold, (train_index, test_index) in enumerate(k_fold.split(df[features], target)):
X_train, X_test = df[features].iloc[train_index], df[features].iloc[test_index]
y_train, y_test = target.iloc[train_index], target.iloc[test_index]
# Apply imputation technique on the train and test sets, if imputation_name is not None
if imputation_name is not None and imputation_technique is not None:
print(imputation_name)
X_train_imputed = imputation_technique.fit_transform(X_train)
X_test_imputed = imputation_technique.transform(X_test)
else:
print(imputation_name)
if model_name in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
X_train_imputed, X_test_imputed = X_train, X_test
else:
break
# Apply the imbalanced data handling technique on the train set
if sampling_technique is not None:
X_train_resampled, y_train_resampled = sampling_technique.fit_resample(X_train_imputed, y_train)
else:
X_train_resampled, y_train_resampled = X_train_imputed, y_train
# Train the model on the resampled train set
model.fit(X_train_resampled, y_train_resampled)
# Make predictions on the test set
y_pred = model.predict(X_test_imputed)
y_prob = model.predict_proba(X_test_imputed)[:, 1]
# Calculate the evaluation metrics
metrics_df = calculate_classification_metrics_one_class(y_test, y_prob)
# Set 'Imputation Technique' based on the specified conditions
if imputation_name in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier']:
imputation_name = 'None'
else:
imputation_name = imputation_name
#imputation_name = None if imputation_name not in ['Decision Trees', 'Random Forest', 'Gradient Boosting Machines (GBM)', 'XGBoost', 'LightGBM', 'HistGradientBoostingClassifier'] else imputation_name
# Create a new DataFrame for each iteration
df_result_2 = pd.DataFrame({
'Algorithm': model_name,
'Hyperparameters': [model.get_params()],
'Imputation Technique': imputation_name,
'Imbalance Class Technique': sampling_name,
'Model Unique Code': f"{model_name}_{sampling_name}_{imputation_name}_fold_{fold}",
**metrics_df # Unpack the metrics_df columns
})
# Append the result to the list
results_2.append(df_result_2)
# Combine all the results DataFrames into one final DataFrame
df_results_final_2 = pd.concat(results_2, ignore_index=True)
# Save each model in a separate file with a name corresponding to the model unique code
for model_unique_code in df_results_final_2['Model Unique Code'].unique():
df_model = df_results_final_2[df_results_final_2['Model Unique Code'] == model_unique_code]
df_model.to_csv(f"{model_unique_code}_2f.csv", index=False)
# Display the final DataFrame
display(df_results_final_2)
None None None None None
| Algorithm | Hyperparameters | Imputation Technique | Imbalance Class Technique | Model Unique Code | Class | TP | TN | FP | FN | ... | Gini | KS | MCC | Kappa | Accuracy | Balanced_Accuracy | F1 | F1_Weighted | Precision | Recall | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | XGBoost | {'objective': 'binary:logistic', 'base_score':... | None | Undersampling | XGBoost_Undersampling_None_fold_0 | Class 1 | 2304 | 1256 | 84 | 153 | ... | 0.362777 | 0.303769 | 0.146602 | 0.088502 | 0.647090 | 0.646380 | 0.185905 | 0.737962 | 0.108588 | 0.645570 |
| 1 | XGBoost | {'objective': 'binary:logistic', 'base_score':... | None | Undersampling | XGBoost_Undersampling_None_fold_1 | Class 1 | 2348 | 1285 | 62 | 102 | ... | 0.350935 | 0.296427 | 0.113252 | 0.058822 | 0.645246 | 0.634125 | 0.131528 | 0.749214 | 0.073540 | 0.621951 |
| 2 | XGBoost | {'objective': 'binary:logistic', 'base_score':... | None | Undersampling | XGBoost_Undersampling_None_fold_2 | Class 1 | 2334 | 1276 | 72 | 115 | ... | 0.328299 | 0.278624 | 0.117450 | 0.064531 | 0.644983 | 0.630755 | 0.145754 | 0.744895 | 0.082674 | 0.614973 |
| 3 | XGBoost | {'objective': 'binary:logistic', 'base_score':... | None | Undersampling | XGBoost_Undersampling_None_fold_3 | Class 1 | 2270 | 1330 | 82 | 114 | ... | 0.274684 | 0.220975 | 0.096716 | 0.052910 | 0.628030 | 0.606094 | 0.139024 | 0.730563 | 0.078947 | 0.581633 |
| 4 | XGBoost | {'objective': 'binary:logistic', 'base_score':... | None | Undersampling | XGBoost_Undersampling_None_fold_4 | Class 1 | 2316 | 1264 | 74 | 142 | ... | 0.394415 | 0.309958 | 0.145992 | 0.084810 | 0.647524 | 0.652167 | 0.175092 | 0.741693 | 0.100996 | 0.657407 |
5 rows × 24 columns
df_results_final_2['AUC_ROC']
0 0.681388 1 0.675467 2 0.664150 3 0.637342 4 0.697207 Name: AUC_ROC, dtype: float64
import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier
from imblearn.under_sampling import RandomUnderSampler
from sklearn.metrics import roc_curve, auc
# Define the dataframe 'df', features, and target
features = df.columns[1:479]
target = df['label']
# Define the hyperparameters for each model
hyperparameters = {
'XGBoost': {'learning_rate': 0.03764400391884294, 'n_estimators': 223, 'max_depth': 131},
}
# Define the imbalanced data handling techniques
sampling_techniques = {
'Undersampling': RandomUnderSampler(),
}
# Define the imputation techniques
imputation_techniques = {
'None': None,
}
# Create a list to store the results
results_2 = []
# Define k-fold cross-validation
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)
# Iterate through each model
for model_name, model in [('XGBoost', XGBClassifier(**hyperparameters['XGBoost']))]:
# Iterate through each imbalanced data handling technique
for sampling_name, sampling_technique in sampling_techniques.items():
# Iterate through each imputation technique
for imputation_name, imputation_technique in imputation_techniques.items():
# Create a new dataframe to store the results for this model, technique combination
df_result_2 = pd.DataFrame(columns=['Algorithm', 'Metrics', 'Hyperparameters', 'Imputation Technique', 'Imbalance Class Technique', 'Model Unique Code'])
# Perform k-fold cross-validation
for fold, (train_index, test_index) in enumerate(k_fold.split(df[features], target)):
X_train, X_test = df[features].iloc[train_index], df[features].iloc[test_index]
y_train, y_test = target.iloc[train_index], target.iloc[test_index]
# Apply Standard Scaler followed by MinMaxScaler[0,1]
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
minmax_scaler = MinMaxScaler(feature_range=(0, 1))
X_train_scaled_minmax = minmax_scaler.fit_transform(X_train_scaled)
X_test_scaled_minmax = minmax_scaler.transform(X_test_scaled)
# Apply the imbalanced data handling technique on the train set
if sampling_technique is not None:
X_train_resampled, y_train_resampled = sampling_technique.fit_resample(X_train_scaled_minmax, y_train)
else:
X_train_resampled, y_train_resampled = X_train_scaled_minmax, y_train
# Train the model on the resampled train set
model.fit(X_train_resampled, y_train_resampled)
# Make predictions on the test set
y_prob = model.predict_proba(X_test_scaled_minmax)[:, 1]
# Calculate the evaluation metrics
metrics_df = calculate_classification_metrics_one_class(y_test, y_prob)
# Create a new DataFrame for each iteration
df_result_2 = pd.DataFrame({
'Algorithm': model_name,
'Hyperparameters': [model.get_params()],
'Imputation Technique': imputation_name,
'Imbalance Class Technique': sampling_name,
'Model Unique Code': f"{model_name}_{sampling_name}_{imputation_name}_fold_{fold}",
**metrics_df # Unpack the metrics_df columns
})
# Append the result to the list
results_2.append(df_result_2)
# Combine all the results DataFrames into one final DataFrame
df_results_final_2 = pd.concat(results_2, ignore_index=True)
# Save each model in a separate file with a name corresponding to the model unique code
for model_unique_code in df_results_final_2['Model Unique Code'].unique():
df_model = df_results_final_2[df_results_final_2['Model Unique Code'] == model_unique_code]
df_model.to_csv(f"{model_unique_code}_2ff.csv", index=False)
# Display the final DataFrame
display(df_results_final_2)
| Algorithm | Hyperparameters | Imputation Technique | Imbalance Class Technique | Model Unique Code | Class | TP | TN | FP | FN | ... | Gini | KS | MCC | Kappa | Accuracy | Balanced_Accuracy | F1 | F1_Weighted | Precision | Recall | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | XGBoost | {'objective': 'binary:logistic', 'base_score':... | None | Undersampling | XGBoost_Undersampling_None_fold_0 | Class 1 | 2316 | 1244 | 96 | 141 | ... | 0.363806 | 0.277477 | 0.123377 | 0.075290 | 0.647090 | 0.622749 | 0.173859 | 0.738059 | 0.101805 | 0.594937 |
| 1 | XGBoost | {'objective': 'binary:logistic', 'base_score':... | None | Undersampling | XGBoost_Undersampling_None_fold_1 | Class 1 | 2406 | 1227 | 67 | 97 | ... | 0.354367 | 0.264055 | 0.108234 | 0.057969 | 0.659205 | 0.626863 | 0.130376 | 0.759670 | 0.073263 | 0.591463 |
| 2 | XGBoost | {'objective': 'binary:logistic', 'base_score':... | None | Undersampling | XGBoost_Undersampling_None_fold_2 | Class 1 | 2374 | 1236 | 74 | 113 | ... | 0.360102 | 0.290816 | 0.118411 | 0.066370 | 0.654991 | 0.630948 | 0.147135 | 0.752404 | 0.083766 | 0.604278 |
| 3 | XGBoost | {'objective': 'binary:logistic', 'base_score':... | None | Undersampling | XGBoost_Undersampling_None_fold_3 | Class 1 | 2375 | 1225 | 86 | 110 | ... | 0.313333 | 0.241961 | 0.102393 | 0.058960 | 0.654636 | 0.610473 | 0.143697 | 0.750654 | 0.082397 | 0.561224 |
| 4 | XGBoost | {'objective': 'binary:logistic', 'base_score':... | None | Undersampling | XGBoost_Undersampling_None_fold_4 | Class 1 | 2406 | 1174 | 85 | 131 | ... | 0.382259 | 0.301273 | 0.135855 | 0.082686 | 0.668335 | 0.639274 | 0.172255 | 0.757321 | 0.100383 | 0.606481 |
5 rows × 24 columns
df_results_final_2['AUC_ROC']
0 0.681903 1 0.677183 2 0.680051 3 0.656667 4 0.691129 Name: AUC_ROC, dtype: float64
import xgboost as xgb
import matplotlib.pyplot as plt
# Plot feature importance
xgb.plot_importance(model, max_num_features=20) # Adjust max_num_features as needed
plt.show()
%pip install Boruta
Requirement already satisfied: Boruta in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (0.3) Requirement already satisfied: numpy>=1.10.4 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from Boruta) (1.26.2) Requirement already satisfied: scikit-learn>=0.17.1 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from Boruta) (1.3.2) Requirement already satisfied: scipy>=0.17.0 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from Boruta) (1.11.4) Requirement already satisfied: joblib>=1.1.1 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from scikit-learn>=0.17.1->Boruta) (1.3.2) Requirement already satisfied: threadpoolctl>=2.0.0 in c:\users\gabrielscatena\appdata\local\programs\python\python312\lib\site-packages (from scikit-learn>=0.17.1->Boruta) (3.2.0) Note: you may need to restart the kernel to use updated packages.
from boruta import BorutaPy
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
np.int = int
np.float = float
np.bool = bool
X = df.iloc[:, 1:-1]
y = df['label']
X = X.fillna(0)
# Define algo
rf = XGBClassifier()
# Define Boruta feature selection method
feat_selector = BorutaPy(rf, n_estimators='auto',verbose=2, random_state=1)
X_numpy = X.values
y = y.ravel()
# Find all relevant features
feat_selector.fit(X_numpy, y)
# Check selected features
selected_features = feat_selector.support_
# Check ranking of features
feature_ranking = feat_selector.ranking_
# Call transform() on X to filter it down to selected features
X_filtered = feat_selector.transform(X_numpy)
# Print selected features
print("Selected Features:")
for i, selected in enumerate(selected_features):
if selected:
print(f'Feature {i + 1}')
# Print feature ranking
print("\nFeature Ranking:")
for i, rank in enumerate(feature_ranking):
print(f'Feature {i + 1}: Rank {rank}')
selected_features_indices = np.where(feat_selector.support_)[0]
# Print selected features
print("\n >Selected Features:")
for feature_idx in selected_features_indices:
print(f'Feature {feature_idx + 1}')
feature_names = X.columns.tolist()
print("\n >Selected Features:")
for feature_idx in selected_features_indices:
print(f'{feature_names[feature_idx]}')
# Define the model (e.g., RandomForestRegressor)
model = XGBClassifier(random_state=0)
# Train the model using selected features
model.fit(X_filtered, y)
# Predict the target variable using the selected features
y_pred = model.predict(X_filtered)
# Calculate MSE
mse = mean_squared_error(y, y_pred)
# Display MSE
print(f'Mean Squared Error (MSE): {mse}')
selected_feature_names = [feature_names[idx] for idx in selected_features_indices]
print(selected_feature_names)
Iteration: 1 / 100 Confirmed: 0 Tentative: 479 Rejected: 0 Iteration: 2 / 100 Confirmed: 0 Tentative: 479 Rejected: 0 Iteration: 3 / 100 Confirmed: 0 Tentative: 479 Rejected: 0 Iteration: 4 / 100 Confirmed: 0 Tentative: 479 Rejected: 0 Iteration: 5 / 100 Confirmed: 0 Tentative: 479 Rejected: 0 Iteration: 6 / 100 Confirmed: 0 Tentative: 479 Rejected: 0 Iteration: 7 / 100 Confirmed: 0 Tentative: 479 Rejected: 0 Iteration: 8 / 100 Confirmed: 0 Tentative: 33 Rejected: 446 Iteration: 9 / 100 Confirmed: 0 Tentative: 33 Rejected: 446 Iteration: 10 / 100 Confirmed: 0 Tentative: 33 Rejected: 446 Iteration: 11 / 100 Confirmed: 0 Tentative: 33 Rejected: 446 Iteration: 12 / 100 Confirmed: 0 Tentative: 16 Rejected: 463 Iteration: 13 / 100 Confirmed: 0 Tentative: 16 Rejected: 463 Iteration: 14 / 100 Confirmed: 0 Tentative: 16 Rejected: 463 Iteration: 15 / 100 Confirmed: 0 Tentative: 16 Rejected: 463 Iteration: 16 / 100 Confirmed: 0 Tentative: 14 Rejected: 465 Iteration: 17 / 100 Confirmed: 0 Tentative: 14 Rejected: 465 Iteration: 18 / 100 Confirmed: 0 Tentative: 14 Rejected: 465 Iteration: 19 / 100 Confirmed: 3 Tentative: 9 Rejected: 467 Iteration: 20 / 100 Confirmed: 3 Tentative: 9 Rejected: 467 Iteration: 21 / 100 Confirmed: 3 Tentative: 9 Rejected: 467 Iteration: 22 / 100 Confirmed: 3 Tentative: 9 Rejected: 467 Iteration: 23 / 100 Confirmed: 3 Tentative: 9 Rejected: 467 Iteration: 24 / 100 Confirmed: 3 Tentative: 9 Rejected: 467 Iteration: 25 / 100 Confirmed: 3 Tentative: 9 Rejected: 467 Iteration: 26 / 100 Confirmed: 3 Tentative: 9 Rejected: 467 Iteration: 27 / 100 Confirmed: 3 Tentative: 9 Rejected: 467 Iteration: 28 / 100 Confirmed: 3 Tentative: 9 Rejected: 467 Iteration: 29 / 100 Confirmed: 3 Tentative: 9 Rejected: 467 Iteration: 30 / 100 Confirmed: 3 Tentative: 9 Rejected: 467 Iteration: 31 / 100 Confirmed: 3 Tentative: 9 Rejected: 467 Iteration: 32 / 100 Confirmed: 3 Tentative: 8 Rejected: 468 Iteration: 33 / 100 Confirmed: 3 Tentative: 8 Rejected: 468 Iteration: 34 / 100 Confirmed: 3 Tentative: 8 Rejected: 468 Iteration: 35 / 100 Confirmed: 3 Tentative: 8 Rejected: 468 Iteration: 36 / 100 Confirmed: 3 Tentative: 8 Rejected: 468 Iteration: 37 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 38 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 39 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 40 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 41 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 42 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 43 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 44 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 45 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 46 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 47 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 48 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 49 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 50 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 51 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 52 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 53 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 54 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 55 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 56 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 57 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 58 / 100 Confirmed: 5 Tentative: 6 Rejected: 468 Iteration: 59 / 100 Confirmed: 6 Tentative: 5 Rejected: 468 Iteration: 60 / 100 Confirmed: 6 Tentative: 5 Rejected: 468 Iteration: 61 / 100 Confirmed: 6 Tentative: 5 Rejected: 468 Iteration: 62 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 63 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 64 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 65 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 66 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 67 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 68 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 69 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 70 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 71 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 72 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 73 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 74 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 75 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 76 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 77 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 78 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 79 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 80 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 81 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 82 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 83 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 84 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 85 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 86 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 87 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 88 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 89 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 90 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 91 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 92 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 93 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 94 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 95 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 96 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 97 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 98 / 100 Confirmed: 6 Tentative: 4 Rejected: 469 Iteration: 99 / 100 Confirmed: 6 Tentative: 4 Rejected: 469
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[32], line 22 20 y = y.ravel() 21 # Find all relevant features ---> 22 feat_selector.fit(X_numpy, y) 24 # Check selected features 25 selected_features = feat_selector.support_ File c:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\boruta\boruta_py.py:201, in BorutaPy.fit(self, X, y) 188 def fit(self, X, y): 189 """ 190 Fits the Boruta feature selection with the provided estimator. 191 (...) 198 The target values. 199 """ --> 201 return self._fit(X, y) File c:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\boruta\boruta_py.py:319, in BorutaPy._fit(self, X, y) 317 # basic result variables 318 self.n_features_ = confirmed.shape[0] --> 319 self.support_ = np.zeros(n_feat, dtype=np.bool) 320 self.support_[confirmed] = 1 321 self.support_weak_ = np.zeros(n_feat, dtype=np.bool) File c:\Users\GabrielScatena\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\__init__.py:338, in __getattr__(attr) 333 warnings.warn( 334 f"In the future `np.{attr}` will be defined as the " 335 "corresponding NumPy scalar.", FutureWarning, stacklevel=2) 337 if attr in __former_attrs__: --> 338 raise AttributeError(__former_attrs__[attr]) 340 if attr == 'testing': 341 import numpy.testing as testing AttributeError: module 'numpy' has no attribute 'bool'. `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here. The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations